Int J Health Geogr - Modeling larval malaria vector habitat locations using landscape features and cumulative precipitation measures.


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CKGROUND: 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.

Resumo Limpo

ckground predict model malaria vector larval habitat locat may provid basi understand spatial determin malaria transmissionmethod use four landscap variabl topograph wet index twi soil type land useland cover distanc stream accumul precipit model larval habitat locat region western kenya two method logist regress random forest addit use two separ data set account variat habitat locat across space timeresult larval habitat like present locat lower slope contribut area ratio ie twi closer stream agricultur land use relat nonagricultur land use friabl claysandi clay loam soil firm silti clayclay soil relat friabl clay soil probabl larval habitat presenc increas increas accumul precipit random forest model accur logist regress model especi accumul precipit includ account season differ precipit accur model two data set area curv auc valu respect twi distanc nearest stream precipit greatest mean decreas gini impur criteria modelsconclus studi demonstr use random forest model larval malaria vector habitat model twi distanc nearest stream two import landscap variabl model includ accumul precipit model improv accuraci larval habitat locat predict account season variat precipit final sampl strategi employ model parameter serv framework creat predict larval habitat model assist larval control effort

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