applications of spatio-temporal analytical …using spatio-temporal analytic methods. computerised...
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APPLICATIONS OF SPATIO-TEMPORAL
ANALYTICAL METHODS IN SURVEILLANCE OF
ROSS RIVER VIRUS DISEASE
BY
WENBIAO HU BMed
A thesis submitted for the Degree of Doctor of Philosophy in the Centre
for Health Research, Queensland University of Technology
MAY 2005
II
For my wife, Xiaodong and our son Junqian
III
KEYWORDS
Classification and regression trees, cluster analysis, generalised linear model,
geographic information system, interpolation, polynomial distributed lag model,
principal components analysis, Ross River virus disease, seasonal auto-regressive
integrated moving average, socio-ecological factors, time series analysis
IV
SUMMARY
The incidence of many arboviral diseases is largely associated with social and
environmental conditions. Ross River virus (RRV) is the most prevalent arboviral
disease in Australia. It has long been recognised that the transmission pattern of RRV
is sensitive to socio-ecological factors including climate variation, population
movement, mosquito-density and vegetation types. This study aimed to assess the
relationships between socio-environmental variability and the transmission of RRV
using spatio-temporal analytic methods.
Computerised data files of daily RRV disease cases and daily climatic variables in
Brisbane, Queensland during 1985-2001 were obtained from the Queensland
Department of Health and the Australian Bureau of Meteorology, respectively.
Available information on other socio-ecological factors was also collected from
relevant government agencies as follows: 1) socio-demographic data from the
Australia Bureau of Statistics; 2) information on vegetation (littoral wetlands,
ephemeral wetlands, open freshwater, riparian vegetation, melaleuca open forests, wet
eucalypt, open forests and other bushland) from Brisbane City Council; 3) tidal
activities from the Queensland Department of Transport; and 4) mosquito-density
from Brisbane City Council.
Principal components analysis (PCA) was used as an exploratory technique for
discovering spatial and temporal pattern of RRV distribution. The PCA results show
that the first principal component accounted for approximately 57% of the
information, which contained the four seasonal rates and loaded highest and positively
for autumn. K-means cluster analysis indicates that the seasonality of RRV is
V
characterised by three groups with high, medium and low incidence of disease, and it
suggests that there are at least three different disease ecologies. The variation in
spatio-temporal patterns of RRV indicates a complex ecology that is unlikely to be
explained by a single dominant transmission route across these three groupings.
Therefore, there is need to explore socio-economic and environmental determinants of
RRV disease at the statistical local area (SLA) level.
Spatial distribution analysis and multiple negative binomial regression models were
employed to identify the socio-economic and environmental determinants of RRV
disease at both the city and local (ie, SLA) levels. The results show that RRV activity
was primarily concentrated in the northeast, northwest and southeast areas in
Brisbane. The negative binomial regression models reveal that RRV incidence for the
whole of the Brisbane area was significantly associated with Southern Oscillation
Index (SOI) at a lag of 3 months (Relative Risk (RR): 1.12; 95% confidence interval
(CI): 1.06 - 1.17), the proportion of people with lower levels of education (RR: 1.02;
95% CI: 1.01 - 1.03), the proportion of labour workers (RR: 0.97; 95% CI: 0.95 –
1.00) and vegetation density (RR: 1.02; 95% CI: 1.00 – 1.04). However, RRV
incidence for high risk areas (ie, SLAs with higher incidence of RRV) was
significantly associated with mosquito density (RR: 1.01; 95% CI: 1.00 - 1.01), SOI at
a lag of 3 months (RR: 1.48; 95% CI: 1.23 - 1.78), human population density (RR:
3.77; 95% CI: 1.35 - 10.51), the proportion of indigenous population (RR: 0.56; 95%
CI: 0.37 - 0.87) and the proportion of overseas visitors (RR: 0.57; 95% CI: 0.35 –
0.92). It is acknowledged that some of these risk factors, while statistically significant,
are small in magnitude. However, given the high incidence of RRV, they may still be
important in practice. The results of this study suggest that the spatial pattern of RRV
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disease in Brisbane is determined by a combination of ecological, socio-economic and
environmental factors.
The possibility of developing an epidemic forecasting system for RRV disease was
explored using the multivariate Seasonal Auto-regressive Integrated Moving Average
(SARIMA) technique. The results of this study suggest that climatic variability,
particularly precipitation, may have played a significant role in the transmission of
RRV disease in Brisbane. This finding cannot entirely be explained by confounding
factors such as other socio-ecological conditions because they have been unlikely to
change dramatically on a monthly time scale in this city over the past two decades.
SARIMA models show that monthly precipitation at a lag 2 months (β=0.004,
p=0.031) was statistically significantly associated with RRV disease. It suggests that
that there may be 50 more cases a year for an increase of 100 mm precipitation on
average in Brisbane. The predictive values in the model were generally consistent
with actual values (root-mean-square error (RMSE): 1.96). Therefore, this model may
have applications as a decision support tool in disease control and risk-management
planning programs in Brisbane.
The Polynomial distributed lag (PDL) time series regression models were performed
to examine the associations between rainfall, mosquito density and the occurrence of
RRV after adjusting for season and auto-correlation. The PDL model was used
because rainfall and mosquito density can affect not merely RRV occurring in the
same month, but in several subsequent months. The rationale for the use of the PDL
technique is that it increases the precision of the estimates. We developed an epidemic
forecasting model to predict incidence of RRV disease. The results show that 95%
and 85% of the variation in the RRV disease was accounted for by the mosquito
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density and rainfall, respectively. The predictive values in the model were generally
consistent with actual values (RMSE: 1.25). The model diagnosis reveals that the
residuals were randomly distributed with no significant auto-correlation. The results
of this study suggest that PDL models may be better than SARIMA models (R-square
increased and RMSE decreased). The findings of this study may facilitate the
development of early warning systems for the control and prevention of this wide-
spread disease.
Further analyses were conducted using classification trees to identify major mosquito
species of Ross River virus (RRV) transmission and explore the threshold of mosquito
density for RRV disease in Brisbane, Australia. The results show that Ochlerotatus
vigilax (RR: 1.028; 95% CI: 1.001 – 1.057) and Culex annulirostris (RR: 1.013, 95%
CI: 1.003 – 1.023) were significantly associated with RRV disease cycles at a lag of 1
month. The presence of RRV was associated with average monthly mosquito density
of 72 Ochlerotatus vigilax and 52 Culex annulirostris per light trap. These results may
also have applications as a decision support tool in disease control and risk-
management planning programs.
As RRV has significant impact on population health, industry, and tourism, it is
important to develop an epidemic forecast system for this disease. The results of this
study show the disease surveillance data can be integrated with social, biological and
environmental databases. These data can provide additional input into the
development of epidemic forecasting models. These attempts may have significant
implications in environmental health decision-making and practices, and may help
health authorities determine public health priorities more wisely and use resources
more effectively and efficiently.
1
TABLE OF CONTENTS
KEYWORDS...............................................................................................................III
SUMMARY.................................................................................................................IV
TABLE OF CONTENTS...............................................................................................1
LIST OF TABLES.........................................................................................................5
LIST OF FIGURES .......................................................................................................7
DEFINITION OF TERMS ..........................................................................................10
ABBREVIATIONS .....................................................................................................12
STATEMENT OF ORIGIANL AUTHORSHIP .........................................................13
ACKNOWLEDGEMENTS.........................................................................................14
PUBLICATIONS BY THE CANDIDATE (2001 - 2004) ..........................................16
CHAPTER 1: INTRODUCTION AND BACKGROUND.....................................20
1.1 INTRODUCTION .................................................................................................20
1.2 AIMS AND HYPOTHESES .................................................................................24
1.3 SIGNIFICANCE OF THE THESIS ......................................................................25
1.4 CONTENTS AND STRUCTURE OF THE THESIS ...........................................26
CHAPTER 2: APPLICATIONS OF GIS AND SPATIAL ANALYSIS IN
MOSQUITO-BORNE DISEASE RESEARCH: A REVIEW OF RELATED
LITERATURE ...........................................................................................................29
2.1 SYSTEMATIC REVIEW......................................................................................29
2.2 CRITICAL APPRAISAL OF KEY SPATIO-TEMPORAL ANALYTIC
METHODS ..................................................................................................................40
2
2.3 APPLICATIONS OF GIS AND SPATIO-TEMPORAL ANALYTIC METHODS
IN RRV RESEARCH ..................................................................................................51
2.4 KNOWLEDGE GAPS IN THIS AREA................................................................56
CHAPTER 3: STUDY DESIGN AND METHOD..................................................58
3.1 STUDY SITE AND STUDY POPULATION.......................................................58
3.2 STUDY DESIGN...................................................................................................61
3.3 DATA COLLECTION AND MANAGEMENT...................................................61
3.4 DATA LINKAGES ...............................................................................................63
3.5 DATA ANALYSIS................................................................................................63
3.6 THE LIMITATIONS OF THE STUDY................................................................69
CHAPTER 4: SPATIAL AND TEMPORAL PATTERNS OF ROSS RIV ER
VIRUS IN BRISBANE, AUSTRALIA.....................................................................72
ABSTRACT.................................................................................................................73
4.1 INTRODUCTION .................................................................................................74
4.2 MATERIAL AND METHODS.............................................................................76
4.3 RESULTS ..............................................................................................................78
4.4 DISCUSSION........................................................................................................84
REFERENCES ............................................................................................................89
CHAPTER 5: SPATIAL ANALYSIS OF SOCIAL AND ENVIRONMEN TAL
FACTORS ASSOCIATED WITH ROSS RIVER VIRUS IN BRISBANE ,
AUSTRALIA..............................................................................................................93
ABSTRACT.................................................................................................................94
5.1 INTRODUCTION .................................................................................................95
3
5.2 MATERIALS AND METHODS...........................................................................96
5.3 RESULTS ..............................................................................................................99
5.4 DISCUSSION......................................................................................................107
REFERENCES ..........................................................................................................114
CHAPTER 6: DEVELOPMENT OF A PREDICTIVE MODEL FOR RO SS
RIVER VIRUS DISEASE IN BRISBANE, AUSTRALIA...................................119
ABSTRACT...............................................................................................................120
6.1 INTRODUCTION ...............................................................................................121
6.2 MATERIALS AND METHODS.........................................................................123
6.3 RESULTS ............................................................................................................128
6.4 DISCUSSION......................................................................................................141
ACKNOWLEDGEMENTS.......................................................................................146
REFERENCES ..........................................................................................................147
CHAPTER 7: RAINFALL, MOSQUITO DENSITY AND THE
TRANSMISSION OF ROSS RIVER VIRUS: AN EPIDEMIC FOREC ASTING
MODEL ....................................................................................................................153
ABSTRACT...............................................................................................................154
7.1 INTRODUCTION ...............................................................................................155
7.2 METHODS ..........................................................................................................157
7.3 RESULTS ............................................................................................................159
7.4 DISCUSSION......................................................................................................167
ACKNOWLEDGEMENTS.......................................................................................170
APPENDIX................................................................................................................171
4
REFERENCES ..........................................................................................................172
CHAPTER 8: MOSQUITO SPECIES AND THE TRANSMISSION OF ROSS
RIVER VIRUS IN BRISBANE, AUSTRALIA.....................................................176
ABSTRACT...............................................................................................................177
8.1 INTRODUCTION ...............................................................................................178
8.2 MATERIALS AND METHODS.........................................................................179
8.3 RESULTS ............................................................................................................181
8.4. DISCUSSION.....................................................................................................188
ACKNOWLEDGEMENTS.......................................................................................190
REFERENCES ..........................................................................................................191
CHAPTER 9: GENERAL DISCUSSION .............................................................194
9.1 INTRODUCTION ...............................................................................................194
9.2 SUBSTANTIVE DISCUSSION..........................................................................194
9.3 THE IMPLICATIONS OF THE STUDY ...........................................................201
9.4 THE STRENGTHS AND LIMITATIONS OF THE STUDY............................203
9.5 RECOMMENDATIONS.....................................................................................205
APPENDIX................................................................................................................211
DATA COLLECTION ..............................................................................................211
REFERENCES ..........................................................................................................225
5
LIST OF TABLES TABLE 2. 1 THE CODING CATEGORIES FOR THE LITERATURE REVIEW .....................32
TABLE 2. 2 ARTICLE NUMBERS BY JOURNAL BASED ON GENERAL HEALTH D OMAIN
(FIRST 50 JOURNALS) ..........................................................................................34
TABLE 2. 3 ARTICLE NUMBERS BY JOURNAL BASED ON MBD...................................35
TABLE 2. 4 CHARACTERISTICS OF 58 MBD PAPERS..................................................38
TABLE 2. 5 STATISTICAL TECHNIQUES AND COMPUTER SOFTWARE FOR SPAT IAL
ANALYSIS *............................................................................................................43
TABLE 4. 1 PRINCIPAL COMPONENT EIGENVALUES AND LOADING FOR EACH SEASON
VARIABLES ...........................................................................................................83
TABLE 4. 2 STATISTICAL CRITERIA FOR THE NUMBERS OF CLUSTERS ......................83
TABLE 4. 3 ANALYSIS OF VARIANCE IN CLUSTER ANALYSIS ......................................84
TABLE 5. 1 SPEARMAN CORRELATION COEFFICIENTS BETWEEN MONTHLY
INCIDENCE OF RRV AND SOCIAL AND ENVIRONMENTAL VARIABLES IN
BRISBANE*.........................................................................................................102
TABLE 5. 2 ADJUSTED RELATIVE RISKS FOR THE TRANSMISSION OF RRV IN
BRISBANE, AUSTRALIA *....................................................................................107
TABLE 6. 1 CHARACTERISTICS OF EXPLANATORY VARIABLES ...............................129
TABLE 6. 2 REGRESSION COEFFICIENTS OF SARIMA ON THE MONTHLY INCIDENCE
OF RRV DISEASE IN BRISBANE, AUSTRALIA , 1985 – 2000 ...............................137
TABLE 7. 1 SPEARMAN CORRELATION COEFFICIENTS BETWEEN MONTHLY
INCIDENCE OF RRV AND RAINFALL AND MOSQUITO DENSITY .........................160
TABLE 7. 2 PDL REGRESSION COEFFICIENTS OF RAINFALL AND MOSQUITO D ENSITY
ON THE MONTHLY INCIDENCE OF RRV DISEASE IN BRISBANE, AUSTRALIA * .163
TABLE 7. 3 LAG DISTRIBUTION COEFFICIENTS IN PDL REGRESSION MODEL .........165
6
TABLE 8. 1 CROSS CORRELATION COEFFICIENTS BETWEEN MOSQUITO DENSI TY AND
INCIDENCE OF RRV...........................................................................................184
TABLE 8. 2 TIME SERIES POISSON REGRESSION MODELS USED TO ADJUST FOR THE
AUTOCORRELATION OF MONTHLY INCIDENCE RATES OF RRV AND
SEASONALITY *...................................................................................................185
7
LIST OF FIGURES FIGURE 1. 1 FLOWCHART OF 5 MANUSCRIPTS IN THESIS ...........................................28
FIGURE 2. 1 THE RESULTS OF SEARCH BASED ON GIS AND SPATIAL ANALYSIS IN
MEDLINE ..............................................................................................................31
FIGURE 2. 2 TRENDS OF PUBLICATIONS ON GIS FOR GENERAL HEALTH DOMAINS ..33
FIGURE 2. 3 TRENDS AND DISTRIBUTION OF EMPIRICAL ARTICLES ON GIS AND
SPATIAL ANALYSIS FOR MBD .............................................................................36
FIGURE 3. 1 LOCATION OF THE STUDY AREA - BRISBANE ..........................................60
FIGURE 4. 1 THE ANNUAL INCIDENCE OF RRV INFECTIONS AND RAINFALL IN
BRISBANE, 1985 - 2001 ........................................................................................79
FIGURE 4. 2 HISTOGRAM OF SEASONAL INCIDENCE OF RRV IN BRISBANE, 1985 –
2001 (X AXIS: SEASONAL INCIDENCE OF RRV, Y AXIS: FREQUENCY (I .E.,
NUMBERS FOR SLAS)) .........................................................................................79
FIGURE 4. 3 SEASONAL INCIDENCE OF RRV DISEASE FOR SLA ACROSS BRISBANE
(FIGURE 4.3-A: SPRING; FIGURE 4.3-B: SUMMER ; FIGURE 4.3-C: AUTUMN ;
FIGURE 4.3-D: WINTER ) .....................................................................................82
FIGURE 4. 4 K-MEANS CLUSTERING ANALYSIS OF INCIDENCE RATE OF RRV IN
BRISBANE, AUSTRALIA ........................................................................................84
FIGURE 5. 1 LOCATION OF BRISBANE, AUSTRALIA ....................................................97
FIGURE 5. 2 THE DISTRIBUTION OF RRV INFECTIONS IN 2001, BRISBANE (CROSS
REFERS TO MOSQUITO MONITOR STATIONS WHICH LOCATED I N HIGH RISK
AREAS BASED ON DISEASE MONITORING RECORDS) .........................................100
FIGURE 5. 3 SPATIAL DISTRIBUTION MODEL USING INVERSE DISTANCE WEI GHTED
INTERPOLATION .................................................................................................104
8
FIGURE 6. 1 LOCATION OF BRISBANE, QUEENSLAND, AUSTRALIA (INCLUDING
LATITUDE AND LONGITUDE OF THE CITY ) ........................................................124
FIGURE 6. 2 THE RELATIONSHIPS BETWEEN MONTHLY INCIDENCE OF ROSS RIVER
VIRUS AND CLIMATE VARIABLES IN BRISBANE BETWEEN 1985 AND 2001 (USING
SEASONALLY DIFFERENCED VARIABLES )..........................................................134
FIGURE 6. 3 CROSS-CORRELATION FUNCTION BETWEEN ROSS RIVER VIRUS AND
CLIMATE VARIABLES AFTER SEASONAL DIFFERENCING . .................................136
FIGURE 6. 4 A: AUTO-CORRELATION (ACF); B: PARTIAL AUTO -CORRELATION OF
RESIDUALS (PACF); AND C: SCATTERPLOT OF RESIDUALS . ...........................138
FIGURE 6. 5 THE VALIDATED SARIMA MODEL OF CLIMATE VARIATION IN
BRISBANE (VALIDATION PERIOD : 1.2001 – 12. 2001 IE., TO THE RIGHT OF THE
VERTICAL DOTTED LINE )...................................................................................140
FIGURE 7. 1 MOSQUITO DENSITY , RAINFALL AND ROSS RIVER VIRUS DISEASE IN
BRISBANE ...........................................................................................................160
FIGURE 7. 2 CROSS-CORRELATION FUNCTIONS BETWEEN ROSS RIVER VIRUS AND
RAINFALL /MOSQUITO DENSITY AFTER SEASONAL DIFFERENCING ...................161
FIGURE 7. 3 VALIDATED POLYNOMIAL LAG DISTRIBUTION MODEL OF MOSQU ITO
DENSITY IN BRISBANE, AUSTRALIA (VALIDATION PERIOD = JAN - DEC/2001, IE.,
TO THE RIGHT OF THE VERTICAL DOTTED LINE ) ..............................................166
FIGURE 7. 4 AUTO-CORRELATION , PARTIAL AUTO -CORRELATION OF RESIDUALS .167
FIGURE 8. 1 LOCATION OF BRISBANE, AUSTRALIA ..................................................179
FIGURE 8. 2 10 MOSQUITO MONITOR STATIONS , BRISBANE, AUSTRALIA ................182
FIGURE 8. 3 THE DISTRIBUTION OF MOSQUITO SPECIES BY SEASON IN BRISBANE,
AUSTRALIA ........................................................................................................183
FIGURE 8. 4 PROPORTION OF MOSQUITO SPECIES IN BRISBANE, AUSTRALIA .........183
9
FIGURE 8. 5 CLASSIFICATION TREE FOR THE RELATIONSHIP BETWEEN
OCHLEROTATUS VIGILAX DENSITY AND RRV*..................................................187
FIGURE 8. 6 CLASSIFICATION TREE FOR THE RELATIONSHIP BETWEEN CULEX
ANNULIROSTRIS DENSITY AND RRV* ................................................................187
FIGURE 9. 1 FRAMEWORK OF RESEARCH RESULTS IN THIS THESIS .........................196
FIGURE 9. 2 FRAMEWORK OF RESEARCH RECOMMENDATIONS IN THIS THESIS ......210
10
DEFINITION OF TERMS
Classification and Regression Trees - builds classification and regression trees for
predicting continuous dependent variables (regression) and categorical predictor
variables (classification).
