Sunday, June 24, 2012


N. Safi, F. Adimi, R. P. Soebiyanto, R. K. Kiang

Commission VIII, WG 2

KEY WORDS: Malaria, risk prediction, Afghanistan, remote sensing


Malaria causes more than one million deaths every year worldwide, with most of the mortality in Sub-Saharan Africa. It is also a significant public health concern in Afghanistan, with approximately 60% of the population, or nearly 14 million people, living in a malaria-endemic area. Malaria transmission has been shown to be dependent on a number of environmental and meteorological variables. For countries in the tropics and the subtropics, rainfall is normally the most important variable, except for regions with high altitude where temperature may also be important. Afghanistan’s diverse landscape contributes to the heterogeneous malaria distribution. Understanding the environmental effects on malaria transmission is essential to the effective control of malaria in Afghanistan. Provincial malaria data gathered by Health Posts in 23 provinces during 2004-2007 are used in this study. Remotely sensed geophysical parameters, including precipitation from TRMM, and surface temperature and vegetation index from MODIS are used to derive the empirical relationship between malaria cases and these geophysical parameters. Both neural network methods and regression analyses are used to examine the environmental dependency of malaria transmission. And the trained models are used for predicting future transmission. While neural network methods are intrinsically more adaptive for nonlinear relationship, the regression approach lends itself in providing statistical significance measures. Our results indicate that NDVI is the strongest predictor. This reflects the role of irrigation, instead of precipitation, in Afghanistan for agricultural production. The second
strongest prediction is surface temperature. Precipitation is not shown as a significant predictor, contrary to other malarious countries in the tropics or subtropics. With the regression approach, the malaria time series are modelled well, with average R2 of 0.845. For cumulative 6-month prediction of malaria cases, the average provincial accuracy reaches 91%. The developed predictive and early warning capabilities support the Third Strategic Approach of the WHO EMRO Malaria Control and Elimination Plan.

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