Predictive mapping of mosquito distribution based on environmental and anthropogenic factors in Taita Hills,Kenya |
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| Affiliation: | 1. Departamento de Epidemiologia, Faculdade de Saúde Pública, Universidade de São Paulo, Av. Dr. Arnaldo, 715, São Paulo, SP, Brazil;2. Divisão Científica de Gestão, Ciência e Tecnologia Ambiental do Instituto de Energia e Ambiente - IEE da Universidade de São Paulo, São Paulo, SP, Brazil;3. Laboratório de Pesquisa em Virologia, Faculdade de Medicina de São José do Rio Preto, São José do Rio Preto, SP, Brazil;4. Laboratório de Entomologia, Superintendência de Controle de Endemias, São Paulo, SP, Brazil;5. Laboratório de Vetores, Superintendência de Controle de Endemias, São José do Rio Preto, SP, Brazil;1. Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA;2. Maricopa County Environmental Services, Department Vector Control Division, Phoenix, AZ, USA;3. Center for Healthcare Organization and Implementation Research, VA Bedford Healthcare System, Bedford, MA, USA;4. Boston University School of Social Work, Boston, MA, USA;5. Center for Health Emergencies, Bruno Kessler Foundation, Trento, Italy;6. Arboviral Diseases Branch (ADB), Division of Vector-Borne Diseases (DVBD), Centers for Disease Control and Prevention (CDC), Fort Collins, CO, USA;1. Department of Veterinary Surgery, College of Veterinary Medicine, Northeast Agricultural University, Harbin, Heilongjiang Province, People''s Republic of China;2. Key Laboratory of the Provincial Education Department of Heilongjiang for Common Animal Disease Prevention and Treatment, College of Veterinary Medicine, Northeast Agricultural University, Harbin, Heilongjiang Province, People''s Republic of China |
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| Abstract: | Mosquitoes are vectors for numerous pathogens, which are collectively responsible for millions of human deaths each year. As such, it is vital to be able to accurately predict their distributions, particularly in areas where species composition is unknown. Species distribution modeling was used to determine the relationship between environmental, anthropogenic and distance factors on the occurrence of two mosquito genera, Culex Linnaeus and Stegomyia Theobald (syn. Aedes), in the Taita Hills, southeastern Kenya. This study aims to test whether any of the statistical prediction models produced by the Biomod2 package in R can reliably estimate the distributions of mosquitoes in these genera in the Taita Hills; and to examine which factors best explain their presence. Mosquito collections were acquired from 122 locations between January–March 2016 along transects throughout the Taita Hills. Environmental-, anthropogenic- and distance-based geospatial data were acquired from the Taita Hills geo-database, satellite- and aerial imagery and processed in GIS software. The Biomod2 package in R, intended for ensemble forecasting of species distributions, was used to generate predictive models. Slope, human population density, normalized difference vegetation index, distance to roads and elevation best estimated Culex distributions by a generalized additive model with an area under the curve (AUC) value of 0.791. Mean radiation, human population density, normalized difference vegetation index, distance to roads and mean temperature resulted in the highest AUC (0.708) value in a random forest model for Stegomyia distributions. We conclude that in the process towards more detailed species-level maps, with our study results, general assumptions can be made about the distribution areas of Culex and Stegomyia mosquitoes in the Taita Hills and the factors which influence their distribution. |
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| Keywords: | Species distribution modeling Vector-borne disease GIS Predictive mapping Mosquito biomod2 |
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