Towards malaria risk prediction in Afghanistan using remote sensing

Malaria Journal

Research

Richard Kiang¹  Radina P Soebiyanto²  Farida Adimi³  Najibullah Safi⁴ 

[1] Global Change Data Center, NASA Goddard Space Flight Center, 20771, Maryland, Greenbelt, USA;Global Change Data Center, NASA Goddard Space Flight Center, 20771, Maryland, Greenbelt, USA;Goddard Earth Sciences and Technology Center, University of Maryland Baltimore County, 21228, Maryland, Baltimore, USA;Global Change Data Center, NASA Goddard Space Flight Center, 20771, Maryland, Greenbelt, USA;Wyle Information Systems, 22102, Virginia, McLean, USA;National Malaria and Leishmaniasis Control Programme, Afghan Ministry of Public Health, Sanatorium Road, Kabul, Darulaman, Afghanistan;

关键词: Malaria;    Normalize Difference Vegetation Index;    Malaria Transmission;    Malaria Case;    Tropical Rainfall Measure Mission;   

DOI  :  10.1186/1475-2875-9-125

 received in 2009-12-09, accepted in 2010-05-13,  发布年份 2010

来源: Springer

【 摘 要 】

BackgroundMalaria is a significant public health concern in Afghanistan. Currently, approximately 60% of the population, or nearly 14 million people, live in a malaria-endemic area. Afghanistan's diverse landscape and terrain contributes to the heterogeneous malaria prevalence across the country. Understanding the role of environmental variables on malaria transmission can further the effort for malaria control programme.MethodsProvincial malaria epidemiological data (2004-2007) collected by the health posts in 23 provinces were used in conjunction with space-borne observations from NASA satellites. Specifically, the environmental variables, including precipitation, temperature and vegetation index measured by the Tropical Rainfall Measuring Mission and the Moderate Resolution Imaging Spectoradiometer, were used. Regression techniques were employed to model malaria cases as a function of environmental predictors. The resulting model was used for predicting malaria risks in Afghanistan. The entire time series except the last 6 months is used for training, and the last 6-month data is used for prediction and validation.ResultsVegetation index, in general, is the strongest predictor, reflecting the fact that irrigation is the main factor that promotes malaria transmission in Afghanistan. Surface temperature is the second strongest predictor. Precipitation is not shown as a significant predictor, as it may not directly lead to higher larval population. Autoregressiveness of the malaria epidemiological data is apparent from the analysis. The malaria time series are modelled well, with provincial average R2 of 0.845. Although the R2 for prediction has larger variation, the total 6-month cases prediction is only 8.9% higher than the actual cases.ConclusionsThe provincial monthly malaria cases can be modelled and predicted using satellite-measured environmental parameters with reasonable accuracy. The Third Strategic Approach of the WHO EMRO Malaria Control and Elimination Plan is aimed to develop a cost-effective surveillance system that includes forecasting, early warning and detection. The predictive and early warning capabilities shown in this paper support this strategy.

【 授权许可】

Unknown   
© Adimi et al; licensee BioMed Central Ltd. 2010. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.