climate, climate change and vector-borne...
TRANSCRIPT
Climate, climate change and vector-borne diseases Dr Nick Ogden, Zoonoses Division, Public Health Agency of Canada
Talk Outline
• Introduction to climate change and vector-borne disease (VBD)
• Climate, climate change and VBD: The biology
• Climate, climate change and VBD: The socio-economics
• Mathematical and simulation models
• Ecological niche models: statistical and other “pattern matching” methods
• Examples
• Summary
2
INTRODUCTION CC & VBD
3
4
Climate change drives emergence/re-emergence of infectious diseases
1. Human awareness (Lyme, SARS)
2. Introduction of exotic pathogens/vectors into existing suitable host/vector/human-contact
ecosystem (SARS, West Nile)
3. Geographic spread from neighbouring endemic areas (Lyme, Rabies)
4. Ecological/environmental change causing endemic disease to increase in
abundance/transmission and (for zoonoses) ‘spill-over’ into humans (Hendra, Nipah,
Hantavirus, RVF)
5. True ‘emergence’: evolution and fixation of new, pathogenic genetic variants of previously
benign microorganisms (Pathogenic Zoonotic Influenzas)
5 VBD types and drivers for their occurrence and emergence/re-emergence • Vector-borne diseases comprise two types:
» Human-vector-human transmitted VBDs » Animal-vector-human transmitted VBDs = vector-borne zoonoses
• Most emerging infectious diseases are zoonoses • Diverse range of VBD
» Fly-borne (mosquitoes): Malaria, dengue, onchocerciasis » Tick-borne: Lyme, Anaplasma, CCHF, RMSF » Flea-borne: Bartonella, Plague » Louse-borne: Relapsing fevers, Typhus
• Climate change is only one potential driver: » Habitat » Hydrology » Landuse » Agriculture » Urbanisation » Globalisation
Source: Thehero
Information public health needs from models
6
Where?
Who?
When?
How many?
How much? $
Eggs
QuestingAdults
QuestingLarvae
FeedingLarvae
EngorgedLarvae
EngorgedAdults
FeedingNymphs
QuestingNymphs
Egg-layingAdults
POP (x)
PEP (q) L to N (s)
N to A (v)
EngorgedNymphs
No. eggsper adult (e)
Rodent Nos.
RodentNos.Temperature
Basic HFR:Immature ticks
Basic HFR:Adult ticks
Deer Nos.
λqa
µqeµql
µqn
µqa
µfl
µfn
µel
µelµel
λqn
λql
FeedingAdults
µfa
HardeningLarvae
µhl
z r
w
u
y
Eggs
QuestingAdults
QuestingLarvae
FeedingLarvae
EngorgedLarvae
EngorgedAdults
FeedingNymphs
QuestingNymphs
Egg-layingAdults
POP (x)POP (x)
PEP (q)PEP (q) L to N (s)L to N (s)
N to A (v)N to A (v)
EngorgedNymphs
No. eggsper adult (e)
Rodent Nos.Rodent Nos.
RodentNos.
RodentNos.TemperatureTemperature
Basic HFR:Immature ticks
Basic HFR:Adult ticks
Deer Nos.
λqa
µqeµql
µqn
µqa
µfl
µfn
µel
µelµel
λqn
λql
FeedingAdults
µfa
FeedingAdults
µfa
HardeningLarvae
µhl
zz rr
ww
u
yy
Who for? • General biological/epidemiological
principles: Public health policy • Risk, impact (BOI) cost-benefit
assessments: Public health policy • Targeting surveillance/interventions:
Public health programs
What methods? • Projection – where and when in the next
decade/century? • Prediction – where at the present? • Forecasting – where and when next
week/month?