Cluster Analysis – is one of data reduction methods that is used to group together
entities with similar properties.
Eigenvalues - measure the amount of the variation explained by each principal
component (PC) and will be largest for the first PC and smaller for the subsequent
PCs. An eigenvalue greater than 1 indicates that PCs account for more variance than
accounted by one of the original variables.
El Niño/Southern Oscillation - is a systematic pattern of global climate variability
(Nicholls 1993). It affects most countries in the Pacific and Indian Oceans, bringing
long droughts and extended wet periods every two to seven years.
Generalised Linear Model - a model for linear and non-linear effects of continuous
and categorical predictor variables on a discrete or continuous but not necessarily
normally distributed dependent (outcome) variable.
Geographical Information System - can be seen as a system of hardware, software
and procedures (tools) designed to capture, manage, manipulate, analysis, modelling,
and display spatial or geo-referenced data for solving complex planning and
management problems.
Multicolinearity - in a multiple regression with more than one X variable, two or
more X variables are colinear if they show strong linear relationships. This makes
estimation of regression coefficients impossible. It can also produce unexpectedly
large estimated standard errors for the coefficients of the X variables involved.
11
Overdispersion - is the situation that occurs most frequently in Poisson and binomial
regression when variance is much higher than the mean (whereas it should be the
same).
Poisson Regression - Analysis of the relationship between an observed count with a
Poisson distribution (i.e., outcome variable) and a set of explanatory variables.
Polynomial - a sum of multiples of integer powers of a variable. The highest power in
the expression is the degree of the polynomial.
Principal Components Analysis - is a useful method of data interpretation which
assists in identifying and understanding data structure.
Relative Risk – the ratio of the cumulative incidence rate in the exposed to the
cumulative incidence rate in the unexposed..
Residuals - reflect the overall badness-of-fit of the model. They are the differences
between the observed values of the outcome variable and the corresponding fitted
values predicted by the regression line (the vertical distance between the observed
values and the fitted line).
Southern Oscillation Index - defined as the normalized difference in atmospheric
pressure between Darwin (Australia) and Tahiti (French Polynesia). The SOI accounts
for up to 40% of variation in temperature and rainfall in the Pacific.
Statistical Local Areas - is a general purpose spatial unit. It is the base spatial unit
used to collect and disseminate statistics other than those collected from the
Population Censuses.
12
ABBREVIATIONS
ABS Australian Bureau of Statistics
CARTs Classification and Regression Trees
CI Confidence Interval
EIP Extrinsic Incubation Period
ENSO EI Nino-Southern Oscillation
GIS Geographic Information System
GLM Generalised Linear Model
GPS Global Position System
MBD Mosquito-Borne Disease
NNDSS National Notifiable Diseases Surveillance System
PCA Principal Components Analysis
PDL Polynomial Distribution Lag
RMSE Root-Mean-Square Error
RR Relative Risk
RRV Ross River Virus
RS Remote Sensing
SARIMA Seasonal AutoRegression Integrated Moving Average
SIRs Standardised Incidence Rates
SLA Statistical Local Areas
SOI Southern Oscillation Index
VBDs Vector-Borne Diseases
13
STATEMENT OF ORIGIANL AUTHORSHIP
The work contained in this thesis has not been previously submitted for a degree or
diploma at any other higher education institution. To the best of my knowledge and
belief, the thesis contains no material previously published or written by another
person except where due reference is made.
Signed: ______________________
Date: ________________________
14
ACKNOWLEDGEMENTS
I reserve my greatest thanks and appreciation to my supervisory team, A/Prof. Shilu
Tong, Prof. Kerrie Mengersen and Prof. Brian Oldenburg, for their critical and
thoughtful comments, and guidance, support, encouragement and advice through the
course of my PhD study. At all times throughout my candidature they have
maintained diligence, interest and enthusiasm for my research. I would like to thank
A/Prof. Shilu Tong, my principal supervisor, for his significant amount of time spent
on the professional guidance of my study and his generous financial support to assist
me to complete my thesis. He has not only been an excellent mentor but also a
constant source of inspiration and motivation. It is difficult to imagine how I would
have completed this thesis without his guidance. I would like to thank Prof. Kerrie
Mengersen for her statistical advice and helpful comments on my project. I would like
to thank Prof. Brian Oldenburg in his capacity as an experienced researcher in looking
over my project, and for his personal and professional guidance. I would also like to
heartfelt thank Prof. Beth Newman and Dr. John Aaskov for their invaluable advice
on my thesis. It has been an honour for me to establish a strong personal and
professional relationship with both of them.
I am indebted to all the organisations involved in this project. All of whom are
acknowledged below:
The Queensland Department of Health for providing the health outcome data in
Queensland
15
The Australian Bureau of Meteorology for providing the meteorological data.
The Queensland Department of Transport for providing the high tide data.
Brisbane City Council for providing the vegetation and mosquito density data.
Australian Bureau of Statistics for providing the socio-demographic data.
I would also like to acknowledge all my colleagues in the Centre for Health Research
for their advice and assistance with research and personal friendship.
Finally, I would like to specially thank my wife, Xiaodong, and my son Junqian, for
their love, patience, encouragement and emotional support through this endeavour and
for their suggestions and comments on my research.
16
PUBLICATIONS BY THE CANDIDATE (2001 - 2004)
JOURNAL ARTICLES
In thesis
1. Hu W , Nicholls N, Lindsay M, Dale P, McMichael AJ, Machenzie J and Tong S.
Development of a predictive model for Ross River virus disease in Brisbane,
Australia. American Journal of Tropical Medicine and Hygiene. 2004;71:129-137.
2. Hu W , Mengersen K, Oldenburg B and Tong S. Spatial analysis of social and
environmental factors associated with Ross River virus in Brisbane, Australia.
Acta Tropica. Under review.
3. Hu W , Tong S, Mengersen K, Oldenburg B and Dale P. Spatial and temporal
patterns of Ross River virus in Brisbane, Australia. Arbovirus Research in
Australia. Under review.
4. Hu W , Tong S, Mengersen K, Oldenburg B and Dale P. Mosquito species and the
transmission of Ross River virus in Brisbane, Australia. To be submitted.
5. Hu W , Tong S, Mengersen K and Oldenburg B. Rainfall, mosquito density and
the transmission of Ross River virus: a time series forecasting model. Ecological
Modelling. Under review.
6. Hu W , Zhang J, Oldenburg B and Tong S. Applications of GIS and spatial
analysis in mosquito-borne disease research: a review of related literature.
International Journal of Health and Geographics. Under review.
Not included in thesis
17
7. Hu W , McMichael AJ and Tong S. El Nino/Southern Oscillation and the
Transmission of Hepatitis A Virus in Australia. Medical Journal of Australia.
2004;180:488-489.
8. Hu W , Tong S and Oldenburg B. Applications of spatio-temporal analytical
methods in surveillance and control of communicable disease. Australasian
Epidemiologist.2004;11:6-12.
9. Tong S, Hu W and McMichael AJ. Climate variability and Ross River virus
transmission in Townsville region, Australia, 1985-1996. Tropical Medicine and
International Health. 2004;9:298-304.
10. Tong S and Hu W . Different responses of Ross River Virus to climate variability
between coastline and inland cities in Queensland, Australia. Occupational and
Environmental Medicine. 2002;59:739-744.
11. Tong S and Hu W . Climate variables and incidence of Ross River virus in Cairns,
Australia: a time series analysis. Environmental Health Perspectives
2001;109:1271-1273
12. Hu W , Tong S, Oldenburg B and Feng X. Serum vitamin A concentration and
growth in children and adolescents in Gansu province, China. Asia Pacific
Journal of Clinical Nutrition. 2001;10:63-66.
13. Tong S and Hu W . Effects of climate variation on the transmission of Ross River
virus in Queensland, Australia. Environmental Health. 2001;1:45-51.
Published abstracts
1. Hu W , Mengersen K, Oldenburg and Tong S. Spatial analysis of social and
environmental factors associated with Ross River virus in Brisbane, Australia.
18
Epidemiology 2004;15:S98.
2. Hu W , Tong S. Ross River virus transmission and El Nino Southern-Southern
Oscillation in Australia. Epidemiology 2003;14: S17.
3. Hu W, Tong S. Exploratory spatial analysis of Ross River virus in Brisbane,
Australia, 1987-2001. Australasian epidemiologist 2003;53.
4. Hu W, Tong S. Preliminary development of an epidemic forecasting model of
Ross River virus disease in relation to environmental variation. Australasian
epidemiologist 2003; 22.
5. Hu W , Zhang J, Tong S, et al. Application of geographic information systems
(GIS) and spatial analysis in epidemiological research. Epidemiology 2003;14:S16.
6. Hu W and Tong S. Exploratory spatial analysis of Ross River virus in Brisbane,
Australia, 1987-2001. Australasian epidemiologist 2003;10:53.
7. Tong S, Hu W. Different responses of Ross River virus to climate variability
between coastline and inland cities in Queensland, Australia.
Epidemiology 2002;13:30.
8. Hu W, Mengersen K, Tong S. Spline regression and auto-regression models with
application to time-series data. Epidemiology 2002;13:757.
9. Tong S, Hu W. Climate variability and Ross River virus transmission in
Townsville, Australia: A SARIMA model. American Journal of Epidemiology
2002;1551:145.
10. Tong S, Hu W. Effects of climate variation on the transmission of Ross River
virus in Australia. American Journal of Epidemiology 2002;155:146.
11. Hu W , Tong S. Climate variation and incidence of Ross River virus in Cairns,
Australia: A time series analysis. Epidemiology 2001;12:137.
19
Book Chapter
Tong S, Bi P and Hu W . Environmental Epidemiology In: Guo X et al, eds.
Environmental Medicine. Beijing, China: Beijing Medical University, 2002:15-30.
20
CHAPTER 1: INTRODUCTION AND BACKGROUND
1.1 INTRODUCTION
1.1.1 The burden of Ross River virus disease in Aus tralia
There are many vector-borne diseases (VBDs) in Australia, including Ross River
virus (RRV) disease, Barmah forest virus, Australia encephalitis, dengue fever,
Kunjin virus, etc. RRV disease is the most prevalent vector-borne disease in Australia
and some Pacific island countries (Aaskov et al. 1981a, Rosen et al. 1981,
Scrimgeous et al. 1987, Mackenzie et al. 1994). RRV causes a non-fatal, but
potentially debilitating, disease of humans known as epidemic polyarthritis or RRV
disease (ICD-9: 663). The disease syndrome is characterized by headache, fever, rash,
lethargy and muscle and joint pain. The arthritic symptoms and lethargy may persist
for many months and can be severe (Condon and Rouse 1995). Since 1991, several
thousand cases of RRV disease throughout Australia have been reported each year to
the National Notifiable Disease Surveillance System (NNDSS), and the majority of
these cases are usually from Queensland (eg, approximately 82% of cases from
Queensland in 2002) (Australian Department of Health and Aged Care 2004). The
single largest reported outbreak occurred in the South Pacific islands in 1979-80,
during which more than 50,000 people were affected (Aaskov et al. 1981a). RRV
activity appears to have increased in Australia in the past decade (Harley et al. 2001,
Australian Department of Health and Aged Care 2004), but the reasons for this remain
largely unknown (Harley et al. 2001). It is estimated that the direct economic cost of
RRV is approximately $2,500 per case (Hawkes et al. 1985, Harley et al. 2001), and
the economic impact of this disease is on the order of tens of millions of dollars
21
annually in direct and indirect health costs nationally (Hawkes et al. 1985, Boughton
1994, Russell 1998b).
1.1.2 Transmission of RRV Ross River virus circulates enzootically in reservoir populations of marsupials in
Australia. Infection is asymptomatic in host animals, but while they are viremic, host
animals can infect mosquitoes that feed upon them. After a variable period of time
(the extrinsic incubation period), virus particles replicate to the point where the
mosquito’s saliva is infective to the mosquito’s next non-immune vertebrate host. If a
human is bitten instead, clinical disease may result. At least 20% of infected
individuals develop an acute disease (Weinstein 1997, Harley et al. 2001, Russell
2002).
For the transmission of RRV, the virus and its reservoir, the vector, the human
population, and environmental conditions are key factors. The virus is dependent on
the continuing presence of non-immune hosts in the reservoir population. The
distribution and abundance of the reservoir population will thus affect the availability
of viremic individuals to mosquitoes and a non-immune reservoir population leads to
increased virus activity. A number of vector-related factors also influence the level of
RRV activity. The mosquitoes are efficient vectors of the disease because of their
susceptibility to the virus and the readiness with which they bite reservoir as well as
human hosts. The greater the abundance of mosquitoes, the greater the probability of
being bitten (Weinstein 1997). The human population is susceptible to RRV infection
if individuals are non-immune and are exposed to the virus at the
reservoir/mosquito/human interface. Such exposure is enhanced by human intrusions
into native ecosystems by the expansion of agriculture, forestry, tourism, or similar
22
activities (Weinstein 1997, Harley et al. 2001). Weather conditions directly affect the
breeding, survival, and abundance of mosquitoes and their extrinsic incubation period.
In seasons with high temperatures and rainfall, the vegetation upon which kangaroos
depend will flourish, and more non-immune reservoir hosts will be added to the
temporally and spatially expanding population (Weinstein 1997, Harley et al. 2001,
Russell 2002).
1.1.3 Spatio-temporal modeling
In disease control programmes, there are several factors involved in the estimation of
disease burden, monitoring of disease trend, identification of risk factors, planning
and allocation of resources, etc; and a common thread involved in all these activities
is 'Geography'. Geographic Information Systems (GIS) and spatio-temporal modelling
potentially have great implications in public health research, and have already
emerged as innovative and important tools for disease surveillance and assessments
(Cressie 1991, Clarke et al. 1996, Khan 1999, Brabyn and Skelly 2002, Hearnden et
al. 2003). GIS are particularly well suited for the study of associations between
location, environment and disease due to their spatial analysis and modelling
capabilities (Gesler 1986, Khan 1999). GIS are defined as ‘automated systems for the
capture, storage, retrieval, analysis, and display of spatial data’ (Clarke et al. 1996).
Spatial modelling takes explicit and formal account of observations with a common
spatial nature and leads to better statistical robustness and inferences (Cressie 1991).
In environmental epidemiological research, data are often correlated in space and time,
and this correlation structure can be evaluated in its own right and also used to
increase the accuracy of modelling and prediction efforts. Recently, GIS and spatio-
temporal modelling have been used in studies of risk factors of VBDs (Hightower et
23
al. 1998, Tong et al. 2001, Tong and Hu 2001, Tong et al. 2002, Tong and Hu 2002),
water-borne disease (Clarke et al. 1991, Hearnden et al. 2003), sexually transmitted
disease (Becker et al. 1998), environmental health (Reeves et al. 1994, Vine et al.
1997, Ebi et al. 2004), injury control and prevention (Braddock et al. 1994) and the
analysis of disease control policy and planning (Gordon and Womersley 1997).
The transmission patterns of some VBDs are sensitive to ecological conditions
(Longley and Batty 1996, Kitron and Kazmierczak 1997, Weinstein 1997, Morrison et
al. 1998). For example, mosquitoes can transmit many diseases (eg, malaria, dengue
and RRV). These mosquito-borne diseases usually have strong spatial and temporal
patterns, because mosquito density and longevity depend on a number of
environmental and ecological factors (eg, temperature, precipitation and mosquito-
breeding habitats). It is generally agreed that GIS and spatio-temporal modelling are
important tools to utilize. These variables can be used in GIS and spatio-temporal
modelling to predict the onset and severity of disease epidemics (Gill 1923,
Hightower et al. 1998, Moore and Carpenter 1999). These techniques have been
increasingly employed in VBD surveillance and risk management.
