Malaria in Africa: Vector Species' Niche Models and Relative Risk Maps

Abstract

A central theoretical goal of epidemiology is the construction of spatial models of disease prevalence and risk, including maps for the potential spread of infectious disease. We provide three continent-wide maps representing the relative risk of malaria in Africa based on ecological niche models of vector species and risk analysis at a spatial resolution of 1 arc-minute (9 185 275 cells of approximately 4 sq km). Using a maximum entropy method we construct niche models for 10 malaria vector species based on species occurrence records since 1980, 19 climatic variables, altitude, and land cover data (in 14 classes). For seven vectors (Anopheles coustani, A. funestus, A. melas, A. merus, A. moucheti, A. nili, and A. paludis) these are the first published niche models. We predict that Central Africa has poor habitat for both A. arabiensis and A. gambiae, and that A. quadriannulatus and A. arabiensis have restricted habitats in Southern Africa as claimed by field experts in criticism of previous models. The results of the niche models are incorporated into three relative risk models which assume different ecological interactions between vector species. The “additive” model assumes no interaction; the “minimax” model assumes maximum relative risk due to any vector in a cell; and the “competitive exclusion” model assumes the relative risk that arises from the most suitable vector for a cell. All models include variable anthrophilicity of vectors and spatial variation in human population density. Relative risk maps are produced from these models. All models predict that human population density is the critical factor determining malaria risk. Our method of constructing relative risk maps is equally general. We discuss the limits of the relative risk maps reported here, and the additional data that are required for their improvement. The protocol developed here can be used for any other vector-borne disease.

Modelling spatial distribution of snails transmitting parasitic worms with importance to human and animal health and analysis of distributional changes in relation to climate

“…Other factors that are known to affect snail distribution, such as land use and snail control were not included as no such period-specific data could be identified. Comparatively few environmental variables were explored in comparison to other studies (Moffett et al, 2007;Stensgaard, 2011;Valencia-Lopez et al, 2012;Stensgaard et al, 2013) because few environmental variables were available for the earlier period at the desired resolution. Only variables shown to have been of importance in earlier research were included, though a dataset of the mean daily temperature from the CRU dataset (Harris et al, 2013) was left out due to its high correlation to other datasets.…”

Non-destructive Determination of Age and Species of Anopheles gambiae s.l. Using Near-infrared Spectroscopy

“…This approach may therefore enable vastly more extensive and intensive measurement of vector population composition across space and time than has previously been possible, enabling much-improved parameterization of analytical models, which provide insight into the ecology of population dynamics. 19,20,66,67 Furthermore, this is the only method that is capable of age-grading adults non-destructively, enabling specimens to be preserved for subsequent analyses of genetic or biochemical traits vital to the current and future success of control methods (e.g., insecticide resistance, diversity, and rates of gene flow). For these reasons, we advocate NIRS as a useful and welcome addition to the vector ecologists toolbox for large-scale field surveys.…”

The climatic suitability for maize cultivation in China

“…These models included the ecological models BIOCLIM, BLOMAPPER, DIVA, and DOMAIN, the dynamic simulation model CLIMEX, a generalized additive model (GAM), a generalized linear model (GLM), a genetic algorithm for rule-set prediction (GARP), and MaxEnt [27][28][29][30][31]. Of these, MaxEnt showed the best predictive capacity and was the most precise [32][33][34][35][36][37][38], giving the most accurate distribution function based on best entropy. First, the characteristic space, i.e.…”