7
Smaller spatial and temporal scales
Great complexity and parameterisation
Modelling objectives and process in the context of climate change
8
Current knowledge of
climate/weather influences on
VBD risk
Quantitative relationship
between climate/weather
and VBD risk
Model
Model/ algorithm
Model/ algorithm
Projected future risk GCM/RCM output
Forecasting risk to drive interventions
Weather data
Design surveillance to drive interventions Assessing future risk
Adaptation
Climate, climate change and VBD: The biology
Vector abundance • Temperature affects mortality rates • Temperature effects development rates/capacity for reproduction • Rainfall affects availability of areas for immature mosquito survival and development
y = 1300,1x-1,4278
R2 = 0,6582
0
50
100
150
200
250
0 5 10 15 20 25 30
Temperature
Days
to ov
iposit
ion
y = 34234x-2,2709
R2 = 0,8283
0
20
40
60
80
100
120
140
160
180
0 5 10 15 20 25 30
Temperature
Days
to ec
losion
y = 101179x-2,5468
R2 = 0,8833
0
50
100
150
200
250
0 5 10 15 20 25 30
Temperature
Days
to m
oult
y = 1595,8x-1,2082
R2 = 0,8268
0102030405060708090
100
0 5 10 15 20 25 30
Temperature
Days
to m
oult
I. scapularis development: Ogden et al. J. Med. Entomol. 2004 Tsetse mortality: Randolph
& Rogers Nature Rev Micro 2003
Vector activity • Temperature affects activity • Increased humidity increases activity • Heavy rainfall decreases activity
00.10.20.30.40.50.60.70.80.9
1
0 5 10 15 20 25 30
Temperature (oC)
Act
ivity
pro
porti
on ImmaturesAdults
Vail & Smith J. Med. Entomol. 1998 Ogden et al. Int. J. Parasitol. 2005
Extrinsic incubation period and vector survival
12
Temperature
Complex effects on VBD ecology: e.g. host communities, vector and host seasonality
• Vector-borne zoonoses are mostly maintained by wildlife: humans are irrelevant to their ecology
• Host communities indirectly affected by climate • Vector seasonality due to temperature effects on development and activity • Host demographic processes (reproduction, birth and mortality rates) are
seasonal and affected directly indirectly (via resource availability) by climate
In Quebec: White-footed mouse range expanding, Deer mouse range contracting
Simon et al. Evol Appl 2014
0
0.1
0.2
0.3
0.4
0.5
0.6
1 2 3 4 5 6 7 8 9 10 11 12
Month
Prop
ortio
n of
ann
ual n
umbe
r of t
icks
+
2050
Changing climate alters tick seasonality and affects pathogen transmission
Ogden et al., J. Theor Biol. 2008; Kurtenbach et al. Nature Rev. Microbiol. 2006
Climate, climate change and VBD: The socio-economics
Effect of climate change on VBD exotic to North America
• Internationally the VBDs most important for public health are human-vector-human transmitted diseases: malaria, dengue, chikungunya
• Climate change may theoretically increase transmission by effects on vectors and pathogen development in vectors (extrinsic incubation period)
• Practically:
» Climate has historically had only a small role in the occurrence of these diseases compared to -
» human-induced control efforts (habitat alteration, eradication, treatment, bed nets), which have often been the main drivers
• But climate change will impact capacity of developing countries to control VBDs
Climate change and the four horsemen of the apocalypse
16
Burke et al. 2009 PNAS
Famine
Climate Change
War Pestilence
Death in developing countries
Increased infected economic/refugee migration
Increased rates of immigration and import of exotic VBD into Canada
Malaria, Dengue, Chikungunya
Mathematical and simulation models
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Model 3: SIS
0102030405060708090
100
1 366 731 1096 1461 1826 2191 2556 2921
Days of simulation
Num
ber o
f hos
ts
SusceptibleInfected
Simulation models: doing the sums – putting together quantitative knowledge of the biology of VBD transmission cycles
0
0.5
1
1.5
2
2.5
1 2 3 4 5 6 7 8 9 10 11 12
Month
Num
ber o
f birt
hs p
er d
ay
P. leucopus Canada
P. leucopus southern NJ
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
1 2 3 4 5 6 7 8 9 10 11 12
Month
Num
ber o
f dea
ths p
er d
ay
P. leucopus Canada
P. leucopus southern NJ
Ogden et al. 2007 Parasitology
Reservoir host dynamics
