Analysing the generality of spatially predictive mosquito habitat models

Abstract

The increasing spread of multi-drug resistant malaria in African highlands has highlighted the importance of malaria suppression through vector control. Its historical success has meant that larval control has been proposed as part of an integrated malaria vector control program. Due to high operation costs, larval control activities would benefit greatly if the locations of mosquito habitats could be identified quickly and easily, allowing for focal habitat source suppression. Several mosquito habitat models have been developed to predict the location of mosquito habitats. However, to what extent these models can be generalised across time and space to predict the distribution of dynamic mosquito habitats remains largely unexplored. This study used mosquito habitat data collected in six different time periods and four different modelling approaches to establish 24 mosquito habitat models. We systematically tested the generality of these 24 mosquito habitat models. We found that although habitat–environment relationships change temporally, a modest level of performance was attained when validating the models using data collected from different time periods. We also describe flexible approaches to the predictive modelling of mosquito habitats, that provide novel modelling architecture for future research efforts.

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

“…We used two approaches for modeling the distribution of larval habitats across the landscape, logistic regression and random forest [ 36 ]. Logistic regression is commonly used in species distribution modeling [ 20 , 21 , 37 ]. Ecologists have recently started using the random forest method as well, because it does not require any assumptions about the distribution of the data [ 38 , 39 ].…”

“…The influence of soil types on the presence of larval habitats has largely been ignored, although Bøgh and colleagues [ 22 ] found larval habitats exclusively in alluvial soils in The Gambia. Finally, seasonal differences in rainfall likely influence the number of larval habitats on the landscape [ 12 , 16 , 19 , 20 , 23 ].…”

Physico-chemical and biological characterization of anopheline mosquito larval habitats (Diptera: Culicidae): implications for malaria control

“…Habitat suitability models take into consideration the occurrence and/or abundance of species in relation to biotic and abiotic environmental factors, evaluating the habitat quality or predicting its effect on species occurrences as a result of environmental changes within the habitat [ 33 ]. However, species-habitat relationships are influenced by regional conditions and hence, the generality of these models needs to be tested [ 34 ]. Therefore, we developed data-driven models using decision trees and generalized linear models in order to assess the relationship between abiotic and biotic environmental factors and the occurrence and abundance of anopheline mosquito larvae in Southwest Ethiopia.…”

Adaptive interventions for optimizing malaria control: an implementation study protocol for a block-cluster randomized, sequential multiple assignment trial

“…A variety of predictor variables for malaria risk will be collected for each cluster: (1) human density and age structure; (2) topographic parameters, which are associated with mosquito larval habitat formation [ 70 , 71 ]; (3) land-use and land-cover types related to the development and survival of Anopheline mosquito larvae and adults [ 72 – 74 ]; and (4) meteorological data. Topographic parameters such as elevation, slope, wetness index, flow distance to stream, aspect of land surface, and curvature will be obtained from the digital elevation model of the study site, which we have already developed.…”