MALARIA: Local topographic wetness indices predict household malaria risk better than land-use and land-cover in the western Kenya highlands

Justin M Cohen , Kacey C Ernst , Kim A Lindblade , John M Vulule , Chandy C John and Mark L Wilson
Malaria Journal 2010, 9:328doi:10.1186/1475-2875-9-328
Published: 16 November 2010
Abstract (provisional)
Background
Identification of high-risk malaria foci can help enhance surveillance or control activities in regions where they are most needed. Associations between malaria risk and land-use/land-cover are well-recognized, but these environmental characteristics are closely interrelated with the land's topography (e.g., hills, valleys, elevation), which also influences malaria risk strongly. Parsing the individual contributions of land-cover/land-use variables to malaria risk requires examining these associations in the context of their topographic landscape. This study examined whether environmental factors like land-cover, land-use, and urban density improved malaria risk prediction based solely on the topographically-determined context, as measured by the topographic wetness index.
Methods
The topographic wetness index, an estimate of predicted water accumulation in a defined area, was generated from a digital terrain model of the landscape surrounding households in two neighbouring western Kenyan highland communities. Variables determined to best encompass the variance in this topographic wetness surface were calculated at a household level. Land-cover/land-use information was extracted from a high-resolution satellite image using an object-based classification method. Topographic and land-cover variables were used individually and in combination to predict household-level malaria in the communities through an iterative split-sample model fitting and testing procedure. Models with only topographic variables were compared to those with additional predictive factors related to land-cover/land-use to investigate whether these environmental factors improved prediction of malaria based on the shape of the land alone.
Results
Variables related to topographic wetness proved most useful in predicting the households of individuals contracting malaria in this region of rugged terrain. Other variables related to human modification of the environment also demonstrated clear associations with household malaria. However, these land-cover/land-use variables failed to produce unambiguous improvements in statistical predictive models controlling for important topographic factors, with none improving prediction of household-level malaria more than 75% of the time.
Conclusions
Topographic wetness values in this region of highly varied terrain more accurately predicted houses at greater risk of malaria than did consideration of land-cover/land-use characteristics. As such, those planning control or local elimination strategies in similar highland regions may use topographic and geographic characteristics to effectively identify high-receptivity regions that may require enhanced vigilance.
http://www.malariajournal.com/content/9/1/328

MALARIA: New tool predicts malaria 90 days before an outbreak

A collaborative nine-year research project in Kenya has created the September launch of a new tool that calculates data based on environmental factors (weather, geography) and mosquitoes' mating schedule to successfully (within 86-100 percent) predict a malaria epidemic.
The Kenya Medical Research Institute (KEMRI), Kenya Meteorological Department and International Centre for Insect Physiology and Ecology tested their tool in Kenya, Tanzania and Uganda according to Canada's International Development Research Centre, which also co-funded the collaboration. KEMRI's director Dr. Solomon Mpoke explained, "This tool will immensely change the way we are going to control malaria in Kenya and the Greater Horn of Africa, since we will have an early warning from the tool three months before it happens."The United Nations had urged this tool be developed before the researchers understood its efficacy."The disease prediction tool should also help policymakers and health officials prepare in time to deal with looming outbreaks," with regards to when and where to spray.
http://www.independent.co.uk/life-style/health-and-families/new-tool-predicts-malaria-90-days-before-an-outbreak-2081847.html