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 10 20 30 40 50 60 70 80 90
Day
Prev
alen
ce o
f inf
ectio
n in
xen
odia
gnos
tic ti
cks
C3H mice infected with strain BL206
C3H mice infected with strain B348
P. leucopus mice infected with strain BL206
P. leucopus mice infected with strain B348
Hanincova et al. 2008 AEM
Host infection and transmission dynamics
y = 1300,1x-1,4278
R2 = 0,6582
0
50
100
150
200
250
0 5 10 15 20 25 30
Temperature
Days
to ov
ipositi
on
y = 34234x-2,2709
R2 = 0,8283
0
20
40
60
80
100
120
140
160
180
0 5 10 15 20 25 30
Temperature
Days
to ecl
osion
y = 101179x-2,5468
R2 = 0,8833
0
50
100
150
200
250
0 5 10 15 20 25 30
Temperature
Days
to mo
ult
y = 1595,8x-1,2082
R2 = 0,8268
0102030405060708090
100
0 5 10 15 20 25 30
Temperature
Days
to mo
ult
I. scapularis development: Ogden et al. J. Med. Entomol. 2004
Vector biology
Climate drivers
Ravel et al. Int J Hygiene Env Health 2004
Agriculture dynamics
Levels of complexity
• Indices of climatic limits for survival using laboratory or field-obtained data:
• Simple mathematical models:
• Complex simulation models
19
)ln)((
2
0 phrHpNaR
nVIIV
−+= −− ββ
Uninfected rodents
Acutely-infected rodents
Carrier rodents
Juvenile females Subadult females
Pregnant females
Litter-bearing femalesJuvenile males Subadult males Adult males
Adult females
Juvenile females Subadult females
Pregnant females
Litter-bearing femalesJuvenile males Subadult males Adult males
Adult females
Juvenile females Subadult females
Pregnant females
Litter-bearing femalesJuvenile males Subadult males Adult males
Adult females
Uninfected rodents
Acutely-infected rodents
Carrier rodents
Juvenile females Subadult females
Pregnant females
Litter-bearing femalesJuvenile males Subadult males Adult males
Adult femalesJuvenile females Subadult females
Pregnant females
Litter-bearing femalesJuvenile males Subadult males Adult males
Adult females
Juvenile females Subadult females
Pregnant females
Litter-bearing femalesJuvenile males Subadult males Adult males
Adult femalesJuvenile females Subadult females
Pregnant females
Litter-bearing femalesJuvenile males Subadult males Adult males
Adult females
Juvenile females Subadult females
Pregnant females
Litter-bearing femalesJuvenile males Subadult males Adult males
Adult femalesJuvenile females Subadult females
Pregnant females
Litter-bearing femalesJuvenile males Subadult males Adult males
Adult females
Eggs
Questing Adults
Questing Larvae
Larvae feedingon infected rodents
Infected engorged larvae
Engorged Adults
Infected nymphs feedingon rodents
Infected questing nymphs
Egg-laying Adults
Engorged NymphsFeeding
Adults
Hardening Larvae
Larvae feedingon uninfected rodents
Larvae feeding on deer
Uninfected engorged larvae
Uninfected questing nymphs
Nymphs feedingon deer
Uninfected nymphs feeding on rodents
Eggs
Questing Adults
Questing Larvae
Larvae feedingon infected rodents
Infected engorged larvae
Engorged Adults
Infected nymphs feedingon rodents
Infected questing nymphs
Egg-laying Adults
Engorged NymphsFeeding
Adults
Hardening Larvae
Larvae feedingon uninfected rodents
Larvae feeding on deer
Uninfected engorged larvae
Uninfected questing nymphs
Nymphs feedingon deer
Uninfected nymphs feeding on rodents
Ixodes scapularis population model
Population and SIR model of Peromyscus leucopus
Uninfected rodents
Acutely-infected rodents
Carrier rodents
Juvenile females Subadult females
Pregnant females
Litter-bearing femalesJuvenile males Subadult males Adult males
Adult females
Juvenile females Subadult females
Pregnant females
Litter-bearing femalesJuvenile males Subadult males Adult males
Adult females
Juvenile females Subadult females
Pregnant females
Litter-bearing femalesJuvenile males Subadult males Adult males
Adult females
Uninfected rodents
Acutely-infected rodents
Carrier rodents
Juvenile females Subadult females
Pregnant females
Litter-bearing femalesJuvenile males Subadult males Adult males
Adult femalesJuvenile females Subadult females
Pregnant females
Litter-bearing femalesJuvenile males Subadult males Adult males
Adult females
Juvenile females Subadult females
Pregnant females
Litter-bearing femalesJuvenile males Subadult males Adult males
Adult femalesJuvenile females Subadult females
Pregnant females
Litter-bearing femalesJuvenile males Subadult males Adult males
Adult females
Juvenile females Subadult females
Pregnant females
Litter-bearing femalesJuvenile males Subadult males Adult males
Adult femalesJuvenile females Subadult females
Pregnant females
Litter-bearing femalesJuvenile males Subadult males Adult males
Adult females
Eggs
Questing Adults
Questing Larvae
Larvae feedingon infected rodents
Infected engorged larvae
Engorged Adults
Infected nymphs feedingon rodents
Infected questing nymphs
Egg-laying Adults
Engorged NymphsFeeding
Adults
Hardening Larvae
Larvae feedingon uninfected rodents
Larvae feeding on deer
Uninfected engorged larvae
Uninfected questing nymphs
Nymphs feedingon deer
Uninfected nymphs feeding on rodents
Eggs
Questing Adults
Questing Larvae
Larvae feedingon infected rodents
Infected engorged larvae
Engorged Adults
Infected nymphs feedingon rodents
Infected questing nymphs
Egg-laying Adults
Engorged NymphsFeeding
Adults
Hardening Larvae
Larvae feedingon uninfected rodents
Larvae feeding on deer
Uninfected engorged larvae
Uninfected questing nymphs
Nymphs feedingon deer
Uninfected nymphs feeding on rodents
Ixodes scapularis population model
Population and SIR model of Peromyscus leucopus
Data synthesised in silico to quantify effects of climate on (e.g.) a vector
20
Development rates, mortality rates and effects of temperature from lab/field studies
Host densities from field studies
Climate input variable
Vector/pathogen abundance or R0 output variables
Climate VBD relationship
Global and local sensitivity analyses conducted to check and measure uncertainty of model outputs
Application of mathematical modelling
• Mathematical models addressing fundamental principles = high level policy » Country-level risk assessment (current and future projections) » Country level risk, impact (BOI) cost-benefit assessments (current and future
projections)
• Simulation models = programmatic activities » Local-level risk, impact (BOI) cost-benefit assessments » Risk mapping using environmental drivers to targeting surveillance and
intervention » Identifying risk populations/locations to targeting surveillance and interventions » Forecasting for heightened surveillance or directing intervention » Dynamically modelling/predicting trajectories of spread » Predicting evolution of new pathogenic variants
Pros and cons Pros • Biological precision of associations between climate/environment and
vector/pathogen occurrence/survival - Use real, demonstrated associations and values
Cons • Require detailed knowledge of/data on ecology and epidemiology • Data frequently not available – need laboratory and field studies • Conflict between precision and parameterisation:
» The more spatio-temporal precision is needed – the more highly parameterised they need to be
» The more highly parameterised, the greater likelihood of erroneous parameter values • Prospective studies are needed for validation
22
Ecological niche models: statistical and other “pattern matching” methods
23
cMeanTXMinSVPRMinTMP ++++≈ 4321 ββββ
Salmonella Case Count and Mean Temperature per Week from 1992 to 2000
0
10
20
30
40
50
60
1 101 201 301 401
Week
Coun
t
-40
-30
-20
-10
0
10
20
30
Tem
pera
ture
Identifying associations between climate and occurrence/abundance
• Used where detailed, sound knowledge of the biology, sufficient to develop
simulation models is lacking
• Seek associations between possible explanatory variables (usually
environmental) and occurrence/abundance of vectors/pathogens using
environmental/ecological niche models:
• Presence-absence data – regression models
• Presence only data – machine learning/algorithm selection methods (MAXENT,
neural networks, GARP, BIOCLIM)
Combined GIS and statistical modelling
I am an Aedes albopictus and I was found here
Associated with: Climate Altitude Aspect Land use Agriculture Wildlife habitat Wildlife species Wildlife abundance Farm animal abundance
Here
Here
Here
Not Here
cMeanTXMinSVPRMinTMP ++++≈ 4321 ββββ
Climate VBD relationship Uncertainty expressed in errors, confidence intervals etc.
