ASSESSMENT OF THE ANNUAL VARIATION OF ASSESSMENT OF THE ANNUAL VARIATION OF MALARIA AND THE CLIMATE EFFECT BASED ON KAHNOOJ MALARIA AND THE CLIMATE EFFECT BASED ON KAHNOOJ

DATA BETWEEN 1994 AND 2001DATA BETWEEN 1994 AND 2001

Conclusions

1. One month lag between predictors and malaria risk maximised the accuracies

2. Seasonality, time trend and autocorrelations between temporal risks explained a considerable part of temporal

3. Ground climate data increased the model accuracies significantly

4. Remote sensing data (NDVI and land surface temperature, improved the spatial and temporal predictions

5. GIS is powerful tool to illustration and analysis the temporal and spatial distributions  

Dr. Ali-Akbar HaghdoostDr. Ali-Akbar HaghdoostDr. Neal AlexanderDr. Neal AlexanderDr Jonathan CoxDr Jonathan Cox

Malaria, as a complex multi-factorial disease, is sensitive to climate, particularly temperature and precipitation. However, it depends on a range of factors such as the quality, and quantity of vector control programmes and case detection/treatment

strategy.

The surveillance data of malaria cases in an endemic area of Iran, Kahnooj, was linked to the ground and remote sensing climate data.

Objective: To check the feasibility of malaria predicting models based on climate, in strongly seasonally transmitted regions

Using Poisson regression models, the numbers of expected cases were predicted based on temperature (ground and remote sensing), humidity and rainfall and vegetation index (NDVI), seasonality, time trend and autocorrelation between temporal risks.

11.07(1.03-1.11)0.99(0.95-1.04)0.56(0.53-0.59)

10.411.210.35.6

28571663164849850962

2972743650012842

Age<55-1415-29>=30

10.52(0.48-0.56)

9.84.8

17993612950

17628626

NationalityIranAfghanistan

10.91 (0.83-1.0)

9.18.2

1914004880

17471401

AccommodationPermanentTemporary

10.86 (0.83-0.88)

10.28.7

976635957135

99328326

SexMaleFemale

Risk ratio(95% CI)

Disease risk between 1994-2002 (per 100)PopulationMalaria caseRisk factors

Malaria Risk factors

P.falciparum riskP.vivax riskAlls species riskObserved annual risk of malaria per 100,000 population between 1994-2001

56 - 162163 - 297298 - 659660 - 19571958 - 2924

64 - 286287 - 537538 - 839840 - 31293130 - 5019

9 - 7677 - 197198 - 348349 - 676677 - 2343

010

020

030

0

Jun 00Jun 98Jun 96Jun 94date

fitted value ppv

010

020

030

0

Jun 00Jun 98Jun 96Jun 94date

fitted value ppf

050

0

Jun 00Jun 98Jun 96Jun 94date

fitted value all species

num

ber o

f cas

es

The fitted values of models based on seasonality, time trend, autocorrelations between risks and meteorological variables classified by species, the observed numbers (dotes) and model estimated number (solid line)

Vegetation index (NDVI)

Altitude

These maps show the distributions of villages, cities and roads, and NDVI and altitude

The data were randomly were divided in modelling (75%) and checking (25%). The parameters were estimated based on the modelling part of data. The accuracy of models were checked by comparing the fitted and observed values in checking part of data

0.00

0.25

0.50

0.75

1.00

Sens

itivi

ty

0.00 0.25 0.50 0.75 1.001 - Specificity

Area under ROC curve = 0.8626

P.vivax

0.00

0.25

0.50

0.75

1.00

Sens

itivi

ty

0.00 0.25 0.50 0.75 1.001 - Specificity

Area under ROC curve = 0.8462

P.falciparum

0.00

0.25

0.50

0.75

1.00

Sens

itivi

ty

0.00 0.25 0.50 0.75 1.001 - Specificity

Area under ROC curve = 0.8452

all speciesspecies specified ROCs, they assess the relationship between sensitivity and specificity of the full models (seasonality, time trend, history of disease in previous 8-18 month

within the village, population NDVI and LST) in predicting local transmissions in all data

The local transmission in each village was defined as the present of at least two species specific malaria cases in a month, or in two consecutive months

Over and under estimation of final model classified by transmission

period, based on seasonality, time trend, ground climate data (temperature, humidity and rainfall, and autocorrelations between temporal risks

881(16.7)870(16.5)2326(14.4)2326(14.4)All species

565(17.1)608(18.4)1454(14.4)1454(14.4)P. vivax

396(20.1)321(16.3)1113(18.4)1113(18.4)P. falciparum

Under estimation(%3)

Over estimation(%3)

Under estimation(%3)

Over estimation(%3)

Checking part2Modelling part1

species

1: the model was built based on three-quarters of data2: the fitted value was computed based on the estimated parameters in modelling part of data3: total numbers of over or underestimation divided by total number of cases4: the average of monthly number of over or underestimations

References: