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

Robert S Mccann,Joseph P Messina,David W Macfarlane,Edward D Walker,John M Vulule,John E Gimnig,M Bayoh

doi:10.1186/1476-072x-13-17

Abstract

BackgroundPredictive models of malaria vector larval habitat locations may provide a basis for understanding the spatial determinants of malaria transmission.MethodsWe 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.ResultsLarval 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.ConclusionsThis 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.

Highlights

Malaria is one of the most significant infectious diseases affecting people in poverty, with an estimated 219 million cases of malaria worldwide in 2010 killing 660,000 people [1]
Giles and Anopheles arabiensis Patton, which are both members of a species complex of eight closely related, morphologically indistinguishable species known collectively as Anopheles gambiae s.l. [11]
The objectives of this study were to create a model for predicting larval An. gambiae s.l. habitat locations using landscape variables that predict the likelihood of standing water bodies, and to account for seasonal changes in habitat probability based on accumulated precipitation

Summary

Introduction

Malaria is one of the most significant infectious diseases affecting people in poverty, with an estimated 219 million cases of malaria worldwide in 2010 killing 660,000 people [1]. The spatial distribution of the larval habitats partially determines the spatial distribution of the adult malaria vectors in many landscapes [5,8,9,10]. Understanding the factors that determine the distribution of the larval habitats facilitates our understanding of the spatial determinants of malaria transmission. In many regions the larval habitats of An. gambiae s.s. and An. arabiensis are similar, and the two species are often found within the same larval habitats [12,13,14] These larval habitats are generally smaller, temporary bodies of standing water persisting for about 20 to 40 days [12,15], with rain being the main source of the water. Predictive models of malaria vector larval habitat locations may provide a basis for understanding the spatial determinants of malaria transmission

Objectives

Methods

Results

Discussion

Conclusion