REMOTE-SENSING AS A LANDSCAPE EPIDEMIOLOGIC TOOL TO IDENTIFY VILLAGESAT HIGH-RISK FOR MALARIA TRANSMISSION

A landscape approach using remote sensing and geographic information s ystem (GIS) technologies was developed to discriminate between village s at high and low risk for malaria transmission, as defined by adult A nopheles albimanus abundance. Satellite data for an area in southern C hiapas, Mexico were digitally processed to generate a map of landscape elements. The GIS processes were used to determine the proportion of mapped landscape elements surrounding 40 villages where An. albimanus abundance data had been collected. The relationships between vector ab undance and landscape element proportions were investigated using step wise discriminant analysis and stepwise linear regression. Both analys es indicated that the most important landscape elements in terms of ex plaining vector abundance were transitional swamp and unmanaged pastur e. Discriminant functions generated for these two elements were able t o correctly distinguish between villages with high and low vector abun dance, with an overall accuracy of 90%. Regression results found both transitional swamp and unmanaged pasture proportions to be predictive of vector abundance during the mid-to-late wet season. This approach, which integrates remotely sensed data and GIS capabilities to identify villages with high vector-human contact risk, provides a promising to ol for malaria surveillance programs that depend on labor-intensive fi eld techniques. This is particularly relevant in areas where the lack of accurate surveillance capabilities may result in no malaria control action when, in fact, directed action is necessary. In general, this landscape approach could be applied to other vector-borne diseases in areas where 1) the landscape elements critical to vector survival are known and 2) these elements can be detected at remote sensing scales.