26 Application of ecological niche modelling
• High level policy » Country-level risk assessment (current and future projections) » Country level risk, impact (BOI) cost-benefit assessments (current and future
projections)
• Programmatic activities » Local-level risk, impact (BOI) cost-benefit assessments » Risk mapping using environmental drivers to targeting surveillance and
intervention » Identifying risk populations/locations to targeting surveillance and interventions » Forecasting for heightened surveillance or directing intervention
Pros and Cons
Pros • Presence-absence data usually readily available (e.g. surveillance) • Explanatory variable data very available (weather stations, remote-sensed
data, habitat maps, digital elevation models etc.) • Presence-absence data act as validating data (leave-one-out analyses)
Cons • Type I and II statistical errors – missing key environmental drivers,
identification of spurious associations • Frequently use false negative data:
» Realised niche rarely the full climatological niche » Vectors/pathogen range limited more by climate independent factors
27
x x x x x x
x x
EXAMPLES
28
Indices of climatic limits for survival: Assessing the risk of Chikungunya emergence in Canada
29
Key determinants of risk of Chikungunya establishment are:
• Immigrating infected people: now increasing in Canada due to infected holiday makers returning from the Caribbean
• Presence of vectors: simple lab/field-generated climatic indicators for persistence of populations of the temperate vector Aedes albopictus (the Asian tiger mosquito)
• Validation of climate indicators against surveillance data for Ae. albopictus in the US
ROC AUC = 0.92
Ogden et al. 2014 Parasites Vectors submitted
Predicted Observed
0 1 2 3 4 0 1 2 3 4
65 66 73 68 72 70 75 77 79 81 84 82 86 88 89 91 93 95 96 98 100
0 1 2 3 4
2011-2040 RCP4.5 2041-2070 RCP4.5 2041-2070 RCP8.5
Overwintering + annual mean
temperature
Jan temp + summer temp
+ annual rainfall
Climatic indicator
30
Projected distributions of Ae. albopictus
• Uncertainty associated with selection of climatic indicator for Ae. albopictus
• But can even so can feed risk assessments
Ogden et al. 2014 Parasites Vectors submitted
Patz et al. EHP 1998
VC = mbca 2 p n / -Ln ( p )
Simple mathematical model: assessing risk of Dengue with climate change
Assessing the risk of Lyme disease emergence in Canada: emergence by geographic spread from the US
32
Eggs
QuestingAdults
QuestingLarvae
FeedingLarvae
EngorgedLarvae
EngorgedAdults
FeedingNymphs
QuestingNymphs
Egg-layingAdults
POP (x)
PEP (q) L to N (s)
N to A (v)
EngorgedNymphs
No. eggsper adult (e)
Rodent Nos.
RodentNos.Temperature
Basic HFR:Immature ticks
Basic HFR:Adult ticks
Deer Nos.
λqa
µqeµql
µqn
µqa
µfl
µfn
µel
µelµel
λqn
λql
FeedingAdults
µfa
HardeningLarvae
µhl
z r
w
u
y
Eggs
QuestingAdults
QuestingLarvae
FeedingLarvae
EngorgedLarvae
EngorgedAdults
FeedingNymphs
QuestingNymphs
Egg-layingAdults
POP (x)POP (x)
PEP (q)PEP (q) L to N (s)L to N (s)
N to A (v)N to A (v)
EngorgedNymphs
No. eggsper adult (e)
Rodent Nos.Rodent Nos.
RodentNos.
RodentNos.TemperatureTemperature
Basic HFR:Immature ticks
Basic HFR:Adult ticks
Deer Nos.
λqa
µqeµql
µqn
µqa
µfl
µfn
µel
µelµel
λqn
λql
FeedingAdults
µfa
FeedingAdults
µfa
HardeningLarvae
µhl
zz rr
ww
u
yy
Key determinants of Lyme disease risk are:
• Suitable habitat for ticks: assessed by field study (Ogden et al. JME 2006a)
• Suitable host densities: assessed previous field studies
• Dispersal of population-seeding ticks into Canada by migratory birds: assessed by surveillance/field study (Ogden et al. JME 2006b, AEM 2008)
• Temperature threshold for tick population persistence: obtained by simulation modelling (Ogden et al. 2005)
• Algorithm using temperature from GCMs and tick dispersion developed and mapped
• Produces information needed for public health risk assessment
Photo by Bill Hilton Jr (www.hiltonpond.org)
High risk Low risk Risk of bird-borne ticks
year 2000
Moderate risk
Projected distributions of Ixodes scapularis
Ogden et al. Int. J. Health Geogr. 2008
High risk Low risk Risk of bird-borne ticks
year 2020
Moderate risk
Projected distributions of Ixodes scapularis
Ogden et al. Int. J. Health Geogr. 2008
High risk Low risk Risk of bird-borne ticks
