1. ASSESSMENT OF THE ANNUAL VARIATION OF MALARIA AND THE CLIMATE EFFECT BASED ON KAHNOOJ DATA 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 Haghdoost Dr. Neal Alexander Dr 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. 1 1.07(1.03-1.11) 0.99(0.95-1.04) 0.56(0.53-0.59) 10.4 11.2 10.3 5.6 28571 66316 48498 50962 2972 7436 5001 2842 Age <5 5-14 15-29 >=30 1 0.52(0.48-0.56) 9.8 4.8 179936 12950 17628 626 Nationality Iran Afghanistan 1 0.91 (0.83-1.0) 9.1 8.2 191400 4880 17471 401 Accommodation Permanent Temporary 1 0.86 (0.83-0.88) 10.2 8.7 976635 957135 9932 8326 Sex Male Female Risk ratio (95% CI) Disease risk between 1994-2002 (per 100)PopulationMalaria case Risk factors Malaria Risk factors P.falciparum risk P.vivax risk Alls species risk Observed annual risk of malaria per 100,000 population between 1994-2001 9 - 76 77 - 197 198 - 348 349 - 676 677 - 2343 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 species 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) All species 565(17.1)608(18.4)1454(14.4) P. vivax 396(20.1)321(16.3)1113(18.4) P. falciparum Under estimation (% 3 ) Over estimation (% 3 ) Under estimation (% 3 ) Over estimation (%3) Checking part 2 Modelling part1 species 1: the model was built based on three-quarters of data 2: the fitted value was computed based on the estimated parameters in modelling part of data 3: total numbers of over or underestimation divided by total number of cases 4: the average of monthly number of over or underestimations References: