Ecological niche models for sand fly species and predicted distribution of Lutzomyia longipalpis (Diptera: Psychodidae) and visceral leishmaniasis in Bahia state, Brazil

被引:0
作者
Moara de Santana Martins Rodgers
Maria Emilia Bavia
Eduardo Oyama Lins Fonseca
Bruno Oliveira Cova
Marta Mariana Nascimento Silva
Deborah Daniela Madureira Trabuco Carneiro
Luciana Lobato Cardim
John B. Malone
机构
[1] Louisiana State University,School of Veterinary Medicine, Department of Pathobiological Sciences
[2] Universidade Federal da Bahia,Department of Preventive Veterinary Medicine
[3] Laboratorio Central de Saúde Pública da Bahia (LACEN/BA),Department of Entomology Surveillance
[4] Universidade Salvador (UNIFACS),School of Health
来源
Environmental Monitoring and Assessment | 2019年 / 191卷
关键词
Ecological niche modeling; Leishmaniasis; Sand fly; Bioclim; Vegetation index; Land surface temperature;
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摘要
Visceral leishmaniasis is a public health problem in Brazil. This disease is endemic in most of Bahia state, with increasing reports of cases in new areas. Ecological niche models (ENM) can be used as a tool for predicting potential distribution for disease, vectors, and to identify risk factors associated with their distribution. In this study, ecological niche models (ENMs) were developed for visceral leishmaniasis (VL) cases and 12 sand fly species captured in Bahia state. Sand fly data was collected monthly by CDC light traps from July 2009 to December 2012. MODIS satellite imagery was used to calculate NDVI, NDMI, and NDWI vegetation indices, MODIS day and night land surface temperature (LST), enhanced vegetation index (EVI), and 19 Bioclim variables were used to develop the ENM using the maximum entropy approach (Maxent). Mean diurnal range was the variable that most contributed to all the models for sand flies, followed by precipitation in wettest month. For Lutzomyia longipalpis (L. longipalpis), annual precipitation, precipitation in wettest quarter, precipitation in wettest month, and NDVI were the most contributing variables. For the VL model, the variables that contributed most were precipitation in wettest month, annual precipitation, LST day, and temperature seasonality. L. longipalpis was the species with the widest potential distribution in the state. The identification of risk areas and factors associated with this distribution is fundamental to prioritize resource allocation and to improve the efficacy of the state’s program for surveillance and control of VL.
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