Management of Mosquito-Borne Disease Risk Through Spatially Explicit Simulation Modeling

EPA Grant Number: F13D20749
Title: Management of Mosquito-Borne Disease Risk Through Spatially Explicit Simulation Modeling
Investigators: Dawson, Daniel
Institution: Towson University
EPA Project Officer: Lee, Sonja
Project Period: September 1, 2014 through September 1, 2016
Project Amount: $84,000
RFA: STAR Graduate Fellowships (2013) RFA Text |  Recipients Lists
Research Category: Academic Fellowships , Fellowship - Environmental Toxicology

Objective:

The mitigation of mosquito-borne disease risk is often reactive and frequently accomplished through wide-scale chemical application. However, environmental and anthropogenic influences on mosquito populations interact, making mosquito-borne disease risk spatially heterogeneous across a landscape. The objective of this research is to develop and test spatially explicit models of mosquito population dynamics and disease risk that consider both environmental factors and mosquitocontrol activities.

Approach:

This research will be accomplished in several stages. First, mechanistic population dynamic models for mosquito vectors will be created that explicitly consider both environmental (e.g., temperature, habitat) and anthropogenic (e.g., mosquito control) factors. Next, models will be integrated into a geographic information system (GIS) to simulate and evaluate their behavior in a spatially explicit environment. Last, these spatial models will be adapted to an actual landscape, potentially the high plains of Texas, and compared and evaluated for their reliability and predictive ability in a real-world environment.

Expected Results:

The control of mosquitoes can be costly, involving the purchase of chemicals, the operation of equipment and vehicles and many personnel hours of work. In addition to these monetary costs, there are unknown ecological costs associated with the release of pesticide chemicals into the environment. The expected results of this research are GIS-based tools that may be used by mosquito-control authorities to predictively inform their activities, so that they may be more efficient and effective at accomplishing their goals of reducing disease risk and/or nuisance problems.

Potential to Further Environmental/Human Health Protection

This research will provide guidance to mosquito-control authorities in the form of GIS-based modeling tools that will help predict where mosquito control can be applied to most effectively reduce disease risk under varying conditions, thereby protecting human health. Furthermore, by increasing the efficiency of these efforts, the use of resources and pesticides can be fine-tuned which help will reduce risks to ecological systems.

Supplemental Keywords:

disease risk, modeling, mosquitoes

Progress and Final Reports:

  • 2015
  • Final