Investigating the spatial micro-epidemiology of diseases within a point-prevalence sample: a field applicable method for rapid mapping of households using low-cost GPS-dataloggers

Abstract

Point-prevalence recording of the distribution of tropical parasitic diseases at village level is usually sufficient for general monitoring and surveillance. Whilst within-village spatial patterning of diseases exists, and can be important, mapping infected cases in a household-by-household setting is arduous and time consuming. With the development of low-cost GPS-data loggers (< £40) and available GoogleEarth(TM) satellite imagery, we present a field-applicable method based on crowdsourcing for rapid identification of infected cases (intestinal schistosomiasis, malaria and hookworm) by household. A total of 126 mothers with their 247 preschool children from Bukoba village (Mayuge District, Uganda) were examined with half of these mothers given a GPS-data logger to walk home with, returning the unit the same day for data off-loading, after which, households were assigned GPS coordinates. A satellite image of Bukoba was annotated with households denoting the infection status of each mother and child. General prevalence of intestinal schistosomiasis, malaria and hookworm in mothers and children was: 27.2 vs 7.7%, 28.6 vs 87.0% and 60.0 vs 22.3%, respectively. Different spatial patterns of disease could be identified likely representing the intrinsic differences in parasite biology and interplay with human behaviour(s) across this local landscape providing a better insight into reasons for disease micro-patterning.