GIS and spatio-temporal modelling methods offer new and expanding opportunities
for VBD research because they can display and model the spatial relationship between
cause and disease (Cressie 1991, Clarke et al. 1996, Khan 1999). The applications of
GIS technology superimpose the temporal and spatial distributions of the ecological
determinants of endemicity of RRV (eg, landscape ecology, climate, reservoir and
vector populations, and human presence and activity). Spatio-temporal modelling can
help us understand the distribution of RRV in space and time. Improved surveillance
24
systems for RRV activity, such as the question of timing for control strategies can
lead to an integrated management model for public health intervention based on a
sound ecological understanding of the disease. Endemic areas of RRV would expand
in both time (length of season) and space (geographic area) under socio-
environmental conditions (eg., optimal climatic, inadequate urban planning, increased
tourists from non-endemic to endemic areas, ecosystem change etc) (Weinstein 1997).
Visualisation demonstrates change or variation over space and time, and can illustrate
where the transmission of diseases occurs. However, caution is needed when
interpreting the spatial pattern of RRV disease using GIS because the localities where
cases occur sometimes differ from those where transmission occurs.
Display of these areas on a GIS-generated map has obvious benefits for the planning
of disease control strategies. Therefore, there is a need to facilitate short-term
epidemic forecasting and to improve scenario-based predictive modelling for the
control and prevention of RRV. It is anticipated that the analyses of spatio-temporal
relationships between risk factors and disease transmission will improve our
understanding of biological/ecological mechanisms of disease outbreaks, and will
assist us to develop scientifically-sound, early-warning systems for this disease.
1.2 AIMS AND HYPOTHESES
This study aims to examine the potential applications of GIS and spatio-temporal
modelling in the surveillance and control of RRV disease.
25
1.2.1 Aims Ø Visualise temporal and spatial distributions of RRV disease in Brisbane,
Queensland;
Ø Conduct exploratory analyses of the potential determinants (eg, climate
variability, vegetation types, mosquito density and population movement, etc) of
these distributions;
Ø Develop a preliminary spatio-temporal epidemic forecasting model of RRV.
1.2.2 Hypothesis
The central hypothesis to be tested is that the transmission of RRV is associated with
a range of socio-ecological factors and this association can be assessed using GIS and
spatio-temporal modelling approaches. As a result of this study, the applications of
GIS and spatio-temporal modelling will assist the surveillance and control of RRV
disease.
Specific hypotheses
(a) Spatio-temporal distribution of RRV can be assessed using GIS;
(b) The distribution of RRV disease is related to socio-ecological variability, and
this relation can be determined by spatio-temporal modelling;
(c) Socio-ecological factors can be used to predict the occurrence of RRV by the
combined use of GIS and spatio-temporal models.
1.3 SIGNIFICANCE OF THE THESIS
26
This study assists in quantifying the relationships between socio-ecological factors
(climate variables, mosquito density, vegetation and human population) and the
epidemic potential of RRV infection in Brisbane, Queensland. It contributes to the
growing literature on the assessment of potential impacts of socio-environmental
change upon the transmission of RRV infection. Increased understanding of the
relative importance of socio-ecological variables in the transmission cycles of RRV
will aid public health planning and policy-making to develop effective strategies to
control and prevent this wide-spread disease. Epidemic forecasting models were
developed and may be directly used for the decision-making process in the
surveillance and control of RRV disease. Additionally, the methods developed
through this study may have a wider application to other public health problems.
1.4 CONTENTS AND STRUCTURE OF THE THESIS This thesis is presented in the publication style. As such, it contains five manuscripts,
each designed to stand on its own. Chapter 2 critically reviews the literature relating
to applications of spatio-temporal model. Chapter 3 provides the study design and
methods.
The five manuscripts are presented in Chapters 4 through 8 (Figure 1.1). Each
manuscript was written in the conventional publication style for a particular journal.
Because each manuscript was designed to stand alone, there was an inevitable degree
of repetitiveness in their introduction, methods and discussion sections.
The first manuscript aimed to visualize the spatio-temporal distributions of notified
RRV infections in Statistical Local Areas (SLAs) of Brisbane and was submitted to
27
Arbovirus Research in Australia. The second manuscript identified socio-economic
and environmental determinants of RRV disease transmission at an ecological level in
Brisbane and was submitted to Acta Tropica. The third manuscript examined the
potential impact of climate variability on the transmission of RRV disease and
explored the possibility of developing an epidemic forecasting system for RRV
disease using the multivariate SARIMA technique, which was published in American
Journal of Tropical Medicine and Hygiene. The fourth manuscript aimed to develop
an epidemic forecasting model using local mosquito density data to predict outbreaks
of RRV disease and was submitted to Ecological Modelling. The fifth manuscript
aimed to identify major mosquito species of RRV disease and to explore the threshold
of mosquito density for transmission and is to be submitted to Journal of Medical
Entomology.
Chapter 9 summarizes the study findings across the five manuscripts, and discusses
conclusions in relation to the overall aims of the study. This chapter further discusses
the study limitations, directions for future research, and public health implications of
the research.
Tables and figures are provided in the text to facilitate reading. The references for
each of the manuscripts are presented at the end of their corresponding chapters. A
complete reference list (including references cited in the manuscripts) is provided at
the end of the thesis.
28
Figure 1. 1 Flowchart of manuscripts in thesis
Chapter 4 Visualise the spatio-temporal distribution
Chapter 5 Identify socio-environmental determinants
Chapter 6 Developing
Predictive model
Socio-economic Climate
Vegetation Mosquito
Climate High tide
Chapter 7 Developing
predictive model
Mosquito density
Chapter 8
Exploring the threshold of
mosquito density
Manuscript 1
Manuscript 3
Manuscript 2
Assist surveillance and control of RRV disease
Manuscript 4
Manuscript 5
29
CHAPTER 2: APPLICATIONS OF GIS AND SPATIAL ANALYSIS IN MOSQUITO-BORNE DISEASE
RESEARCH: A REVIEW OF RELATED LITERATURE
Mosquito-borne diseases (MBDs) are prevalent and a significant cause of disease
burden in more than 100 countries, infecting 700 million people and causing about 3
million deaths every year (Fradin and Day 2002). MBDs typically have strong spatial
and temporal patterns, because mosquito density and longevity depend on a number of
environmental and ecological factors (e.g., temperature, precipitation and mosquito-
breeding habitats). GIS and spatio-temporal modelling methods offer new and
expanding opportunities for MBD research because they can display and model the
spatial relationship between cause and disease (Cressie 1991, Clarke et al. 1996, Khan
1999).
2.1 SYSTEMATIC REVIEW
Although there are some excellent reviews of GIS in public health (Clarke et al. 1996,
Moore and Carpenter 1999, Cromley 2003, Croner 2003, Ricketts 2003, Rushton
2003), there was still a need to examine systematically the applications of GIS and
spatial analysis in MBD research. This study aims to evaluate methodologies,
strengths and limitations of GIS and spatial analysis tools, and to make
recommendations for further applications of GIS and spatial analysis in MBD
research.
2.1.1 Design
30
The systematic review was based on empirical studies of MBD (e.g., malaria, dengue,
lymphatic filariasis, West Nile virus, Japanese encephalitis, Rift Valley Fever and
Ross River Virus diseases) that utilized GIS and spatial analysis. These MBD were
chosen because of their substantial health impact, causing about millions deaths
worldwide every year (The Center for Disease Control and Prevention 2004).
2.1.2 Search methods A comprehensive literature search was conducted using MedLine which contains
bibliographic citations from more than 4,600 biomedical journals. MedLine was
selected as the main database because it covered over 95% of related articles in a pilot
study. The key words used in this study included “geograph* information system*”
for general health domains and “(geograph* information system* or spa* analysis)
and (malaria or dengue or lymphatic or Ross River virus or West Nile or Japanese
encephalitis or Yellow fever or Rift valley fever)” for MBD (search methods were
defined by Medline EBSCOhost database). 815 articles (review articles: 10.7%;
empirical articles: 89.3%) that were published between 1986 and 2003 were reviewed
for all health domains, as well as 58 empirical articles for MBD including malaria
(43), dengue fever (7), lymphatic filariasis (4), West Nile virus (3) and Ross River
virus (1) (Figure 2.1).
31
Figure 2. 1 The results of search based on GIS and spatial analysis in Medline
2.1.3 Coding and analysis A standardised coding system was developed for the study and codes were entered
directly into a database. All studies were coded on as many dimensions as possible, so
that the characteristics of MBD studies could be quantified. Categorizing of study
design was established on the basis of data collection, GIS methods, spatial analysis
methods, study purpose, study scale, exploratory factors and spatio-temporal model
(Table 2.1). All 58 empirical articles in MBD were reviewed. Cross-checking and
double data entry were performed to ensure the quality of data. All data processing
Medline
Mosquito-borne diseases (empirical articles) Health domain
Keywords: Geograph* information system* or spa* analysis Keywords: Geograph* information system*
Empirical articles
Review articles
Malaria
Dengue
Lymphatic
RRV
43
7
4
1
87
728
West Nile
3
32
was performed using the Statistical Package for the Social Sciences (SPSS) program
(Statistical Package for the Social Sciences 1997a).
Table 2. 1 The coding categories for the literature review
Dimensions Sub - Dimensions
Data collection Field survey Disease surveillance system Remote Sensing and Global Positioning System
GIS methods Visualisation Exploratory Modelling
Study scale
Country State City (Town) Suburb
Study purpose Identify disease risk factors Improve disease prediction
Spatial analysis model Clustering Dispersion (diffusion) Interpolation techniques
Exploratory factors Climate factors Social-economic factors Ecological factors
Spatio-temporal model Time factors Climate, social-economic and ecological factors
2.1.4 Results
A number of interesting trends have emerged from the analysis. Figure 2.2 shows the
distribution of relevant articles by year. There has been a substantial increase in the
use of GIS in the health research domain between 1986 and 2003.
Table 2.2 shows the distribution of GIS/spatial analysis-related manuscripts by order
of number of papers in health science journals (first 50 journals). Environmental
Health Perspectives, Environmental Management, Water Science and Technology,
33
Journal of Environmental Management, American Journal of Tropical Medicine and
Hygiene and Social Science and Medicine were the most common vehicles of GIS-
related articles. Table 2.3 shows that the American Journal of Tropical Medicine and
Hygiene, Southeast Asian Journal of Tropical Medicine and Public Health,
Transactions of the Royal Society of Tropical Medicine and Hygiene, American
Journal of Epidemiology, Annuals of Tropical Medicine and Parasitology, Bulletin of
the World Health Organization, Computer Methods and Programs in Biomedicine,
International Journal of Epidemiology and Tropical Medicine and International
Health were the most common vehicles for empirical articles relating MBD and GIS.
Figure 2.3 shows the percentage of the empirical papers on MBD by year (Figure 2.3a)
and by disease (Figure 2.3b). Of all articles coded, 72.0% were related to malaria
research, and others were related to dengue fever (12.0%), lymphatic filariasis (9.0%),
West Nile (5.0%) and Ross River viruses (2.0%).
Figure 2. 2 Trends of publications on GIS for general health domains
0
50
100
150
200
250
1986 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
Year
Num
bers
Empirical articles Review articles
34
Table 2. 2 Article numbers by journal based on general health domain (First 50 journals)
Journals Numbers ENVIRONMENTAL HEALTH PERSPECTIVES 30 ENVIRONMENTAL MANAGEMENT 30 WATER SCIENCE AND TECHNOLOGY 25 JOURNAL OF ENVIRONMENTAL MANAGEMENT 24 AMERICAN JOURNAL OF TROPICAL MEDICINE AND HYGIENE 23 SOCIAL SCIENCE & MEDICINE 23 THE JOURNAL OF APPLIED ECOLOGY (CHINA) 17 ENVIRONMENTAL MONITORING AND ASSESSMENT 16 SCIENCE OF THE TOTAL ENVIRONMENT 13 HEALTH & PLACE 12 JOURNAL OF PUBLIC HEALTH MANAGEMENT AND PRACTICE 12 ACTA TROPICA 11 EPIDEMIOLOGY 11 PREVENTIVE VETERINARY MEDICINE 10 STATISTICS IN MEDICINE 10 ANNALS OF TROPICAL MEDICINE AND PARASITOLOGY 9 JOURNAL OF EXPOSURE ANALYSIS AND ENVIRONMENTAL EPIDEMIOLOGY 9 TROPICAL MEDICINE & INTERNATIONAL HEALTH 9 EMERGING INFECTIOUS DISEASES 8 ENVIRONMENTAL POLLUTION 8 AMERICAN JOURNAL OF PUBLIC HEALTH 7 ENVIRONMENTAL RESEARCH 7 ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY 7 JOURNAL OF MEDICAL ENTOMOLOGY 7 SOUTHEAST ASIAN JOURNAL OF TROPICAL MEDICINE AND PUBLIC HEALTH 7 ACCIDENT; ANALYSIS AND PREVENTION 6 ENVIRONMENT INTERNATIONAL 6 JOURNAL OF ENVIRONMENTAL QUALITY 6 JOURNAL OF ENVIRONMENTAL SCIENCE AND HEALTH 6 MEDECINE TROPICALE : REVUE DU CORPS DE SANTE COLONIAL (MARS) 6 RISK ANALYSIS : AN OFFICIAL PUBLICATION OF THE SOCIETY FOR RISK ANALYSIS 6 THE SCIENTIFIC WORLD JOURNAL [ELECTRONIC RESOURCE] 6 VETERINARY PARASITOLOGY 6 AMBIO 5 AMERICAN JOURNAL OF EPIDEMIOLOGY 5 ANNALS OF THE NEW YORK ACADEMY OF SCIENCES 5 ANNUAL REVIEW OF PUBLIC HEALTH 5 AUSTRALIAN AND NEW ZEALAND JOURNAL OF PUBLIC HEALTH 5 CENTRAL EUROPEAN JOURNAL OF PUBLIC HEALTH 5 GROUND WATER 5 INTERNATIONAL JOURNAL OF HYGIENE AND ENVIRONMENTAL HEALTH 5 JOURNAL OF ENVIRONMENTAL SCIENCES (CHINA) 5 JOURNAL OF THE AIR & WASTE MANAGEMENT ASSOCIATION 5 JOURNAL OF THE EGYPTIAN SOCIETY OF PARASITOLOGY 5 MOLECULAR ECOLOGY 5 TRANSACTIONS OF THE ROYAL SOCIETY OF TROPICAL MEDICINE AND HYGIENE 5 CHINESE JOURNAL OF EPIDEMIOLOGY 5 ADVANCES IN PARASITOLOGY 4 ARCHIVES OF ENVIRONMENTAL HEALTH 4
BIOELECTROMAGNETICS 4
35
Table 2. 3 Article numbers by journal based on MBD
Journals Total
AMERICAN JOURNAL OF TROPICAL MEDICINE AND HYGIENE 13 SOUTHEAST ASIAN JOURNAL OF TROPICAL MEDICINE AND PUBLIC HEALTH 5 TRANSACTIONS OF THE ROYAL SOCIETY OF TROPICAL MEDICINE AND HYGIENE 4 AMERICAN JOURNAL OF EPIDEMIOLOGY 3 ANNALS OF TROPICAL MEDICINE AND PARASITOLOGY 3 BULLETIN OF THE WORLD HEALTH ORGANIZATION 3 COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 3 INTERNATIONAL JOURNAL OF EPIDEMIOLOGY 2 MEDICAL AND VETERINARY ENTOMOLOGY 2 TROPICAL MEDICINE & INTERNATIONAL HEALTH 2 CHINESE JOURNAL OF PARASITOLOGY & PARASITIC DISEASES 1 CHINESE JOURNAL OF PREVENTIVE MEDICINE 1 AFRICA HEALTH 1 ARCHIVES DE L'INSTITUT PASTEUR DE MADAGASCAR 1 ECOLOGICAL APPLICATIONS: A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 1 THE KAOHSIUNG JOURNAL OF MEDICAL SCIENCES 1 HEALTH & PLACE 1 HEALTH PSYCHOLOGY 1 JAPANESE JOURNAL OF INFECTIOUS DISEASES 1 JOURNAL OF MEDICAL ENTOMOLOGY 1 JOURNAL OF THE AMERICAN MOSQUITO CONTROL ASSOCIATION 1 JOURNAL OF THE EGYPTIAN SOCIETY OF PARASITOLOGY 1 JOURNAL OF VECTOR ECOLOGY 1 PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 1 PROCEEDINGS OF THE ROYAL SOCIETY OF LONDON 1 PAN AMERICAN JOURNAL OF PUBLIC HEALTH 1 SOUTH AFRICAN MEDICAL JOURNAL 1 VECTOR BORNE AND ZOONOTIC DISEASES 1 GRAND TOTAL 58
36
Figure 2. 3 Trends and distribution of empirical articles on GIS and spatial analysis for MBD
0
2
4
6
8
10
12
14
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
Year
Num
bers
Numbers of empirical articles
Malaria72%
Dengue12%
Lymphatic filariasis9%
Ross River virus2%
West Nile5%
37
Table 2.4 presents the characteristics of MBD research articles according to
categorization of study. Most data collections adopted a disease surveillance system
(63.8%), or with Remote sensing (RS)/Global position system (GPS) (15.5%) or with
field survey (13.8%). Most research methods employed visualisation and exploratory
data analysis (43.1%), although 27.6% of the studies also presented modelling and an
additional 24.1% used visualisation alone. The main study purposes were to identify
risk factors (55.2%). The main spatial scale was country (43.1%), but other
geographic units (eg, city and suburb) were also commonly used. Ecological factors
were the main risk factors used as exploratory variables alone (27.6%), or with
climate factors (13.8%) or with socio-economic factors (12.1%). The majority of
studies (86.2%) did not employ spatio-temporal modelling, but 12.1% of articles
included time factors, and only 1.7% of articles used climate, socio-economic and
ecological factors in spatio-temporal modelling.
