Predicting malaria infection in Gambian children from satellite data and bed net use surveys: the importance of spatial correlation in the interpretation of results.
In line with the renewed World Health Organization Global Malaria Control Strategy, we have advocated the use of satellite imagery by control services to provide environmental information for malaria stratification, monitoring, and early warning. To achieve this operationally, appropriate methodologies must be developed for integrating environmental and epidemiologic data into models that can be used by decision-makers for improved resource allocation. Using methodologies developed for the Famine Early Warning Systems and spatial statistics, we show a significant association between age related malaria infection in Gambian children and the amount of seasonal environmental greenness as measured using the normalized difference vegetation index derived from satellite data. The resulting model is used to predict changes in malaria prevalence rates in children resulting from different bed net control scenarios.
Thomson
M C
MC
MALSAT Research Group, Liverpool School of Tropical Medicine, United Kingdom.
Connor
S J
SJ
D'Alessandro
U
U
Rowlingson
B
B
Diggle
P
P
Cresswell
M
M
Greenwood
B
B
D000818:Animals; D001769:Blood / Q000469:parasitology; D002675:Child, Preschool; D005544:Forecasting; D005714:Gambia / Q000453:epidemiology; D006801:Humans; D007223:Infant; D016778:Malaria, Falciparum / Q000453:epidemiology* / Q000517:prevention & control / Q000635:transmission; D008954:Models, Biological; D015233:Models, Statistical; D018512:Parasitemia / Q000453:epidemiology; D010963:Plasmodium falciparum / Q000254:growth & development; D012044:Regression Analysis; D012424:Rural Population; D017215:Satellite Communications; D012621:Seasons; D016036:Seroepidemiologic Studies; D014329:Tropical Climate; D014481:United States; D018557:United States National Aeronautics and Space Administration