Abstract
The increase in mosquito populations following extreme weather events poses a major threat to humans because of mosquitoes’ ability to carry disease-causing pathogens, particularly in low-lying, poorly drained coastal plains vulnerable to tropical cyclones. In areas with reservoirs of disease, mosquito abundance information can help to identify the areas at higher risk of disease transmission. Using a Geographic Information System (GIS), mosquito abundance is predicted across the City of Chesapeake, Virginia. The mosquito abundance model uses mosquito light trap counts, a habitat suitability model, and dynamic environmental variables (temperature and precipitation) to predict the abundance of the species Culiseta melanura, as well as the combined abundance of the ephemeral species, Aedes vexans and Psorophora columbiae, for the year 2003. Remote sensing techniques were used to quantify environmental variables for a potential habitat suitability index for the mosquito species. The goal of this study was to produce an abundance model that could guide risk assessment, surveillance, and potential disease transmission. Results highlight the utility of integrating field surveillance, remote sensing for synoptic landscape habitat distributions, and dynamic environmental data for predicting mosquito vector abundance across low-lying coastal plains. Limitations of mosquito trapping and multi-source geospatial environmental data are highlighted for future spatial modeling of disease transmission risk.
Highlights
Vector-borne diseases such as those transmitted by mosquitoes, contribute significantly to the total disease burden in developing countries
Provided there is a disease reservoir population, this can lead to an increase in vector-borne disease transmission such as Eastern Equine Encephalitis (EEE) and West Nile Virus (WNV)
This study has addressed this shortcoming by predicting mosquito abundance both spatially and temporally
Summary
Vector-borne diseases such as those transmitted by mosquitoes, contribute significantly to the total disease burden in developing countries. The increase in mosquito populations following extreme weather events poses a major threat to humans due to mosquitoes’ ability to carry disease-causing pathogens. Environmental conditions such as increased rainfall and higher temperatures can lead to an increase in mosquito populations, commonly referred to as ‘blooms’. Provided there is a disease reservoir population (e.g., birds), this can lead to an increase in vector-borne disease transmission such as Eastern Equine Encephalitis (EEE) and West Nile Virus (WNV). These diseases commonly increase following extreme weather events such as hurricanes and tropical storms [1]. In order to prevent the spread of disease, it is advantageous to predict vector abundance, both spatially and temporally
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