38
Table 2. 4 Characteristics of 58 MBD papers
Characteristic Numbers (%) Data collection Field survey 2 3.5 Disease surveillance 37 63.8 RS*/GPS** 0 0.0 Field survey and disease surveillance 8 13.8 Field survey and RS/GPS 0 0.0 Disease surveillance and RS/GPS 9 15.5 Field survey and disease surveillance and RS/GPS 2 3.4 100.0 GIS methods Visualisation 14 24.1 Exploratory 0 0.0 Modelling Visualisation and exploratory
0 25
0.0 43.1
Visualisation and modelling 3 5.2 Exploratory and modelling 0 0.0 Visualisation and exploratory and modelling 16 27.6 100.0 Spatial analysis methods Clustering 11 19.0 Dispersion 1 1.7 Interpolation 2 3.4 Clustering and dispersion 0 0.0 Clustering and interpolation 1 1.7 Dispersion and interpolation 0 0.0 Clustering and dispersion and interpolation 4 6.9 No spatial analysis 39 67.3 100.0 Study Purpose Identify disease risk factors 32 55.2 Improve disease prediction 8 13.8 Identify disease risk factors and improve disease prediction 18 31.0 100.0 Study scale Country 25 43.1 State 16 27.6 City (town) 6 10.3 Suburb 11 19.0 100.0 Explanatory factors Climate factors 4 6.9 Social-economic factors 4 6.9 Ecological factors 16 27.6 Climate factors and social-economic factors 0 0 Climate factors and ecological factors 8 13.8 Social-economic factors and ecological factors 7 12.1 Climate and social-economic factors and ecological factors 2 3.4 No explanatory factors† 17 29.3 100.0 Spatio-temporal model Time factors 7 12.1 Climate and social-economic and ecological and time factors 1 1.7 No spatio-temporal model 50 86.2 100.0 * RS: Remote sensing; ** GPS: Global position system, †: All these papers did not incorporate/explore explanatory factors.
39
2.1.5 Discussion Four major findings have arisen from this review. Firstly, the use of GIS for health
research has increased significantly over the past 10 years. Secondly, the majority of
papers with a focus on MBD have been related to malaria, indicating a growing
awareness among this research community of the importance of varying forms of
spatial analysis. Thirdly, fewer than one-third of the studies used a spatial model
(32.7%) and a small percentage used a spatio-temporal model that incorporated
climatic, social-economic and ecologic factors (1.7%). Finally, journals across the
globe are acknowledging and promoting the use of GIS and spatial analysis.
Field survey, disease surveillance system and RS/GPS techniques have been the three
primary data collection methods used, with disease surveillance system being the
most common. Only a few studies (19%) applied both RS and GPS, which are
important data collection tools that can provide useful information in predicting the
spatial and temporal distribution of disease (Clarke et al. 1991, Beck et al. 1994, Hay
1997). The application of this technology to MBD research is likely to increase due to
its ability to include a spatial and spatio-temporal component to disease prevention,
management and control.
Visualisation, exploratory analysis and modelling are the three primary spatial
analysis methods utilized in MBD research (Cressie 1991, Briggs 1992, Walter 1993,
Clayton and Hills 1994, Rushton et al. 1996, Torok et al. 1997, Morrison et al. 1998,
Kleinschmidt et al. 2000, Pickle 2000). These methods can improve our
understanding of the biological/ecological mechanisms of disease outbreaks and assist
in assessing the spatio-temporal relationships between risk factors and disease
40
transmission. Model building in particular can be used to test hypotheses about the
causes, nature and processes of disease transmission (Cliff and Ord 1981, Oliver and
Webster 1990, Longstreth 1991, Longley and Batty 1996, Kitron and Kazmierczak
1997, Moore 1999, Kleinschmidt et al. 2000). Most research methods employed
visualisation and exploratory data analysis (43.1%) without modelling. These research
tools can be used to identify the spatial relationship and space-time clusters of disease.
However, spatial analysis methods appeared to be relatively uncommon in mosquito-
borne disease research, as 67.3% of articles had not used such methods.
Most of the previous studies concerned with factors that influence the transmission of
MBD have not taken into account the spatio-temporal features of these diseases in the
modelling processes. As discussed later in this chapter, the quantitative relationship
between socio-ecological variables and the transmission of MBD remains unclear.
Fully integrated and validated spatio-temporal statistical models using climate
variables, socio-economic, and ecological factors should be developed. Such a
modelling approach may have significant applications in the development of an
epidemic forecasting model of MBD. A software package for manipulation of GIS
data and application of spatio-temporal modelling may also need to be developed for
the surveillance and control of MBD.
2.2 CRITICAL APPRAISAL OF KEY SPATIO-TEMPORAL ANALY TIC METHODS
2.2.1 GIS capabilities GIS is an integrated set of computer hardware and software tools designed to capture,
store, retrieve and display spatially-referenced data (Bailey and Gatrell 1995). GIS
41
can be used to generate maps and perform some spatial analyses. Some of these
technologies, like the GPS and RS, are often used to collect geographic data.
Data capture implies that data can be imported by using the GIS from existing
external digital sources. GIS is capable of importing the most common data formats
both for image-type and line-type maps. Additionally, a GIS user can scan a map and
input it into the GIS database, or trace over a map’s features using a digitising tablet.
Further, GIS can accomplish all functions of a regular database system, such as
entering and editing data and updating information in an existing database (Clarke et
al. 1996, Khan 1999).
Data storage refers to storage of both attribute and map data. Attribute data are usually
stored in a relational database management system. Map data must be first encoded
into a set of numbers. Image maps are usually stored as grided arrays. The more
efficient and flexible these data formats or structures, the more operations can be
performed on the map data without further processing (Moore and Carpenter 1999).
Data records can be retrieved in one of two ways. The relational database manager
allows searching, reordering, and selecting on the basis of a feature's attributes and
values. GIS also allows spatial retrieval. For example, the user could select all clinics
that are more than 10 kilometres from a major road and within 100 metres of a river or
lake (Clarke et al. 1996).
Display functions include predominantly the presentation of maps. Tools exist for
constructing many types of maps (eg, contours, symbols, shading or choropleth, and
42
sized symbols). Disease mapping capabilities include mapping point locations of
cases, incidence rates by area, and standardised rates. Although a pattern of disease
diffusion is visually apparent at this stage, it is necessary to employ spatial analytical
techniques for better understanding the complex nature of the spatial trend (Dale and
Morris 1996).
2.2.2 Spatial analytical methods Some common spatial techniques used in communicable disease research include
clustering techniques, analysis of relative spaces, diffusion studies, dispersion and
interpolation (Gesler 1986, Moore and Carpenter 1999) (Table 2.5), as described
further below.
43
Table 2. 5 Statistical techniques and computer software for spatial analysis*
Format Data type Major strength Major limitation So ftware Clustering Point
Moran’s I Continuous Identification of local area clusters
Ignores underlying distribution of population at risk
STAT!,(Jacquez 1994)Space-Stat, (Anselin and Bao 1998)TSpStat(Carpenter 1999)
K-function Case-control Identification of local area clusters, adjusts for population distribution
May ignore temporal occurrence of events
Splus(Rowlingson and Diggle 1993)
Geographic analysis machine
Dichotomous Adjusts for population distribution
Computer intensive Geographic analysis machine(Openshaw et al. 1987)
Spatial scan Case data Adjusts for population distribution; identifies primary and secondary clusters
Computer intensive SaTScan(Kulldorf et al. 1996)
Area Joint counts Dichotomous Identification of large-
scale clusters Low power Space-Stat, TSpStat
Ohno Categorical Identification of large-scale or local area clusters
Low power; ignores adjacencies non-adjacent areas
Cluster, (Public Health Service 1992)TSpStat
Moran’s I Continuous Identification of large-scale clusters
May ignore close, non-adjacent areas
STAT!,Space-Stat
Poisson Intergers Identification of large-scale or local area clusters
Ignores adjacencies Cluster, TSpStat
Dispersion Line analysis Simulation Compares disease front to
random walk to detect a pattern of movement; can get rate and pattern spread
Requires programming
Trend surface Empirical data; time to first report
Can model time to first occurrence, model pattern, and rate of spread
Does not account for spatial auto-correlation
ArcView Spatial Analyst(ArcView and GIS3.1 1998)
Spatial adaptive filtering
Empirical data and simulation
Used to forecast case-number using population as predictor
Requires programming
Expansion method
Simulation Model diffusion in space and time
Need understanding of parameter relations
Interpolation
Splining Fits a minimum surface Not appropriate if large changes in short distance
ArcView Spatial Analyst
Inverse distance weighted
Weights values of neighbours for prediction of new point value
ArcView Special Analyst
Kriging Handles spatial autocorrelation; estimates both local population density and block averages
Not sensitive to preferential sampling
GeoEas; ArcView Spatial Analyst
Trend surface Process triangulation to determine neighbours in successive points on map
Mapinfo; ArcView Special Analyst
* Modified from Moore and Carpenter, Epidemiology Reviews Vol. 21, No. 2, 1999.
44
2.2.2.1 Detection of clusters Spatial clustering methods are exploratory tools that help researchers and
policymakers make sense of complex geographic patterns. Knowing whether or not
clusters exist and where they are located provides an important foundation for health
research and policy formulation.
A cluster can be a number of health events situated close together in space and/or time.
Areal (or regional) data are usually used to identify clusters on a larger scale such
SLAs. Two techniques, Geary’s c and Moran’s I, are similar in that they compare
adjacent area values in order to assess the level of large scale clustering(Moran 1948,
Geary 1954). However, tests based on Moran’s I are consistently more powerful than
those based on Geary’s c (Cliff and Ord 1981, Walter 1992). Both Geary’s c and
Moran’s I techniques may find clusters of high risk, but they have negligible power in
detecting highly localised hot spots. Moran’s test has been frequently applied to a
variety of epidemiological problems (such as Lyme disease) to test areal clusters
(Shafer 1980).
In spatial analysis, k-mean cluster analysis is used to group together entities with
similar properties. The cluster analysis method divides a large number of objects into
a smaller number of relatively homogeneous groups on the basis of their structure
(Tabachnick and Fidell 1996). K-mean cluster analysis is used to describe a number
of different classification algorithms. Its purpose is to join objects into successively
larger clusters (hierarchical tree) using some measure of similarity between the
objects. The K-means algorithm addresses a different problem, namely that of which
objects belong to a certain predefined number of clusters.
45
Spatial auto-correlation analysis is an additional technique that has been employed to
detect disease patterns. It is defined as the relation among values of a single variable.
A good introduction to spatial auto-correlation is given in the review by Goodchild
(Goodchild 1985). It describes the auto-correlation in a variable by computing some
index of covariance for a series of lag distances (Davis 1986). Correlation usually
decreases with distance until it reaches or approaches zero.
2.2.2.2 Interpolation and smoothing Some spatial techniques are widely used to interpolate new data points, “smooth” data,
or filter signals from noise. Inverse distance weighting is the simplest interpolation
method. A neighbourhood about the interpolated point is identified and a weighted
average is taken of the observation values within this neighbourhood. The weights are
a decreasing function of distance (Moore and Carpenter 1999). Kriging is a technique
used to estimate point values by using surrounding, known point values (Oliver and
Webster 1990). It is a method of spatial prediction using a weighted moving average
interpolation to produce the optimal spatial linear prediction (Cressie 1991). This
method/technique has been used in geostatistics as an interpolation method and is
considered as the best linear estimate of the characteristic under study because it
reflects the minimum mean square error. Kriging has also been widely used in
epidemiological studies. For example, the spatial and temporal distribution of
Anopheles gambiae mosquitoes in houses in a village in Ethiopia was monitored
(Ribeiro et al. 1996). Using Kriging techniques, investigators demonstrated clustering
at the edges of the village and the changing pattern over time. Kleinschmidt et al.
(2000) employed Kriging approaches to improve malaria prediction at a local level in
Mali. Study of the spread of a disease over large areas has been the subject of a more
46
recent study, which used Kriging to estimate the underlying spatial process of an
influenza-like epidemic in France (Carrat and Valleron 1992).
2.2.3 Temporal Analytic Techniques
Time series analysis has increasingly been employed for control and prevention of
communicable diseases. The common analytical framework uses time series models
to forecast or estimate expected numbers of cases, followed by comparison with
actual observations. Most attention has been focused on the use of the Box-Jenkins
modelling strategy to construct Seasonal auto-regressive integrated moving average
(SARIMA) models for specific health variables including vector-borne disease
(Helfenstein 1986, Stroup et al. 1988, Walter 1993, Tong and Hu 2001, Hu et al.
2004, Tong et al. 2004). The modelling strategy analyses a long series of values in a
stationary mode. However most health variables of interest are not stationary, and
analysts have to resort to preliminary transformations, such as time series differencing
or variance-stabilising to achieve stationary status. After choosing the transformation,
the steps of model identification, parameter estimation, and diagnostic checking are
performed. Key tools for modelling are the auto-correlation function (ACF) and the
partial auto-correlation function (PACF) (Helfenstein 1986, Tong and Hu 2001). As
discussed above, for adequate modelling, a time series should be stationary with
respect to mean and variance. If the mean increases or decreases over time, the series
may need to be transformed (eg, differenced) to make it stationary, before being
modelled (Allard 1998). A simple inspection of the graph of the untransformed series
is the most useful approach. Similarly, if the variance (as indicated by the excursions
around the mean becoming smaller or larger over time) increases or decreases some
47
transformation (logarithm or square root, etc) should also be applied. A time series
with seasonal non-stationarity may be transformed to stationary data by taking
seasonal differences into account (Bowie and Prothero 1981, Helfenstein 1991).
It is important to identify and remove the trend and seasonal components when
modeling the exposure-outcome relations using time-series data (Tabachnick and
Fidell 2001). When this is not done, highly seasonal series can appear to be related,
purely because of their seasonality rather than because of any real relationship.
Similarly, trended series can also exhibit spurious collinearity. Consequently, the
estimation of the potential impact of trend components, and the development of
appropriate approaches to remove their effects, are important methodological issues in
any time series analysis, especially in the analysis of a dependent variable and its
potential explanatory variables or risk factors.
Cross-correlations can be used to compute a series of correlations between dependent
variables and independent variables over a range of time lags (here, a time lag is
defined as the time span between observation of dependent and independent variables)
(Chatfield 1975). The polynomial distributed lag (PDL) time series models can reduce
the effect of temporal multicollinearity. These models have been used for decades in
econometrics (Judge et al. 1980) and recently the approach has been applied in
epidemiology (Pope and Schwartz 1996, Schwartz 2000).
An advantage of the PDL model is that it does not require the specification of a
temporal relationship between the response and explanatory variables, and
additionally the degree of the polynomial term can be identified as part of the analysis.
48
This, combined with the flexibility of the PDL model in describing a very large range
of temporal patterns, makes it an ideal ‘semi-parametric’ choice for epidemiological
modelling.
The predictive validity of the models can be evaluated by using the root mean square
(RMS) error and RMS percentage error criterion (RMS error = [�=
N
t 1
( t-Yt)2/N]1/2;
RMS percentage error = {N
1�
=
N
t 1
[( t-Yt)/Yt]2} 1/2, where t is the predicted value and
Yt is the observed value for month t, N is the number of observations) (Makridakes et
al. 1998). The smaller the RMS error, the better the model in terms of the ability of
forecast.
2.2.4 Applications of GIS and Spatio-Temporal Metho ds in disease surveillance and control Emerging and reemerging infectious diseases are important challenges requiring new
responses from public health and medical care systems. Ecological studies of agent-
vector-host relationships and improved surveillance methods have been cited as
important priorities for addressing these infectious disease problems. GIS analysis is
playing an important role in the renewal of efforts to view the problems of infectious
disease at a variety of geographic scales, including the global scale.
Among the most important types of exploratory analysis for MBDs are methods for
identifying space-time clusters of disease. Areas may differ greatly in population size,
and therefore, prevalence rates have different levels of variability and thus reliability
(Clayton and Kaldor 1987). However, many methods used for exploratory analysis of
49
disease patterns are not appropriate for MBDs, because the methods are essentially
static and assume independence. With MBDs, cases clearly are not independent and
the diseases move through time and space. In these situations, one can use spatial-time
auto-correlation methods to explore the spatial and temporal patterns of MBDs.
Researchers have long used probability mapping to show the statistical significance of
prevalence rates (Clayton and Kaldor 1987); however, probability mapping does not
give a sense of the actual rates or the populations on which they are based. An
alternative method is to smooth rates towards a regional or local mean value using,
among other approaches, empirical Bayes methods (Ord and Getis 1995). GIS can be
used to generate geographically based regional or local means to which actual rates
are smoothed. These might be based on averaging rates for contiguous areas, or they
might rely on more complex, multivariate, spatial clustering procedures that
incorporate proximity as well as population attributes.
Modelling is an important part of the spatial analysis of communicable disease, which
includes procedures for testing hypotheses about the causes of disease and the nature
as well as processes of disease transmission. In general, modelling involves the
integration of GIS with standard statistical and epidemiological methods. GIS can
assist in generating data for insertion to epidemiological models, displaying the results
of statistical analyses and modelling processes that occur over space. GIS has been
used in a particular study on not only to integrate diverse data sets and calculate new
variables, but also to map geographic variation in disease risk, as predicted from a
logistic regression and Poisson regression model (Clarke et al. 1996).
50
Using GIS and GPS, Chadee et al (Chadee and Kitron 1999) mapped the precise
location of all reported malaria cases, and associated them with breeding habitats of
Anopheline vectors. The spatial and temporal clustering of malaria cases was analysed
statistically with k nearest neighbour statistic. The results of this study indicate that
local transmission is most likely to follow the detection of a P. malariae case in areas
where An. bellator is common. The application of a GIS and a space-time statistic
provided visual and quantitative confirmation of the suspected local transmission of
P. malariae in the interior of Trinidad and outbreak of P. vivax in the southwest
corner. The management of malaria surveillance data in a GIS will allow for the rapid
production of maps and statistical analyses that will identify clusters of cases and
assist in directing the necessary resources for control activities.
Thompson et al (1997) analysed malaria transmission over time and space in densely
populated malaria-endemic areas using rainfall, water, and vegetation state along with
malaria transmission indices. The spatial analyses of the malaria risk in this area
showed that the distance from water was the most important risk factor in this
population.
In an analysis of the distribution of Lyme disease in Wisconsin, GIS was used to
associate county-level data on tick distribution, human population density, Lyme
disease case distribution, and proportion of wooded areas, to help explain the
distribution of the disease in the state (Kitron and Kazmierczak 1997). The GIS
allowed user to obtain location data and measure distances between locations.
Measures of spatial auto-correlation and local spatial statistics were used to identify
clusters of disease cases.
51
In some studies, GIS is also utilized in combination with a statistical modelling
technique such as logistic regression. For instance, in a study of environmental risk
factors for Lyme disease in Maryland, USA, GIS was used to identify a range of
environmental risk factors such as land use/land cover, forest distribution, soils,
elevation, and watersheds (Glass et al. 1995). These variables were then included in a
logistic regression analysis to model risk factors for cases of Lyme disease in certain
areas.
