Application of geographic information systems to the study of the ecology of mosquitoes and mosquito-borne diseases

Geographic information systems (GIS) are powerful computer mapping and analysis systems for studying spatial patterns and processes; they are applicable to numerous disciplines, including the study of mosquito ecology. The distribution of mosquitoes is largely dependent upon the spatial distribution of their larval breeding sites, their flight range and the spatial distribution of their preferred hosts. These are all heterogeneous in space and time and GIS therefore has many potential applications to the study of mosquitoes and the diseases they transmit. GIS may be used to map and analyse the spatial distribution of mosquitoes and to assess the ecological factors that contribute to observed distributions. A detailed understanding of what drives heterogeneities in the distribution of mosquitoes and mosquito-borne diseases can help to design better, more efficient control programmes that maximize the use of limited resource

Modeling larval malaria vector habitat locations using landscape features and cumulative precipitation measures

BACKGROUND: Predictive models of malaria vector larval habitat locations may provide a basis for understanding the spatial determinants of malaria transmission. METHODS: We used four landscape variables (topographic wetness index [TWI], soil type, land use-land cover, and distance to stream) and accumulated precipitation to model larval habitat locations in a region of western Kenya through two methods: logistic regression and random forest. Additionally, we used two separate data sets to account for variation in habitat locations across space and over time. RESULTS: Larval habitats were more likely to be present in locations with a lower slope to contributing area ratio (i.e. TWI), closer to streams, with agricultural land use relative to nonagricultural land use, and in friable clay/sandy clay loam soil and firm, silty clay/clay soil relative to friable clay soil. The probability of larval habitat presence increased with increasing accumulated precipitation. The random forest models were more accurate than the logistic regression models, especially when accumulated precipitation was included to account for seasonal differences in precipitation. The most accurate models for the two data sets had area under the curve (AUC) values of 0.864 and 0.871, respectively. TWI, distance to the nearest stream, and precipitation had the greatest mean decrease in Gini impurity criteria in these models. CONCLUSIONS: This study demonstrates the usefulness of random forest models for larval malaria vector habitat modeling. TWI and distance to the nearest stream were the two most important landscape variables in these models. Including accumulated precipitation in our models improved the accuracy of larval habitat location predictions by accounting for seasonal variation in the precipitation. Finally, the sampling strategy employed here for model parameterization could serve as a framework for creating predictive larval habitat models to assist in larval control efforts