year 2050
Moderate risk
Projected distributions of Ixodes scapularis
Ogden et al. Int. J. Health Geogr. 2008
year 2080
Projected distributions of Ixodes scapularis
High risk Low risk Risk of bird-borne ticks
Moderate risk
Ogden et al. Int. J. Health Geogr. 2008
Montreal ON
NY VT NH
Validation of blacklegged tick modelling in field studies and analyses of surveillance data
37
Ogden et al., 2008 Int J Hlth Geogr; 2010 EHP Leighton et al. 2012 J Appl Ecol
Ogden et al., Environ Health Perspect 2011
0
1
2
3
4
5
6
7
8
9
10
0 2000 4000 6000 8000 10000 12000 14000 16000
Algorithm used in risk maps
Inde
x of
cer
tain
ty fo
r I. s
capu
laris
pop
ulat
ions Quebec: 70 sites
New Brunswick: 16 sitesStatistical model
0
1
2
3
4
5
6
7
8
9
10
0 50 100 150 200 250 300
Model-predicted temperature suitability for I. scapularis
Inde
x of
cer
tain
ty fo
r I. s
capu
laris
pop
ulat
ion Quebec: 70 sites
New Brunswick: 16 sitesStatistical model
Current status of I. scapularis and Lyme disease incidence in Canada
38
2004 2014
Logistic regression modelling of Culex pipiens occurrence
39
Description Coef. Std. Err. Z p-value
Annual mean temperature 4.959 0.887 5.59 <0.001
Annual mean precipitation -0.026 0.005 -5.29 <0.001
Max temperature of the warmest period -1.106 0.236 -4.70 <0.001
January mean minimum temperature -0.379 0.115 -3.31 0.001
May mean minimum temperature -0.606 0.195 -3.11 0.002
June mean minimum temperature 0.338 0.139 2.43 0.015
September mean minimum temperature -0.897 0.196 -4.57 <0.001
October mean minimum temperature -0.864 0.229 -3.78 <0.001
January mean maximum temperature -0.381 0.150 -2.55 0.011
April mean maximum temperature -0.498 0.147 -3.40 0.001
September mean maximum temperature 0.670 0.197 3.39 0.001
November mean maximum temperature -0.458 0.187 -2.46 0.014
December mean maximum temperature -0.416 0.135 -3.09 0.002
January mean precipitation 0.026 0.007 3.64 <0.001
February mean precipitation 0.051 0.009 5.82 <0.001
March mean precipitation 0.028 0.010 2.85 0.004
April mean precipitation 0.056 0.007 7.79 <0.001
May mean precipitation 0.044 0.006 7.38 <0.001
June mean precipitation 0.022 0.006 3.57 <0.001
July mean precipitation 0.027 0.006 4.42 <0.001
August mean precipitation 0.036 0.007 5.32 <0.001
September mean precipitation 0.031 0.007 4.62 <0.001
October mean precipitation 0.026 0.006 4.41 <0.001
November mean precipitation 0.026 0.008 3.23 0.001
Within 2 kms of Cropland 0.053 0.160 3.30 0.001
Within 2 kms of Built-up land 0.441 0.167 2.64 0.008
Intercept -15.085 4.320 -3.49 <0.001
Presence-absence data
Multiple variables explored
Description Coef. Std. Err. Z p-value
Annual mean temperature above 2°C -0.067 0.059 -1.14 0.256 Annual precipitation between 600 and 1000 mm 0.001 0.001 7.40 <0.001 Mean March minimum temperature 0.148 0.025 5.90 <0.001 April mean precipitation below 130 mm 0.025 0.002 11.49 <0.001 Mean September maximum temperature above 21°C 0.081 0.013 6.24 <0.001 Mean November minimum temperature of previous year above -4°C 0.241 0.064 3.79 <0.001 Mean minimum temperature for July above 13°C 0.067 0.016 4.18 <0.001 Mean precipitation for August below 125 mm 0.010 0.002 4.21 <0.001 Intercept -5.158 0.356 -14.49 <0.001
Multi-variable model developed = climate-vector occurrence relationship
Projections developed – using GCM output
Hongoh et al., J App Geogr 2012
Statistical forecasting of WNv risk: Wang et al. J Med Entomol 2011
Summary
• A range of modelling methods are available to: » Elucidate quantitative relationships between climate and VBD occurrence/abundance » Develop projections using GCM outputs/develop adaptation tools using weather data
• All have pros and cons • Our confidence in them depends on validation • Gaps and needs to improve modelling:
» Data and knowledge of the life-cycles/transmission cycles of vectors and pathogens for simulation models
» Surveillance data collected in systemic spatio-temporal patterns for statistical models » Long-term monitoring to calibrate and validate projections » More direct linkage of climate and VBD simulation models » Linkage of effects of climate change on wider ecological and socio-economic
determinants of VBD risk » Standards for characterising uncertainty (spatial, temporal, model output)
41