Morrison et al (1998) examined the spatial and temporal distribution of Dengue
outbreak in Florida, Puerto Rico, by using GIS. Spatio-temporal analysis was used to
characterise the spatial clustering patterns for all reported cases. The rapid temporal
and spatial progress of the disease within the community suggests that control
measures should be applied to an entire municipality, rather than to the areas
immediately surrounding houses of reported cases.
Many statistical analyses using spatial data can now be performed within the GIS
environment, although more advanced or complex techniques may require data
analysis within a statistical software package, exporting to the GIS for map displays.
Some GIS programs have been linked to statistical packages, such as the SpaceStat
Extension for ArcInfo and Splus (Table 2.5). Spatio-temporal analytic methods are
evolving rapidly.
2.3 APPLICATIONS OF GIS AND SPATIO-TEMPORAL ANALYTI C METHODS IN RRV RESEARCH
52
Only a few studies have used GIS and spatio-temporal analytic methods in RRV
research. Tong et al (2001) found that the geographic distribution of notified RRV
cases appears to have expanded in Queensland over recent years using GIS. This
finding is consistent with the geographic variation observed in South Australia
(Selden & Cameron 1996). Muhar et al (2000) indicated that the areas with the
highest infection rates of RRV mostly coincide with known major mosquito breeding
sites. However, determinants of the spatial and temporal variation of RRV remain
unclear.
2.3.1 The potential impact of social environmental factors on RRV
Changes in climate and the environment may influence the abundance and distribution
of vectors and intermediate hosts of RRV (Lindsay et al. 1993, Mackenzie et al. 1994).
Precipitation is important in the transmission of mosquito-borne diseases including
RRV infection. All mosquitoes have aquatic larval and pupal stages and therefore
require water for breeding. Quantity, timing and pattern of rainfall would affect the
breeding of mosquitoes. Sufficient amounts of precipitation will assist in maintaining
the mosquito’s breeding habitats further into the summer months, which is
particularly important for fresh-water breeding mosquitoes.
Warmer temperatures may allow mosquitoes such as Culex annulirostris and
Ochlerotatus (formerly Aedes) vigilax to reach maturity much faster than at lower
temperatures (Lindsay et al. 1993). Transmission of an arbovirus may therefore be
enhanced under warmer conditions because more vector mosquitoes become
infectious within their life span. The potential impact of climate changes on RRV in
53
Australia was first addressed in the late 1980s, with speculation that RRV
transmission could be enhanced by extended activity of some major vectors such as
Ochlerotatus. vigilax and Culex annulirostris (Liehne 1998).
High tides and rise in sea-level have been implicated as important precursors of
outbreaks of RRV (McManus et al. 1992, Lindsay et al. 1993, Tong and Hu 2002).
Tidal inundation of saltmarshes is a major source of water for breeding of the
important arbovirus vectors Ochlerotatus vigilas and Ochlerotatus camptorhynchus.
Females of both species lay their eggs on soil, mud substrate and the plants around the
margins of their breeding sites. The eggs hatch when high tides subsequently inundate
sites. Large populations of adult mosquitoes can emerge as quickly as eight days after
a series of spring tides (Lindsay et al. 1993). There is some evidence that a rise in sea-
level may contribute to a major outbreak of RRV. For example, in an outbreak of
RRV in south-western Australia during the summer of 1988-1989, a rise in sea-level
of 5.5 cm (above the long-term mean), exacerbated by a pattern of strong north and
south-westerly winds, led to more frequent and widespread inundation of coastal
saltmarshes in the region than is normally recorded. This subsequently increased the
populations of Ochlerotatus camptorhynchus mosquitoes, and as a result, an outbreak
of RRV infection occurred (Lindsay and Mackenzie 1996).
Relative humidity influences longevity, mating, dispersal, feeding behaviour and
oviposition of mosquitoes (McMichael et al. 1996). At high humidity, mosquitoes
generally survive for longer and disperse further. Therefore, they have a greater
chance of feeding on an infected animal and surviving to transmit a virus to humans
or other animals. Relative humidity also directly affects evaporation rates from vector
54
breeding sites. Clearly, humidity is another factor contributing to outbreaks of RRV
disease, particularly in normally arid regions (Lindsay and Mackenzie 1996).
El Niño/Southern Oscillation (ENSO) is a systematic pattern of global climate
variability (Nicholls 1993). El Niño is a major warming of surface ocean waters in the
eastern tropical Pacific. These events occur irregularly at 2 to 7 years and may persist
for as long as 2 years. They are characterized by shifts in the overall weather pattern.
It affects most countries in the Pacific and Indian Oceans, bringing long droughts and
extended wet periods every two to seven years (Kovats et al. 2000). The Southern
Oscillation refers to a major air pressure shift between the Asian and east Pacific
regions. The Southern Oscillation is measured by a simple index, Southern Oscillation
Index (SOI), defined as the normalized difference in atmospheric pressure between
Darwin (Australia) and Tahiti (French Polynesia). The SOI accounts for up to 40% of
variation in temperature and rainfall in the Pacific (Nicholls 1993). Tong et al (1998)
seemed to support their finding, i.e., there was a moderate positive relationship
between the SOI and the incidence of RRV infection. However, another study found
no association between the SOI and the outbreaks of RRV infection in Australia
(Harley and Weistein 1996).
Social and environmental factors may also contribute and interact in determining
RRV transmission (Tong 2004).Population growth has often led to unplanned and
uncontrolled urbanization, which in turn has resulted in a deterioration of water,
sewage and waste management systems in large, tropical urban centres. The increased
human populations living in intimate contact with increasingly high densities of
mosquito populations create ideal conditions for increased RRV (Mackenzie et al.
55
2000, Muhar et al. 2000, Harley et al. 2001). Environmental factors also affect
immune status, migration, mosquito breeding and mating behaviour and other factors
pertaining to the vertebrate host. Additionally, these factors influence human
behaviour and demographics and may determine the likelihood of human exposure to
RRV (Mackenzie et al. 2000, Harley et al. 2001, Tong et al. 2001). Changes in
agricultural practice such as building dams and irrigation systems have created
ideal larval habitats for selected species, primarily Cules and Anopheles species.
Clearing forests for agricultural use and urban development (near wetlands) may have
the same impact (Mackenzie et al. 2000, Tong et al. 2001). There is evidence showing
that increases in mosquito abundance have occurred in some inland areas because of
provision of irrigation, and in coastal areas because of agricultural, residential and
industrial developments (Russell 1998b).
RRV has been isolated from many mosquito species, indicating wide susceptibility
among mosquitoes (Mackenzie et al. 1994). A major vector is Culex annulirostris
which breeds in freshwater habitats, especially in irrigated areas in inland areas (Kay
1979, Russell 1994, Dale and Morris 1996). Along coastal regions, saltmarsh
mosquitoes represent the major threat, including Ochlerotatus vigilax and
Ochlerotatus camptorhynchus (Dale et al. 1986, Mackenzie et al. 1994, Russell 1994,
Russell 1998a). There is laboratory evidence that Ochlerotatus notoscriptus may also
be a vector in the domestic urban situation (Ritchie et al. 1997, Watson and Kay
1998). However, major mosquito species associated with RRV disease and their roles
in the transmission of RRV remain to be determined (Ritchie et al. 1997, Ryan et al.
1999).
56
Tourism and travel have also become important mechanisms for facilitating the spread
of RRV. For example, the introduction of RRV to the South Pacific in 1979 in a
viraemic human led to the largest RRV epidemic to date (Aaskov et al. 1981a). It was
estimated that more than 100 RRV viraemic travellers may enter New Zealand from
Australia every year (Kelly-Hope et al. 2004).
2.3.2 Development of predictive model of RRV Some studies have examined the relationship between climate variation and RRV
disease (Lindsay et al. 1993, McMichael et al. 1996, Tong et al. 1998, Mackenzie et
al. 2000, Tong et al. 2002). Several models have been developed to predict the
likelihood of RRV epidemics using weather and environmental data (Maelzer et al.
1999, Woodruff et al. 2002). However, some important methodological issues such as
stationarity and auto-correlation of spatio-temporal data have not been formally
addressed in previous research. Fully integrated and validated spatio-temporal
statistical models using climate, socio-economic and ecological factors need to be
further developed.
2.4 KNOWLEDGE GAPS IN THIS AREA Although RRV is the most common VBD in Australia, relatively little research has
been conducted on the spatio-temporal analysis of this disease.
According to the literature, current knowledge gaps are:
57
1. The quantitative relationship between socio-ecological variables and the
transmission of RRV remains unclear. Determinants of the spatial and temporal
variation of RRV need to be assessed;
2. Most of the previous studies concerned with causal factors of the transmission
of RRV have not taken into account the spatio-temporal features of this disease
in the modelling processes. Fully integrated and validated spatio-temporal
statistical models using the socio-ecological data (eg, climate variables,
vegetation distribution, mosquito density and population movement) have not
been formally attempted;
3. Predictive models for manipulation of GIS data and applications of spatio-
temporal analytic methods are yet to be developed for the surveillance and
control of RRV and other VBDs.
In summary, GIS and spato-temporal modelling have great potential for MBDs. The
use of modelling techniques is still in its infancy. Fully integrated and validated
spatio-temporal statistical models need to be further developed. Such a modelling
approach is likely to have significant applications in the control and prevention of
MBDs.
58
CHAPTER 3: STUDY DESIGN AND METHOD This chapter discusses the general study design and methods, as each manuscript has
its own separate detailed methods section.
3.1 STUDY SITE AND STUDY POPULATION The incidence of RRV infection has been high in Queensland during the past decade
(Kay et al. 1984, Mackenzie et al. 1994, Curran et al. 1997, Mackenzie et al. 1998).
Queensland, located in the northeast of Australia, 10-28o south latitude and 138-153o
east longitude, is the second largest state (after Western Australia) but has the largest
habitable area in Australia. It occupies the north-eastern quarter of the continent and
covers approximately 1,727,000 km2, with 7,400 km of mainland coastline (9,800 km
including islands). It has typically sub-tropical climate characteristics with average
temperatures of 25oC in summer and 15oC in winter. Rainfall varies regionally and
seasonally, and most of the state receives over 50% of its rainfall during summer.
Average rainfall varies from less than 150 mm in the southwest region to more than
4,000 mm on the far northern coast. Perennial sub-tropical temperatures and
precipitation make it a suitable environment for the development of mosquitoes,
particularly in coastal regions (Australian Bureau of Statistics 2002).
In this study, Brisbane was selected as the main research site to study the spatio-
temporal relationship between socio-ecological variables (climate variability,
vegetation, mosquito density and socio-economic status) and the transmission of RRV
infection. Brisbane was chosen because it is the capital of Queensland, has the highest
59
population density in the state, and has significant public health implications in
relation to the control and prevention of RRV (Figure 3.1). Brisbane is situated on the
east coast of Australia. The coastal areas are very flat, with extensive mangrove
forests, salt marshes and mudflats. In the west of the densely populated city centre the
terrain is hilly, and to a large extent, covered by rainforests and eucalypt forests.
Within the administrative boundaries of Brisbane City Council, which also determine
the study area of this investigation, the population size was 883,440 in July 2001
(Australian Bureau of Statistics 2002). Brisbane had the highest number of RRV cases
notified in Queensland between 1985 and 2001. The average yearly incidence was
29.14/100,000.
60
Figure 3. 1 Location of the study area - Brisbane
61
3.2 STUDY DESIGN This study applied an ecological research design in assessing major determinants of
RRV disease using spatio-temporal methods and developing epidemic forecasting
models for disease control and risk management planning.
The potential impact of socio-ecological variation on the transmission of RRV
infection was assessed by the following procedures. Firstly, the spatio-temporal
distribution of RRV infection and the expansion of the epidemic foci were examined at
the SLA level in Brisbane with the GIS, over the period 1985-2001. Secondly, the
association of the incidence of RRV infection with socio-ecological variables was
assessed to look for the risk factors for transmission of RRV infection. Finally, a
preliminary epidemic forecasting model of RRV and supportive tools were developed
for improving surveillance systems in Brisbane, Queensland.
3.3 DATA COLLECTION AND MANAGEMENT
3.3.1 Data collection Daily data on RRV cases between 1985 and 2001 in Brisbane have been obtained
from the Queensland Department of Health. They include the onset date and place of
onset of the notified cases of RRV infection, age and sex of the patients and
laboratory test date. As RRV is a notifiable disease, positive test results have to be
reported by laboratories to the Queensland Department of Health. The requirement for
notification of RRV disease is based on a demonstration of IgM antibodies in blood,
demonstration of a fourfold or greater change in serum antibody titres between acute
62
and convalescent phases, isolation of RRV or demonstration of arboviral antigen or
genome in blood (Rich et al. 1993).
Daily average meteorological data for Brisbane from 1985 to 2001 were provided by
the Australian Bureau of Meteorology. They include the records of maximum
temperature, minimum temperature, rainfall, relative humidity at 9am and 3pm, and
SOI.
Daily data on the sea tides along the Brisbane coast were obtained from the
Queensland Department of Transport. They include daily sea level data (high tide, low
tide and the onset time) from 1 January 1985 to 31 December 2001.
Annual population data in each SLA for the period 1985-2001 were obtained from the
Australian Bureau of Statistics. Data on a variety of population characteristics
including numbers of overseas visitors, proportion of indigenous residents, proportion
of labour workers (as defined by the Australian Standard Classification of
Occupations), educational level and family income, were included.
The information on vegetation in Brisbane (littoral wetlands, ephemeral wetlands,
open freshwater, riparian vegetation, melaleuca open forests, wet eucalypt, open
forests and other bushland) has been obtained from the Brisbane City Council.
Data were obtained on the monthly mosquito density at 10 mosquito monitoring
stations in Brisbane, Australia, between November 1998 and December 2001, from
Brisbane City Council. There were at least 14 mosquito species reported.
63
3.3.2 Data management
Information from the electronic records was extracted and coded for health outcome,
climatic variables and other social factors. Monthly incidences of RRV diseases were
calculated and used as response variables. Monthly average climatic variables such as
monthly mean maximum (minimum) temperature, total precipitation, mean relative
humidity, mean high tides and mean SOI, mosquito density, vegetation density and
socio-economic factors, etc. were calculated and used as a time series of independent
variables.
3.4 DATA LINKAGES
Brisbane consists of 162 SLAs in 2001. The digital base map data sets used for
constructing the GIS were obtained primarily from the ABS; and these data were
manipulated to facilitate the accurate identification of the spatial locations of SLA,
and their linkages with the other data layers. The places of onset were geo-coded to
the digital base maps of localities utilising MapInfo and Microsoft Access software.
3.5 DATA ANALYSIS
The changes and secular trends in temporal and spatial distributions of these diseases
were examined using spatio-temporal analytic methods, within both small (eg., SLAs)
and larger (eg., city-wide) scale contexts. There are four stages in the empirical
analysis of the spatio-temporal relationships between socio-ecological variation and
the transmission of RRV disease to address as described below.
64
3.5.1 Stage I- Visualisation of data
The digital base map data sets used for constructing the GIS were obtained primarily
from the ABS and these data were manipulated to facilitate the accurate identification
of the spatial locations of SLA. The onset localities of RRV cases in the data set were
geo-coded to the digital base maps of locations utilising MapInfo (MapInfo
Corporation 2003) and Microsoft Access software. The location for each case of RRV
disease was obtained by overlaying the database of onset localities with the digital
base maps. The GIS software automatically linked onset localities from the data set to
the digital base map database. Onset localities that could not be automatically geo-
coded were matched interactively, using post code as a secondary search criterion to
reduce potential assignment errors.
Temporal and spatial distributions of the RRV cases were described at the SLA level
in Brisbane. MapInfo Professional (MapInfo Corporation 2003) was used to analyse
the trends of disease transmission and to display the spatial and temporal variation of
RRV disease. To visualise their geographic variation over time, onset places of RRV
cases were geo-coded to the digital base maps of localities utilising MapInfo
(MapInfo Corporation 2003) and S-plus (S-Plus Insightful Corporation 2003) for
spatial stats software. Moreover, we constructed such maps locally in order to access
empirical information about the temporal dynamics and other socio-ecological factors
in specific sites.
65
Principal components analysis (PCA) was used as an exploratory technique for
discovering the spatial and temporal structure of RRV. PCA is a technique that
belongs to the broader field of factor analysis (Tabachnick and Fidell 1996). The
extraction of the principal components is successive, with the first principal
components explaining most of the variance in the original data. Each extracted
component is characterized by its eigenvalue which roughly corresponds to the
number of manifest variables this component represents.
K-means cluster analysis was used to classify SLAs into homogeneous subgroups
according to their seasonal incidence of RRV disease. Cluster analysis is used to
describe a number of different classification algorithms (Tabachnick and Fidell 1996).
These algorithms allow the organization of observed data into meaningful structures,
thus promoting the development of taxonomies. Its purpose is to join objects into
successively larger clusters (hierarchical tree) using some measure of similarity
between the objects.
A spatial distribution model was developed using an inverse distance weighted
interpolation between the standardised incidence rates (SIRs). Inverse distance
weighted methods are based on the assumption that the interpolating surface should
be influenced most by the nearby points and less by the more distant points (Kaluzny
et al. 1996).
3.5.2 Stage II- Exploratory data analysis
66
3.5.2.1 Univariate analyses
RRV infection occurs most commonly among those aged between 25 and 39 years
and the male-to-female ratio has been reported as 0.6:1 (Harley et al. 2001), therefore
we used age-standardized incidence rates of RRV disease for both males and females
in exploratory data analysis.
Univariate analyses were conducted to summarize each variable in the data set. The
distribution of incidence of RRV disease was examined. Since the distribution of
seasonal incidence of RRV disease by SLA was typically skewed, a logarithmic
transformation of the seasonal incidences of RRV disease was conducted. The
distributions of the seasonal incidence of RRV disease were normal or almost normal
after their logarithmic transformations. The monthly incidence of RRV incidence by
SLA, followed on approximately Poisson distribution, but data are slightly
overdispersion compared with the Poisson distribution.
Possible impacts of outliers and missing values on analytical outcomes were also
detected and assessed at this stage. Outliers might have a large effect on the fit of
predictive models and the estimated coefficients. The SAS/SPSS software has a
function to detect outliers automatically (SAS Institute Inc. 1997, Statistical Package
for the Social Sciences 1997a). Several outbreaks of RRV disease were detected in
this process. However, these outliers were included in the analysis after the
logarithmic transformation of variables as necessary, because these notifications of
RRV disease were all laboratory confirmed. No apparent outliers were found with
independent variables.
