Spatial Modeling of the Risk of Mosquito-borne Disease Transmission, Chesapeake, Virginia

The increase in mosquito populations following extreme weather events poses a major threat to humans because of mosquitoes' ability to carry disease-causing pathogens. 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 trap counts, habitat suitability, and environmental variables to predict the abundance of the species Culiseta melanura, as well as the combined abundance of Aedes vexans and Psorophora columbiae, for the year 2003. The mosquito abundance values are compared to vulnerable population indices to determine the spatial distribution of risk of disease transmission. The goal of this project is to create a reproducible model that could be embedded in a decision support system to aid in detecting areas at high risk of mosquito-borne disease transmission.  M.A

Remote sensing and modeling of mosquito abundance and habitats in Coastal Virginia, USA

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

Spatial Modeling of the Risk of Mosquito-borne Disease Transmission Chesapeake Virginia

The increase in mosquito populations following extreme weather events poses a major threat to humans because of mosquitoes' ability to carry disease-causing pathogens. 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 trap counts habitat suitability and environmental variables to predict the abundance of the species Culiseta melanura as well as the combined abundance of Aedes vexans and Psorophora columbiae for the year 2003. The mosquito abundance values are compared to vulnerable population indices to determine the spatial distribution of risk of disease transmission. The goal of this project is to create a reproducible model that could be embedded in a decision support system to aid in detecting areas at high risk of mosquito-borne disease transmission.

Remote sensing and modeling of mosquito abundance and habitats in Coastal Virginia, USA

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