Use of remote sensing to identify spatial risk factors for malaria in a region of declining transmission: a cross-sectional and longitudinal community survey

• Published in: Malaria Journal
• Main Authors: Clennon Julie, Kamanga Aniset, Shields Timothy, Kobayashi Tamaki, Hamapumbu Harry, Moss William J, Mharakurwa Sungano, Thuma Philip E, Glass Gregory
• Format: Article in Journal/Newspaper
• Language: English
• Published: BMC 2011
• Subjects: Arctic medicine. Tropical medicine; RC955-962; Infectious and parasitic diseases; RC109-216; Arctic
• Online Access: https://doi.org/10.1186/1475-2875-10-163; https://doaj.org/article/be2c4c59a1bc476baef9341fc92c12a4

Abstract Background The burden of malaria has decreased dramatically within the past several years in parts of sub-Saharan Africa. Further malaria control will require targeted control strategies based on evidence of risk. The objective of this study was to identify environmental risk factors for malaria transmission using remote sensing technologies to guide malaria control interventions in a region of declining burden of malaria. Methods Satellite images were used to construct a sampling frame for the random selection of households enrolled in prospective longitudinal and cross-sectional surveys of malaria parasitaemia in Southern Province, Zambia. A digital elevation model (DEM) was derived from the Shuttle Radar Topography Mission version 3 DEM and used for landscape characterization, including landforms, elevation, aspect, slope, topographic wetness, topographic position index and hydrological models of stream networks. Results A total of 768 individuals from 128 randomly selected households were enrolled over 21 months, from the end of the rainy season in April 2007 through December 2008. Of the 768 individuals tested, 117 (15.2%) were positive by malaria rapid diagnostic test (RDT). Individuals residing within 3.75 km of a third order stream were at increased risk of malaria. Households at elevations above the baseline elevation for the region were at decreasing risk of having RDT-positive residents. Households where new infections occurred were overlaid on a risk map of RDT positive households and incident infections were more likely to be located in high-risk areas derived from prevalence data. Based on the spatial risk map, targeting households in the top 80 th percentile of malaria risk would require malaria control interventions directed to only 24% of the households. Conclusions Remote sensing technologies can be used to target malaria control interventions in a region of declining malaria transmission in southern Zambia, enabling a more efficient use of resources for malaria elimination.