67
Missing values were detected during the process of data analyses. The SAS and SPSS
software has a function to deal with missing data during the process of data analysis.
For example, the softwares will suggest to use alternative listwise, pairwise or
maximum likelihood method to conduct the regression analysis if there is any data
missing. In this study, as four individuals (observations) have missing values on
mosquito density data (June – September 1999), we simply omit those individuals
from the analysis as the missing values appeared to be at random. This approach is
usually called listwise deletion (ie., known as complete case analysis). The listwise
deletion would be based on the non-missing values for all variables. That is, all of the
cases with missing data on any of all variables would be excluded from the analysis.
3.5.2.2 Bivariate analyses
Spearman’s correlation analysis
The relationships between the monthly incidence of RRV and independent variables
were examined using Spearman’s correlation.
Cross correlation analysis
To determine whether socio-ecological variation was associated with RRV disease, the
function of cross-correlations was used to compute a series of correlations between
independent variables and the incidence of RRV disease over a range of time lags.
Multicollinearity
Multicollinearity was considered in this study. Sometimes, the variables were very
highly correlated (eg, maximum and minimum temperature: r>0.8). Muliticollinearity
68
causes both logical and statistical problems. Therefore, these highly correlated
variables were included in separate models.
3.5.2.3 Multivariate regression analysis
< To examine the impacts of social and environmental factors on the RRV disease,
where data were over-dispersed relative to the Poisson distribution, a generalised
linear model (GLM) was adopted with negative binomial link. The distributional
characteristics of RRV and potential determinants were assessed using the
Statistical Analysis System software (SAS 2003). These data were examined at a
SLA level.
< Classification and regression trees (CARTs) were developed to explore the
threshold of mosquito density for RRV disease. For each of the two main
mosquito species, monthly density was used to identify presence or absence of
RRV at a lag of one month. A minimum node deviance of 10% of the total
deviance was used to prune the trees.
< To consider the impacts of seasonality on transmission of RRV disease,
“seasonality” was created as four categorical variables or used a sinusoidal term
(sin(2 *month/12)) to control for seasonality. A comparison was made between
the categorical variables and sinusoidal term and found little difference between
these two methods.
< Possible autocorrelations in the study were also explored. To control for
autocorrelation, a first order autoregressive term was fitted in the model.
69
3.5.3 STAGE III- TIME SERIES FORECASTING MODELS
< Seasonal Auto-regressive Integrated Moving Average (SARIMA) time series
model with environmental variables was used to estimate independent
contribution of each climate variable and of high tide.
< Polynomial Distributed Lag (PDL) time series regression models were used to
examine associations between rainfall and mosquito density.
< It is important to undertake model diagnostics. Within a given analytic approach,
the best model was identified using goodness-of-fit tests and residual analyses.
The goodness-of-fit of the models was checked for adequacy, using both time
series (auto-correlation functions of residuals) and classical tools (to check the
normality of residuals).
3.5.4 STAGE IV- VALIDATION OF THE MODEL
: The robustness of the models was assessed via the collection of further data. To
validate the SARIMA and PDL models, these were applied to predict RRV
infections between January and December 2001 in Brisbane.
In summary, a large scale spatio-temporal approach was used in this study. Major
determinants of the transmission of RRV disease were explored, using GIS and spatial
modelling approach. The key findings were presented in each of the following
chapters.
3.6 THE LIMITATIONS OF THE STUDY
70
The ecology of the transmission of RRV infection is complex and poorly understood.
In this study, only part of the ecological and social factors (climatic variables,
vegetation, mosquito density and human population movement) were studied as data
on other factors (eg, local health promotion expenditure, mosquito control measures,
population immunity, housing conditions and personal health behaviour) were
unavailable. These are major limitations of the study and need to be considered in
future research of RRV disease.
Both under-reporting and over-diagnosis are possible in the NNDSS data (ie,,
notification rates). Under-reporting is likely to occur when people infected by RRV
have sub-clinical conditions and/or did not seek to see a doctor because they knew
there is no effective treatment for this disease; over-diagnosis is also possible in
endemic situations because an IgM response is usually based on a single serum
specimen, and it may represent past infection in a person who currently has another
disease (Mackenzie et al. 1998). Since no population-based serological surveys have
been conducted in Queensland, it is difficult to estimate the accuracy of notification
rates. Therefore, incidence data generally are biased due to the fact that they may
reflect patient access to health services rather than true morbidity, and they are
dependent on good denominator data being available at the same level of aggregation
as the case data. However we have no reason to believe that such under-reporting or
over-diagnosis favours some areas over others in Brisbane.
71
However, in our study, the analytic units were monthly incidence, monthly climate,
monthly high tide time-series data, and the impact of the other social and ecological
factors on the transmission of RRV infection may be minimal, because they were
unlikely to change dramatically within such a short time frame.
.
72
CHAPTER 4: SPATIAL AND TEMPORAL PATTERNS OF ROSS RIVER VIRUS IN BRISBANE, AUSTRALIA
Citation:
Wenbiao Hu, Shilu Tong, Kerrie Mengersen, Brian Oldenburg and Pat Dale (2004).
Spatial and temporal patterns of Ross River virus in Brisbane, Australia. Submitted to
Arbovirus Research in Australia.
Date submitted: September 2004
Contribution of authors:
WH was the principal author of the manuscript, performed all data analysis and wrote
the manuscript. ST and KM contributed to the development of analytical protocol,
interpretation of the results and assisted with writing the manuscript. BO and PD
contributed to the manuscript by providing feedback on the analyses and initial drafts.
73
single dominant transmission route across these three groupings. Therefore, there is a
need to explore socio-economic and environmental determinants of RRV transmission
at the SLA level in future research.
Key words: Cluster analysis, GIS, PCA, RRV, Spatial visualisation
93
CHAPTER 5: SPATIAL ANALYSIS OF SOCIAL AND ENVIRONMENTAL FACTORS ASSOCIATED WITH ROSS RIVER VIRUS IN BRISBANE, AUSTRALIA
Citation:
Wenbiao Hu, Kerrie Mengersen, Brian Oldenburg and Shilu Tong (2004). Spatial
analysis of social and environmental factors associated with Ross River virus in
Brisbane, Australia. Submitted to Acta Tropica.
Date submitted: March 2005
Contribution of authors:
WH was the principal author of the manuscript, performed all data analysis and wrote
the manuscript. ST and KM contributed to the development of analytical protocol,
assisted with interpretation of the results and writing of the manuscript. BO
contributed to the manuscript in terms of providing feedback on the analyses and
initial drafts.
94
119
CHAPTER 6: DEVELOPMENT OF A PREDICTIVE MODEL FOR ROSS RIVER VIRUS DISEASE IN
BRISBANE, AUSTRALIA
Citation:
Wenbiao Hu, Neville Nicholls, Mike Lindsay, Pat Dale, Anthony J McMichael and
John S Mackenzie and Shilu Tong. (2004). Development of a predictive model for
Ross River virus disease in Brisbane, Australia. American Journal of Tropical
Medicine and Hygiene. 71: 129-137.
Date submitted: January 2004
Accepted for publication: March 2004
Contribution of authors:
WH was the principal author of the manuscript, performed all data analysis and wrote
the manuscript. ST supervised the project, interpreted the results and assisted with
writing the manuscript. Other authors contributed to the manuscript in terms of
providing feedback on the analyses and initial drafts.
120
153
CHAPTER 7: RAINFALL, MOSQUITO DENSITY AND THE TRANSMISSION OF ROSS RIVER VIRUS: AN
EPIDEMIC FORECASTING MODEL
Citation:
Wenbiao Hu, Shilu Tong, Kerrie Mengersen and Brian Oldenburg. (2004). Rainfall,
mosquito density and transmission of Ross River virus: A time series analysis.
Submitted to Ecological Modelling.
Date submitted: April 2005
Contribution of authors:
WH was the principal author of the manuscript, performed all data analysis,
interpreted the results and wrote the manuscript. ST and KM assisted with
interpretation of the results and writing the manuscript. BO contributed to the
manuscript in terms of providing feedback on the analyses and initial drafts.
154
176
CHAPTER 8: MOSQUITO SPECIES AND THE TRANSMISSION OF ROSS RIVER VIRUS IN
BRISBANE, AUSTRALIA
Citation:
Wenbiao Hu, Shilu Tong, Kerrie Mengersen, Brian Oldenburg and Pat Dale. (2004).
Mosquito species and the transmission of Ross River virus in Brisbane, Australia. To
be submitted to Journal of Medical Entomology.
Contribution of authors:
WH was the principal author of the manuscript, performed all data analysis and wrote
the manuscript. ST and KM contributed to the development of analytical protocol,
interpretation of the results and wring of the manuscript. BO and PD contributed to
the manuscript in terms of providing feedback on the analyses and initial drafts.
177
194
CHAPTER 9: GENERAL DISCUSSION
9.1 INTRODUCTION
The connection and major features of the five manuscripts are shown in Figure 9.1.
Each manuscript has its own separate discussion section in which the findings in
relation to literature, an interpretation of contributing factors, the limitations of the
study and the implications for public health interventions have been separately and
specifically addressed. This chapter discusses the findings in the five manuscripts at a
general level.
9.2 SUBSTANTIVE DISCUSSION
The results of this study show that the incidence of RRV disease was spatially
variable in Brisbane. The variation in both spatial and temporal distribution of RRV
disease indicates a complex ecology that is unlikely to be explained by a single factor
or dominant transmission route across these different groupings. This study helps to
identify high-risk areas in space and time.
The results of the analysis on socio-ecological determinants of RRV indicate that
there was a remarkable variation in the spatial distribution of RRV incidence in
Brisbane. The RRV disease incidence in Brisbane was significantly associated with
SOI at a lag of 3 months, the proportion of people with lower levels of education, the
proportion of labour workers residing in the SLA and the vegetation density. In the 10
high-risk SLAs with mosquito monitoring stations, RRV disease incidence was
195
Manuscript I Manuscript II
Spatial patterns at SLA
High risk areas
Middle risk
areas
Temporal patterns at SLA
High incidence in autumn
Different disease ecologies
Low risk areas
Identify major determinants
Social Climate Vegetation Mosquito
Model I Model II
SOI Lower levels of
education Labour workers
Vegetation density
Mosquito density SOI
Human population density
Indigenous population Overseas visitors
196
Figure 9. 1 Framework of research results in this thesis
Manuscript III
Developing predictive
models Climate variables
Tem
perature
Rainfall
Relative h
um
idity
High tide
Model I Model II
SARIMA SARIMA with rainfall
Manuscript IV
Developing predictive
model
Model I for rainfall
Mosquito density
Rainfall
Model II for
mosquito density
Model III for rainfall and mosquito density
Manuscript V
PDL model
Identify major mosquito species
Oc. vig
ilax
Cu
. ann
uliro
stris
Threshold Threshold
52 72
Validation of model
197
associated with mosquito density, SOI at a lag of 3 months, human population
density, the proportion of residents of indigenous ancestry and the proportion of
overseas visitors. Hence, the spatial pattern of RRV disease in Brisbane is determined
by a combination of local ecological, socio-economic and environmental factors.
We endeavored to develop empirical models to forecast epidemics of RRV disease.
To our knowledge, this is the first attempt to develop epidemic forecasting models for
predicting RRV disease in a metropolitan area. Both the SARIMA model and the PDL
model developed in this study appeared to have a high degree of accuracy and
therefore may have implications in the planning of disease control and risk
management programs. The results of this study suggest rainfall directly affects
mosquito density which then impacts on RRV disease. Rainfall and mosquito density
appear to have played a significant role in the transmission of RRV disease in
Brisbane. These variables may be used to assist in forecasting outbreaks of RRV
disease in Brisbane.
SARIMA modelling is a useful tool for interpreting and applying surveillance data. It
has great potential to be used as a decision-support tool in MBDs. SARIMA models
often require long-term time series (eg., more than 50 months). PDL time series
regression models were used because rainfall and mosquito density can affect not only
RRV occurring in the same month, but in several subsequent months. The rationale
for the use of the PDL technique is that it increases the precision of the estimates.
However, it needs a powerful statistical software package, such as SAS, and a special
computer programme.
198
Finally, the study provides evidence that mosquito density is significantly associated
with RRV disease in Brisbane, Australia. The key species of the RRV disease were
Ochlerotatus vigilax and Culex annulirostris at a lag of 1 month and the threshold for
the occurrence of RRV cases was average monthly mosquito density of 72
Ochlerotatus vigilax and 52 Culex annulirostris per trap, respectively.
The overall findings of this study suggest that changes in climate and the environment
may have direct and indirect impacts on the transmission of RRV. Climate and
environmental factors can influence the length and efficiency of extrinsic incubation
of RRV as well as breeding, survival, longevity, dispersal, and many other aspects of
the biology of the vector and host. Additionally, these factors influence human
behaviour and demographics and may determine the likelihood of human exposure to
RRV (Tong 2004).
It’s important to understand and identify lag effects of environmental factors on the
RRV transmission. The incubation period in humans ranges from 5-21 days (Harley et
al. 2001), which may explain why the disease is transmitted rapidly once an outbreak
occurs. Extreme weather events (eg, heavy rainfalls) often trigger outbreaks of RRV
disease at a lag of one to two months (Mackenzie et al. 2000, Tong et al. 2002). Such
delays are consistent with the development of mosquitoes and the external period of
incubation of RRV within mosquitoes and incubation period of the virus in the host.
Such lags may assist disease control managers to plan effective public health
interventions in advance.
199
However, the research findings of this study may be influenced by alternative
scenarios which are considered below:
1) The quality of the notification data varies with time and space. It’s well
recognised that the increased incidence of RRV is partly due to the increased
awareness of this disease among medical practitioners and the general public.
Nevertheless, the impact of such a factor on the notified incidence of RRV is
unlikely to differ substantially with the spatial and temporal scales used in this
study (ie, monthly rates within one city).
2) Localities where people got infected and were notified don’t match. Although
some studies indicate that the geographical distribution of RRV cases reflects
fairly accurately the locations in which infections actually occur (Hawkes et al.
1985, Selden and Cameron 1996), differences may exist between these two
places, particularly in holiday seasons. However, a recent survey suggests that
the locations where RRV cases were notified matched well with those where
infections occurred in Brisbane (Quinn et al. 2004).
3) The ecology of RRV is complex and many factors (including virus, vector,
host and environmental conditions) are involved in its transmission cycles. For
example, higher rainfalls would initiate and support mosquito virus activity
and the infection in the vertebrate population. As the breeding cycles of the
primary vertebrate host (macropods) usually take more than 1 year to
complete, the pool of susceptible vertebrates in the following year would be
reduced, virus amplification would be minimal, and the probability of
incidence rate would also small. Conversely, in the year after an epidemic year,
the high level of vertebrate population immunity would be sufficient to reduce
the probability of successive epidemic years to very low levels, even if under
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suitable socio-environmental conditions (Woodruff et al. 2002). In general, the
biannual peaks of RRV disease may be mainly determined by host immunity
cycles. However, how host immunity cycles impact on lags and design of an
early warning system remains to be determined (Mackenzie et al. 2000, Muhar
et al. 2000, Harley et al. 2001).
4) The virus has been recorded from 42 species from 7 genera, and 10 of these
species transmit the virus under laboratory conditions (Ritchie et al. 1997,
Ryan et al. 1999). Major mosquito species associated with RRV transmission
and their roles in the transmission of RRV remain to be determined. Because
different vectors can be involved in different regional environments, the
ecology of the virus and epidemiology of outbreaks vary as environmental
factors determining vector abundance vary within and between regions and
seasons (Russell 2002). For example, Ae. vigilax is the principal vector of
RRV in coastal regions. Culex annulirostris as a major vector breeds in
freshwater habitats, especially in irrigated areas in inland areas (Kay 1979,
Russell 1994, Dale and Morris 1996). In this study, however, we found that
both Ae.vigilax and Culex annulirostris appeared to have played significant
roles in the RRV disease in Brisbane.
5) Some societal factors also influence the transmission of RRV. Changes in
agricultural practice such as building dams and irrigation systems have
created ideal larval habitats for selected specie of mosquitoes. Clearing forests
for agricultural use and urban development (near wetland) could increase the
potential for RRV transmission (Lindsay and Mackenzie 1996, Mackenzie et
al. 2000, Tong and Hu 2002). The increased human populations living in
intimate contact with increasingly high densities of mosquito populations (i.e.
201
around wetland and salt-marsh habitats) create ideal conditions for increased
RRV. Tourism and travel have also become important mechanisms for
facilitating the RRV and its vectors. For example, the introduction of RRV to
the South Pacific in 1979 in a viraemic human led to the largest RRV
epidemic to date (Aaskov et al. 1981a).
Despite many socio-ecological factors that may affect the patterns of RRV disease,
the findings of this study suggest that changes in environmental conditions are one of
the key determinants of RRV incidence. These findings may be used in the planning
of future public health interventions and risk management programs as illustrated in
the next section.
9.3 THE IMPLICATIONS OF THE STUDY
The findings from this study may have a variety of implications in the planning of
public health intervention. 1) GIS and spatial analytic approach developed through
this study may be used in the surveillance of RRV and other infectious diseases to
identify and monitor high-risk areas over different periods of time. 2) The findings of
this study suggest that the major determinants of the RRV disease may differ at the
city and local levels. Therefore, different public health strategies may need to be
developed in the disease control and risk management programs. For example, human
population density is a significant determinant of RRV incidence in the high-risk
areas (but not across the city). Thus, health education and vector control programs
should focus on communities with a high population density when the disease season
comes. 3) systematic and integrated training may be necessary for medical
202
practitioners and public health professionals to achieve some knowledge of RRV
disease outbreak. 4) Computer models need to be developed on the basis of these
findings to predict possible epidemic activity under different socio-environmental
conditions, and as a means of predicting future consequences of socio-environmental
change. The development of epidemic forecasting systems is important in the control
and prevention of infectious disease outbreaks in the future. Should an outbreak of
RRV occur, a large-scale public health intervention is usually required. Early warning
based on forecasts from the model can assist in improving vector control and personal
protection. For example, increasing insecticide spraying during high-risk periods and
decreasing it during low-risk periods will improve cost-effectiveness of operations.
