%0 Journal Article %1 Moffett2007 %A Moffett, Alexander %A Shackelford, Nancy %A Sarkar, Sahotra %D 2007 %J PLoS One %K malaria vectorborne Africa predictivemodel MaxEnt %N 9 %P e824 %R 10.1371/journal.pone.0000824 %T Malaria in Africa: vector species' niche models and relative risk maps. %U http://dx.doi.org/10.1371/journal.pone.0000824 %V 2 %X 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 ädditive" 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.

@article{Moffett2007, 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.}, added-at = {2010-01-14T17:47:30.000+0100}, author = {Moffett, Alexander and Shackelford, Nancy and Sarkar, Sahotra}, biburl = {https://www.bibsonomy.org/bibtex/28236f1419665d633cfdb49d95876e2ba/uvesco}, doi = {10.1371/journal.pone.0000824}, file = {:Moffett2007.pdf:PDF}, institution = {Section of Integrative Biology, University of Texas at Austin, Austin, Texas, United States of America.}, interhash = {edc13b211e24c96a513d391a0330d59f}, intrahash = {8236f1419665d633cfdb49d95876e2ba}, journal = {PLoS One}, keywords = {malaria vectorborne Africa predictivemodel MaxEnt}, language = {eng}, medline-pst = {epublish}, number = 9, pages = {e824}, pmid = {17786196}, timestamp = {2010-01-14T17:47:30.000+0100}, title = {Malaria in Africa: vector species' niche models and relative risk maps.}, url = {http://dx.doi.org/10.1371/journal.pone.0000824}, volume = 2, year = 2007 }