Disease control programmes, if anticipating an increase in RRV, can increase
vigilance, e.g., by alerting district health offices, filling vacant positions of health
staff, requesting more frequent reporting to facilitate early identification of problem
areas. 5) The disease surveillance data can be integrated with social, biological and
environmental databases. These data may provide additional input into the
development of epidemic forecasting models. These attempts, if successful, may have
significant implications in environmental health decision-making and practices, and
may help health authorities determine public health priorities more wisely and use
resources more effectively and efficiently. 6) Spatio-temporal analysis and CART
techniques can be used to identify major vectors of RRV disease and vector
thresholds as well. They may have applications as a decision-supportive tool in
disease control and risk-management planning programs.
203
9.4 THE STRENGTHS AND LIMITATIONS OF THE STUDY
This research has five major strengths. Firstly, to our knowledge, this is the first eco-
epidemiologic study examining the spatial variation of RRV disease and its major
determinants at both the city and local area (ie., high-risk SLAs) levels. Secondly,
detailed information on socio-economic and ecological characteristics were
incorporated in the model. Thirdly, sophisticated time-series models were used in the
attempt to develop an epidemic forecasting system for the control and prevention of
RRV disease in metropolitan areas. Fourthly, both ARIMA and PDL models
developed in this study appeared to have a high degree of accuracy and may have
implications in the disease control and risk management planning. Finally, research
outcomes from this study may have important implications for public health decision-
making in the control and prevention of RRV infection.
There are several limitations to this research, which include possible confounding and
biases in the study. In general, measurement and information biases are possible in
ecological studies. Other potential risk factors which were not measured such as
social variables may also have impacted on the transmission of vector-borne diseases.
Specific considerations of these issues are discussed below:
9.4.1 Possible bias
9.4.1.1. Information bias
Although the disease surveillance system in Australia generally operates well,
information bias is still possible in the process of notification. The failure to report
204
cases is an important source of information bias. A case that occurred “elsewhere” and
was neglected is another source of failure of reporting. For instance, the case register
in Brisbane fails to include residents in Brisbane who develop the disease in Sydney
during their visits there. Lower reporting rates of the notifiable diseases due to various
reasons (e.g., the GPs are less familiar with the case diagnosis at the early stage of the
notifiable system) is potentially another source of information bias. On the other hand,
over-diagnosis might also be possible. For example, as indicted earlier, over-diagnosis
is likely to occur in endemic situations because an IgM response is usually based on a
single serum specimen, and it may represent past infection in a person who currently
has another disease (Mackenzie et al. 1998).
Different examiners during different observation periods and in different locations
might lead to different notification results. In addition, disease interventions, changes
in the requirements for disease reporting, and modifications to the surveillance system
might all impact on the quality of data, and then the internal validity of the study
(Rothman and Greenland 1998). As the RRV data in Brisbane were collected by
different staff in different locations, over different observation periods, bias is possible.
However, information bias of such kind is unlikely to have a significant impact on the
results of this study because the data quality is unlikely to change remarkably on the
monthly basis.
9.4.1.2. Selection bias
Selection bias is also possible as the notification data do not include asymptomatic
patients and people who have clinical symptoms but don’t seek a doctor. In addition,
205
the generalisability of the model developed in this study may be limited, because only
local data were used in the construction of the model.
9.4.2 Confounding
A number of potential confounders may affect the assessment of the relationship
between socio-ecological variability and the transmission of RRV infection. These
potential confounders include local health promotion expenditure, mosquito control
measures, population immunity and housing conditions, which might vary across
SLAs, or over time. These factors may impact on the incidence of RRV, but the data
on these factors were unavailable for most of the study period in Brisbane. In a small
SLA there could be an influence from natural habitats in adjacent SLAs. For example,
as boundaries between SLA are quite often defined by creeks, the associated
vegetation will influence the mosquito density on both sides of the border. Distance
from each individual case location to natural habitats should be considered in further
detailed research (Muhar et al. 2000)
9.5 RECOMMENDATIONS
9.5.1 Disease and vector surveillance and monitorin g
The current disease surveillance system must continue but its effectiveness may need
to be improved to increase the accuracy of the surveillance data (Figure 9.2). A
rigorous evaluation is required to examine the likelihood of under-reporting and over-
reporting of the disease. Additionally, vector surveillance and monitoring programs
206
need to be strengthened because it will provide not only forewarning of outbreaks of
the disease but also valuable information for public health decision-making.
9.5.2 Public health interventions
The effectiveness of public health interventions can be improved by: 1) using GIS and
spatial analysis to identify and monitor hot spots of RRV disease; 2) identifying major
vector(s) and the threshold of RRV disease, and then targetting education campaigns
and mosquito control activities at specific areas; 3) providing systematic and
integrated training for medical practitioners and public health professionals; and 4)
collaborating among epidemiologists, public health physicians, microbiologists,
ecologists and environmental health practitioners to assess major determinants of
RRV transmission.
9.5.3 Community health education
Community participation and health education can be an important approach to
reduce the transmission of RRV diseases (Figure 9.2). Regular health promotion
campaigns should be performed before the beginning of each epidemic season:
managing mosquito breeding sites from around communities; keeping swimming
pools full and well maintained; screening living areas, and using mosquito bed-nets to
keep out mosquitoes; using insect repellents in areas where mosquitoes are active;
wearing loose light-coloured long-sleeved shirts and long trousers, socks and covered
footwear to prevent being bitten by mosquitoes.
207
9.5.4 Evaluation of vector control program
Vector control may be an effective and economic approach to reduce the transmission
of RRV and other vector-borne diseases. Ochlerotatus vigilax and Culex annulirostris
are two major mosquito species of RRV disease in Brisbane. Public health authorities
need to pay more attention to the monitoring and control of these mosquito species.
The evaluation of the vector control strategies is necessary to examine the
effectiveness and feasibility of vector control measures. Also combining the vector
control strategies and improved city planning and development is important because
the latter is of growing importance as people seek life-style changes and urban spread
impinges on wetlands.
9.5.5 Direction for future research
To better understand the natural transmission of RRV infection, a geographical
epidemiological research is needed in Australia and some island nations (e.g, Papua
New Guinea and Fiji). It could include research on the distribution of vectors and
their movement; sero-epidemiological investigation; and the virus carrier rate of
vectors. Due to limited geographic distribution of RRV (ie, Australia and a few
Pacific island nations) and the high costs required for vaccine development and
licensing, it may be more effective economically to increase research into vector
control strategies and into improved town planning and urban development (Tong et
al. 2001). We also need much more research on the environmental and behavioural
determinants of infection. Such work could yield evidence for public health measures
to reduce the RRV incidence by informing the public to eliminate or reduce mosquito
208
breeding sites, or through behavioural changes to reduce the chances of being bitten
by vector mosquitoes.
Finally, there is a need for further research into the complex ecology of the virus,
epidemiology of the disese and uncertainties associated with the impact of predicted
socio-environmental factors, because the improved understanding of the determinants
of RRV disease is important for the development of epidemic forecasting system.
9.5.6 Preliminary development of an epidemic foreca sting model for RRV control and prevention
Due to the limitations mentioned above, the results from this study should be
interpreted with caution. Computer models need to be developed on the basis of in-
depth research to predict possible epidemic activity under different environmental
conditions, and as a means of predicting future consequences of environmental
change (Russell, 1998b; Wilson, 1995). Therefore, more research is certainly needed
in this important public health field.
A toolkit should be developed for the display and modelling of spatial data.
Recommendations were made on suitable modelling methods and processes for
analysing spatio-temporal data. The preliminary epidemic early warning systems were
developed using these technologies.
The toolkit should include the following components: a) GIS function to store,
retrieve and display the spatio-temporal distribution of RRV cases by SLA; b) a
process to categorise SLAs at high, medium and low risk based on socio-ecological
209
factors; c) a spatial and temporal analytic model (computer model) using computer
programme; and d) the application of GIS and spatio-temporal models to predict
where and when RRV will occur, and display their spatial distribution using a spatio-
temporal model.
The following approaches need to be used to achieve these goals:
1) Improved understanding of the ecology of RRV disease such as the inter-
relationships between virus, vector, host and socio-environmental changes;
2) Improving the quality of RRV surveillance data through medical training and
community education;
3) Combining RRV disease surveillance data with socio-demographic and
ecological data (eg, weather, tides, mosquito density and vector control
programs) on a regular basis;
4) Build up the digital base map data sets used for the construction of the GIS
and administrative boundaries in Brisbane. The digital base map data should
be manipulated to facilitate the accurate identification of the spatial locations
of SLAs and their linkages with onset places of notified RRV infections with
socio-ecological data layers.
5) Develop the SARIMA and PDL models to assess the independent effects of
individual socio-ecological variables on the transmission of RRV.
6) Integrate the above steps and then provide the user with direct and easy
methods to manipulate the complex spatio-temporal models in order to predict
SLAs at high risk, based on socio-ecological factors.
210
Figure 9. 2 Framework of research recommendations in this thesis
Recommendations
Disease and vector
surveillance and monitoring
Com
munity health education
Evaluation of vector control
program
Direction for future resea
rch
Epidem
ic forecasting m
odel
Continue Health education
Effective Economic
Environmental Behavioural
Software package
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APPENDIX
DATA COLLECTION
SUPPLY OF DIGITAL DATA
CONSULTANTS AGREEMENT
Brisbane City Council
iDivision GIS Services Level 20, 69 Ann Street
Brisbane QLD 4000
Contact Peter Lefel
Telephone + 61 7 3403 6713
Facsimile + 61 7 3403 5103
BCC REF : BM0 Our Business - A Better Brisbane
26th November, 2003 Queensland University of Technology Centre for Health Research - Public Health Victoria Park Road Kelvin Grove 4059 Attn: Wenbiao Hu Re: TERMS AND CONDITIONS FOR THE SUPPLY OF DIGITAL DATA TO CONSULTANTS This agreement dated this 26th day of November, 2003 between the Brisbane City Council and Queensland University of Technology Centre for Health Research - Public Health of Victoria Park Road, Kelvin Grove covers the supply, in MapInfo format(s), of the following digital licensed data covering the City of Brisbane. 1.0 LICENSED DATA SPECIFICATION 1.1 Brisbane City Council Data Vegetation Data of the following years: 1991, 1993, 1995, 1997 and 2001. 1.2 NRM Digital Cadastral Data n/a
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2.0 DEFINITIONS (a) Cadastral means relating to titles or registered interests over land or water, or to the alienation, leasing or occupation of State lands or mining areas or roads, and includes reference to the boundaries of the land, area or road. (b) Consultant means any consultant, contractor or business partner of the Licensor engaged for a specific project for the Licensor. (c) Brisbane City Council Data means any data owned by the Brisbane City Council. It includes data that has been reformatted or converted on to a different media or translated into another format. (d) End User means any corporation, organisation or person who receives or accesses for payment or otherwise Licensed Data or Licensed Data Products. (e) Intellectual Property Rights means all copyright, patent application rights, patent rights, design rights, database rights, trade mark rights (whether registered or unregistered), trade secrets and confidential information, all know-how, and all other rights of intellectual property. (f) Licence means the non-exclusive, non-transferable licence granted by the Licensor to the Licensee pursuant to this agreement. (g) Licensor means the Brisbane City Council (h) Licensed Data means all data which is identified in section 1.0 of this agreement (Licensed Data Specification). It represents data that is currently available to the Brisbane City Council at the date of issue. It includes data that has been reformatted or converted on to a different media or translated into another format. (i) Licensed Data Product means any value added product derived from or based on the Licensed Data or any Licensed Data Product. (j) NRM means the Department of Natural Resources and Mines (formerly known as the Department of Natural Resources) (k) NRM Digital Cadastral Data means that information relating to the Cadastral land parcels of the State which is extracted from the DCDB and supplied to the Brisbane City Council under license agreement. It includes data that has been reformatted or converted on to a different media or translated into another format. Under this agreement, you (the licensee) undertake to comply with the following conditions. 3.0 FEES PAYABLE NIL Please Note: This is NOT an invoice.
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4.0 PERMITTED USE 4.1 The Consultants use of the Licensed Data or Licensed Data Product shall be limited solely to its own personal use and use by licensed subcontractors for a PhD project that focuses on the application of spatio-temporal analytic methods in the surveillance of Ross River virus (RRv) disease and tries to develop an epidemic forecasting system using environmental variables. Upon the completion of this PhD, Mr Hu will provide a copy of his project to Brisbane City Council and present the results of his study to a meeting of interested Council Officers. It shall not be made available to third parties (including any corporation, institution, organisation or person in any manner associated with the recipient), on-sell or distribute the data for reward to any other third party. 4.2 The Consultant shall ensure that any subcontractor(s) used on the specified project shall sign a license agreement which includes the terms outlined in this agreement. 4.3 The Consultant shall not purport to or grant rights to the Licensed Data or Licensed Data Product, in either hardcopy or electronic format to any other person or organisation. 4.4 The Consultant shall not use the Licensed Data or Licensed Data Products with the intention of encroaching on the privacy of an individual or company or other organisation. 4.5 The Consultant shall not change the coordinates of the Licensed Data 5.0 OWNERSHIP 5.1 The Consultant acknowledges that it has no rights of ownership in the Brisbane City Council Data whether in its original form or as reformatted or converted onto different media by the Licensee and all Intellectual Property Rights including copyright are retained by the Brisbane City Council. 5.2 The Consultant also acknowledges that it has no rights of ownership in the NRM Digital Cadastral Data whether in its original form or as reformatted or converted onto different media by the Licensee and all Intellectual Property Rights including copyright are retained by the State of Queensland (Department of Natural Resources and Mines). 6.0 LIABILITY 6.1 The Consultant shall indemnify the Licensor, its employees and agents from all liability and the Licensor, its employees and agents shall not be liable to the Licensee for any loss or damage suffered or incurred by the Licensee or any other party arising from or in relation to any error or inaccuracy to the Licensed Data or Licensed Data Products howsoever caused and whether by negligence or otherwise in or arising from or in relation to the performance or use of the Licensed Data or Licensed Data Products. 6.2 The Consultant acknowledges that the Brisbane City Council and the State of Queensland (Department of Natural Resources and Mines) does not guarantee the accuracy or completeness of the Licensed Data, and does not make any warranty about the licensed data. 6.3 The Consultant agrees that the Brisbane City Council and the State of Queensland is not under any liability to the Licensee for any loss or damage (including consequential loss or damage) from any use of the Licensed Data. 6.4 The terms of this agreement may be pleaded as a bar to any claim or action brought by the Licensee against the Licensor.
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7.0 ACKNOWLEDGEMENT The Consultant agrees to acknowledge the source of the Licensed Data by including on any map and/or report the following words “Information supplied by Bimap, Brisbane City Council under copyright”. 8.0 CONFIDENTIALITY 8.1 The Consultant agrees that the Licensed Data is valuable commercial information of the Brisbane City Council and The State of Queensland (through the Department of Natural Resources and Mines). 8.2 The Consultant agrees to disclose Licensed Data or Licensed Data Products only to such of its employees and servants who need to know it for the purpose of the Consultant exercising its obligations under this agreement. 8.3 The Consultant shall take all reasonable steps to maintain and safeguard the confidentiality of the Licensed Data or Licensed Data Product and to ensure that its employees and servants maintain the confidentiality of the Licensed Data or Licensed Data Product and use the Data solely for the purposes permitted under this agreement. 9.0 DISCLAIMER The Licensee acknowledges that while every endeavour has been made to ensure that the material here produced is accurate in what it conveys, the Licensor takes no responsibility for any errors or omissions therein or for any acts that may occur due to its use and the Licensee specifically acknowledges and accepts such condition. Brisbane City Council does not warrant the correctness or completeness of the Licensed Data. It is the responsibility of the Licensee at all times to ensure that such parts of the Licensed Data used by it are correct by means of independent verification before any reliance is placed on it, with reference to City Plan Classifications, by application to the Brisbane City Council for Planning and Development Certificates. 10.0 LICENCEE TO INCLUDE DISCLAIMER The Licensee shall include the following disclaimer on all copies of all Licensed Data and Licensed Data Products transacted by the Licensee : “While every care is taken by Brisbane City Council (BCC) and the Department of Natural Resources and Mines (NRM) to ensure the accuracy of this data supplied by BCC and NRM, BCC and NRM jointly and severally make no representations or warranties about its accuracy, reliability, completeness or suitability for any particular purpose and disclaim all responsibility and all liability (including without limitation, liability in negligence) for all expenses, losses, damages (including indirect or consequential damage) and costs which may be incurred as a result of data being inaccurate or incomplete in any way and for any reason. Based on Data provided with the permission of the Department of Natural Resources and Mines: Cadastral Data (month / Year)”.
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Signatories Dated this________________ day of ___________________________, 200_
Licensee Signed for and on behalf of __________________________________ ______________________________ (print name of delegate) (signature) (Queensland University of Technology Centre for Health Research - Public Health) In the presence of _________________________________________ _______________________________ (insert name ) (signature)
Licensor Signed for and on behalf of ______ROBERT PETERS________ _____________________________ (print name of delegate) (Brisbane City Council) (signature) ABN 72 002 765 795 In the presence of __________________________________________ _____________________________ (insert name ) (signature)
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Digital Geospatial Data Transfer MetaData Sheet
Brisbane City Council
iDivision GIS Services
69 Ann Street
Brisbane QLD 4000
Telephone + 61 7 340 36713
Facsimile + 61 7 340 35103
BIMAP REF : BM0
Our Business - A Better Brisbane METADATA DETAILS : DATA SET : Queensland University of Technology - Centre for Health Research - Public Health - Wenbiao Hu - Mapinfo format data sets of the following Vegetation year sets; 1991, 1993, 1995, 1997 and 2001.
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DATA DESCRIPTION : 1. Vegetation 2001 - Information in this category represents Natural Vegetation cover including forest, woodland and shrubland communities, and saline and freshwater wetland communities, along waterways and regrowth communities. This theme was captured from a digital imagery acquired at various times and may have special significance for one or more of the following reasons: • contribute importantly to flora & fauna habitats • form an important link in the natural areas network in/next to Brisbane • contribute significantly to Brisbane’s landscape character/visual amenity • be subject to a VPO &/or Voluntary Conservation Agreement. Mapping may preclude narrow strips of vegetation along creek-lines and does not include remnant areas less than 1.2 hectares in overall size, and environments where vegetation has been greatly modified for urban development, agriculture, extractive industries etc. 2. Vegetation 1997 - Information in this category represents Natural Vegetation cover including: forest, woodland and shrubland communities, saline and freshwater wetland communities, along waterways and regrowth communities. This theme was captured from aerial photography flown in January 1997 and may have special significance for one or more of the following reasons: contribute importantly to flora & fauna habitats; form an important link in the natural areas network in/next to Brisbane: contribute significantly to Brisbane’s landscape character/visual amenity; and be subject to a VPO &/or Voluntary Conservation Agreement. Mapping may preclude narrow strips of vegetation along creeklines and does not include remnant areas less than two hectares in size, and environments where vegetation has been greatly modified for urban development, agriculture, extractive industries etc. The following are descriptions of the community types represented on this theme: bZ3b Banksia robur, Melaleuca linariifolia, Leptospermum polygalifolium ssp. cismontanum shrubland cM3a Casuarina glauca open forest cM3a.1 Casuarina glauca- Melaleuca quinquenervia + eucalypt species open forest cS3a Allocasuarina littoralis - Banksia integrifolia + emergent Eucalyptus spp. open scrub cS3b Casuarina glauca open scrub eL3a.1 Eucalyptus signata-E. seeana-Casuarina littoralis-Acacia spp-low open forest eM2b Eucalyptus crebra - E. tereticornis woodland eM2i Eucalyptus robusta - E. tereticornis - Lophostemon suaveolens woodland eM2j Eucalyptus signata - Eucalyptus intermedia woodland eM2k Eucalyptus signata + other Eucalyptus spp. woodland eM2k.1 Eucalyptus signata + other eucalyptus spp. heath understorey woodland eM2o Mixed Eucalyptus spp. woodland eM2p Eucalyptus tereticornis woodland eM2p.1 Eucalyptus tereticornis - Lophostemon suaveolens - E. siderophloia woodland eM2p.3 Eucalyptus tereticornis mixed eucalypt + Melaleuca spp. woodland eM2q Eucalyptus seeana - E. major woodland em2r Eucalyptus seeana - mixed eucalypt and Melaleuca spp. woodland eM2s Eucalyptus propinqua => major + mixed species woodland eM3a Eucalyptus acmenoides - E. drepanophylla - other Eucalyptus spp. open forest. eM3ab.1 Eucalyptus planchoniana - E. baileyana + E. nigra open forest eM3ae Eucalyptus tereticornis - E. crebra/siderophloia - Lophostemon confertus/suaveolens open forest eM3af Eucalyptus maculata - E.carnea/ E. acmenoides - E. crebra/ E. siderophloia open forest eM3af.1 Eucalyptus carnea/E. trachyphloia - E. drepanophylla open forest eM3ah Eucalyptus fibrosa - E. henryi open forest eM3ai Eucalyptus propinqua => major - E. nigra open forest eM3aj Eucalyptus propinqua => major - E. signata + other Eucalyptus spp. open forest eM3ak Eucalyptus propinqua => major + other eucalypt spp. open forest
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eM3b Eucalyptus acmenoides - E. propinqua open forest eM3j Eucalyptus intermedia - E. acmenoides and/or E. microcorys open forest. eM3k Eucalyptus intermedia- Eucalyptus microcorys - Lophostemon confertus open forest eM3na Eucalyptus maculata - E. tereticornis - E. moluccana + other eucalypt spp. open forest eM3o Eucalyptus moluccana open forest eM3p Eucalyptus nigra - E. resinifera open forest eM3p.1 Eucalyptus nigra + other Eucalyptus spp. open forest. eM3u Eucalyptus signata - E. intermedia open forest eM3w Eucalyptus siderphloia &/ or E. fibrosa subsp. fibrosa open forest eM3z Eucalyptus tereticornis - E. drepanophylla open forest eM3z.a Eucalyptus tereticornis - E. moluccana open forest eM3nr E. tereticornis - E. moluccana + other eucalypts and vine forest affinity species open forest eT3c Eucalyptus grandis or Eucalyptus saligna tall open forest mM2a Melaleuca quinquenervia woodland mM2b Melaleuca quinquenervia - Lophostemon suaveolens + other Eucalyptus spp.woodland mM2d Melaleuca quinquenervia - E. tereticornis - Eucalyptus spp. woodland mM3a Melaleuca quinquenervia open forest mM3b Melaleuca quinquenervia - Eucalyptus robusta open forest mM3c Melaleuca quinquenervia - Eucalyptus tereticornis - Lophostemon suaveolens open Forest mM3d Mixed Melaleuca quinquenervia open forest mS3b Melaleuca nodosa open scrub oM3a Waterhousia floribunda - Casuarina cunninghamiana - Cinnamomum camphora open forest oM3b Waterhousia floribunda open forest with emergent Eucalyptus grandis and/or Lophostemon confertus oM3c Waterhousia floribunda - Melaleuca quinquenervia + other riparian vegetation open forest oM3d Glochidion sumatranum - G. ferdinandi + other species open forest tM3a Lophostemon confertus open forest tM3b Lophostemon confertus - Eucalyptus intermedia open forest tM3c Lophostemon confertus + eucalyptus + vine forest species open forest vM4a Argyrodendron trifoliolatum - Pseudoweinmannia lachnocarpa closed-forest (Araucarian notophyll vine forest) vM4d Closed-forest altered in structure and composition by logging wS3a Acacia spp. - Allocasuarina littoralis + emergent eucalyptus open scrub zDod.1 Sections of continuous vegetation native + exotic species forming a canopy or shade area + sections of para grass zDod.2 Scattered remnant riparian vegetation of one or more trees generally overgrown with para grass + exotic and endemic species lining the banks zEoa Ephemeral wetlands (Freshwater) dominated by native vegetation zEob Ephemeral wetlands (Saline) zFoa.1 Mangroves zFoa.2 Saltmarsh, littoral marsh, closed-grassland and mudflats zWoa Freshwater bodies with areas of aquatic vegetation. 3. Vegetation 1995 - See Vegetation 1997 above and accompanying documentation. 4. Vegetation 1993 - See Vegetation 1997 above and accompanying documentation. 5. Vegetation 1991 - See Vegetation 1997 above and accompanying documentation.
DATA OWNER : BCC, BIMAP CONTACT NAME: Peter Lefel CONTACT POSITION : Senior GIS Officer ORGANISATION : Brisbane City Council MAIL ADDRESS : G.P.O. Box 1434 Brisbane QLD 4001 TELEPHONE : (07) 340 36713 FACSIMILE : (07) 340 35103
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DATA SOURCE : 1. VEGETATION 2001:
External vegetation boundaries were derived from:
3m resolution Daedalus Airborne Thematic Mapper (ATM) 12 band Multispectral Digital Imagery flown April /
May 1999
· 30m Landsat TM satellite imagery acquired September 1997 and May 2001.
(The Landsat TM data was used to provide a change in vegetation extents “Vegetation Loss” which was then applied to
the 3m Daedalus ATM data).
Internal vegetation community boundaries were derived from:
1997 Vegetation mapping.
30m Landsat TM 2001 satellite imagery acquired May 2001
Vegetation community classifications were provided by:
1997 Vegetation mapping.
1999 Wetlands mapping.
Numerous Reports and Studies carried out for / by Environment and Parks.
Field inspection sites surveys carried out by City Design.
Air Photo Interpretation flown March 1999 and January 2001.
Data was mapped by officers of the iDivision GIS Support and Environment and Parks Teams.
2. Vegetation 1997 - The data was captured from 1:30 000 aerial photographs flown in January 1997. Data was mapped
by officers of the Natural Environment Program and digitised by River City Technology.
2. Vegetation 1995 - The data was captured from 1:30 000 aerial photographs flown in 1995. Data was mapped by
officers of the Natural Environment Program and digitised by River City Technology.
2. Vegetation 1993 - The data was captured from 1:30 000 aerial photographs flown in 1993. Data was mapped by
officers of the Natural Environment Program and digitised by River City Technology.
2. Vegetation 1991 - The data was captured from 1:30 000 aerial photographs flown in 1991. Data was mapped by
officers of the Natural Environment Program and digitised by River City Technology.
POSITIONAL ACCURRACY : not determined - see data source NATIVE FORMAT : Microstation Design Files, MapInfo CURRENCY DATE: 1991/01/01
RESTRICTIONS : See attached Licence Agreement
EXTRACT DETAILS : OFFICER'S NAME : Peter Lefel DATE OF EXTRACT : 2003/11/26 BIMAP REF NUM : BM0 AREA COVERAGE OF EXTRACT : NUMBER OF RECORDS/ELEMENTS : DATA FORMAT SUPPLIED : MapInfo FILE SIZE (uncomp) : (Mb) (comp) : (Mb) ATTACHMENTS : Licence Agreement
COMMENTS : See attached Licence Agreement
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Date: Mon, 08 Apr 2002 17:02:31 +1000 From: Judy Kroll <[email protected]> Subject: Re: Meteorological data X-Sender: judyk@postoffice To: Wenbiao Hu <[email protected]> X-Mailer: QUALCOMM Windows Eudora Pro Version 3.0.5 (32) Dear Wenbiao Attached is a compressed file containing daily met observations for the stations you requested. Each .csv data file is accompanied by a .mdt station information file. The .csv files are readily opened in Excel by nominating comma separated data. $60 has been charged to the Visa Card supplied and copies of the transaction and tax receipt forwarded by mail. Regards Judy wenbiao.zip Judith Kroll Climate & Consultative Services Section Bureau of Meteorology Postal Address: GPO BOX 413 BRISBANE QLD 4001 Ph (07) 3239 8665 Fax (07) 3239 8679 Email [email protected] ============================================================================= Sophos Anti-Virus ver3.56 was used to scan the attachment(s) to this email message. The attachment(s) was/were found to be free of known viruses. QUT VIRUS TEAM http://www.its.qut.edu.au/info-sources/virusteam
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From: <[email protected]> X-Lotus-FromDomain: QDOT To: [email protected] Date: Wed, 11 Dec 2002 11:34:11 +1000 Subject: Tidal Readings - Qld Ports Queensland Transport [Maritime Safety Queensland] E-mail Message Mineral House 41 George St. GPO Box 2595 Brisbane 4001 From: G J (John) Broadbent Telephone: (07) 3224 8802 Facsimile: (07) 3404 3089 E-mail : [email protected] Internet http://www.transport.qld.gov.au/qldtides To Mr W Hu : QUT [email protected] Subject: Tidal Readings - Qld Ports Your Reference: Our Reference: 665/6 Part 8 Date: Wednesday, 11 December 2002 Message I refer to your email message of 3 Decemeber requesting a copy of the high and low tidal recordings from the Cairns, Townsville, Mackay, Gladstone, Bundaberg and Brisbane tidal stations for the period 1985 to 2001. A copy of the following data is provided herewith. File Name Details D056012A.85W Cairns Observed High &Low tides for 01/01/1985 to 31/12/2001 D055003A.85W Townsville Observed High &Low tides for 01/01/1985 to 31/12/2001 D054004A.87W Mackay Observed High &Low tides for 06/11/1987 to 31/12/2001 D052027A.85W Gladstone Observed High &Low tides for 1/01/1985 to 16/12/1999 D056027A.00W Gladstone Observed High &Low tides for 17/07/2000 to 31/12/2001 D051011A.85W Bundaberg Observed High &Low tides for 01/01/1985 to 31/12/2001 D046046A.85W Brsbane Observed High &Low tides for 01/01/1985 to 31/12/2001 Please be aware given the precision of the tidal recordings provided a span of 15 years would not be sufficient to discern a reliable indication of the sea level response to any change in climate that may be occuring. A copy of the file format is attached The release is subject to the following conditions: 1. Data are used for your present study only. 2. That the data provided is not released to a third party without the prior written approval of Queensland Transport. 3. Queensland Transport is acknowledged where appropriate. Datum for the observed tide heights is Lowest Astonomical Tide (LAT) datum. .
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The times are referred to the Australian Eastern Standard Time. The LAT datum is1.643m below the Australian Height Datum (AHD) at Cairns. The LAT datum is1.856m below the Australian Height Datum (AHD) at Townsville The LAT datum is2.941m below the Australian Height Datum (AHD) at Mackay. The LAT datum is2.268m below the Australian Height Datum (AHD) at Gladstone. The LAT datum is1.693m below the Australian Height Datum (AHD) at Bundaberg. The LAT datum is1.243m below the Australian Height Datum (AHD) at Brisbane. (G J Broadbent) Senior Maritime Officer (See attached file: D056012a.85w)(See attached file: D055003a.85w) (See attached file: D054004a.87w)(See attached file: D052027a.85W)(See attached file: D052027a.00w)(See attached file: D051011a.85W)(See attached file: D046046a.85w) Tidal Unit - Queensland Department of Transport FILE FORMAT:- HILO Record 1 Station Type, Data Type, number of days on file, scale factor, datum shift, start time, interval, geographic quadrant, latitude degrees, latitude minutes, longitude degrees, longitude minutes, time zone, data owner (supplier of predictions), confidentiality code Format a2,a1,i5.5,2x,i5.5,6x,i5.5,2x,i4.4,2x,i4,1x,i1, 1x,i3,1x,i2,1x,i4,1x,i2,1x,a5,1x,a5,1x,a1 Record 2 Station number, station name, analysis period Format a7,2x,a40,a22 Record 3 Gauge datum, TG Benchmark description, Benchmark height, Metric or Imperial Format 12x,a24,6x,a15,f7.3,a1 Record 4 File datum, datum shift, metric or imperial, above or below datum, prediction source (ifapplicable) Format 22x,a7,9x,f6.3,a1,1x,a5,16x,(a12 if applicable) SUBSEQUENT RECORDS Flag1, flag2, program, number of tides, date(ddmmyyyy), and up to 6 tides [hilo indicator, time(hhmm), height(mm)] [In the case of Stream Stations 3 tides, hilo indicator, time(hhmm), height(mm)] Format a1,a1,a6,i2,1x,i2,i2,i4,6(i2,2(i2.2),i5.4) In the case of an heights station (Station Type Code HT) there is a single reading for each time. In the case of a streams station (Station Type Code SV or SC) the readings are in pairs for each time. The Data Type Code indicates the nature of the reading. FILE FORMAT EXAMPLE Predicted Readings HTP00006 10000 00000 0000 0 4 -21 16 149 18 1000E BPA C 060002A HAY POINT STORM SURGE 01/01/1992 05/02/1992 GAUGE DATUM LOW WATER DATUM :TGBM PSM 38627 17.660M ABOVE GD FILE HEIGHTS REFER TO LWD WHICH IS 0.000M ABOVE GAUGE DATUM CN C060002A.94A PRED 4 01011992 10612 3720-11233 1250 11756 4270-12343 0890 PRED 3 02011992 10658 3780-11321 1310 11846 4230 PRED 4 03011992-10026 0450 10739 3820-11401 0150 11928 4300 PRED 4 05011992-10140 0780 10848 4050-11509 0200 12040 4850
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FILE FORMAT EXAMPLE Actual Readings HTO00006 10000 00000 0000 0 4 -21 16 149 18 1000E BPA C 060002A HAY POINT STORM SURGE 25/04/1992 29/04/1992 GAUGE DATUM LOW WATER DATUM :TGBM PSM 38627 17.660M ABOVE GD FILE HEIGHTS REFER TO LWD WHICH IS 0.000M ABOVE GAUGE DATUM G HILO 2 25041992 11155 1420-11800 0670 HILO 4 26041992 10047 1940-10749 0820 11253 1340-11846 0710 EHILO 6 27041992-10257 1040 10601 1990-10956 1000 11208 1600-11410 1190 11741 2O20 HILO 1 27041992-12112 0990 G HILO 0 28041992 XHILO 0 29041992 Note on Codes: Program = PRED Indicates the file contains predicted tidal heights Program = HILO Indicates the file contains observed tidal heights Tidetype = 1 Indicates a High Tide Tidetype = -1 Indicates a Low tide Flag1 = G Indicates missing heights in tidal observations Flag2 = E Indicates more then six tides per day Flag2 = X Indicates that observed data may have spurious heights Station Type Code = HT Height Recording Station = SV Streams Recording Station (Vector form - Speed, Direction) = SC Streams Recording Station (Co-ordinate form - North, East) Data Type Code = P Predicted Readings = O Actual Readings = C Tidal Constituents Important Notice Confidentiality and Legal Privilege This e-mail message is intended only for the addressee and may contain legally privileged and confidential information. If you are not the addressee you are notified that the transmission, distribution, or photocopying of this e-mail is strictly prohibited. Thelegal privilege and confidentiality attached to this e-mail is not waived, lost or destroyed by reason of a mistaken delivery to you. If you have received this e-mail in error please immediately notify me by telephone and destroy the original and any copies that you may have. ************************************************************ Opinions contained in this e-mail do not necessarily reflect the opinions of the Queensland Department of Main Roads, Queensland Transport or National Transport Secretariat, or endorsed organisations utilising the same infrastructure. If you have received this electronic mail message in error, please immediately notify the sender and delete the message from your computer. ************************************************************ D056012a.85w
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X-Mailer: Novell GroupWise Internet Agent 6.0.3 Date: Tue, 10 Feb 2004 17:54:12 +1000 From: "Mike Muller" <[email protected]> To: <[email protected]> Cc: <[email protected]> Subject: Re: Mosquito density X-Junkmail-Status: score=8/50, host=mail-msgstore01.qut.edu.au Wenbiao - Finally, I have extracted some data to send you. With all the operational pressure we are under recently, especially following the storms, we do not have time to get these into graphical results at the moment, so I thought I should just send them to you in "raw" form and let you see what you can make of them. When you open these files, you will get a message about Macros, which probably refers to other spreadsheets on our server. I suggest you click on Disable Macros. You will then get a second pop-up message about a formula and circular references. I understand that you need to click on Cancel. The spreadsheet will then be there for you to play with. Anyway, maybe you are more familiar with manipulating Excel files than I am! I cannot guarantee that there are no errors in layout, but if there are, I hope you can get around them. Remember that we started in 1998 with 5 sites in the eastern suburbs, and added 5 more in the west later on. If you have any questions, please don't hesitate to get back to me. Kind regards, and sincere apologies for the delay. Mike Muller Medical Entomologist Brisbane City Council Vegetation and Pest Services 145 Sydney Street New Farm QLD Australia 4005 Ph 3403 0157 Fx 3403 2950 Mob 0414 911 522
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