Grantee Research Project Results
An Agent-Based Model of the Spatial Dynamics of Eastern Equine Encephalitis Virus Transmission in a Swamp Forest Ecosystem
EPA Grant Number: F5F21807Title: An Agent-Based Model of the Spatial Dynamics of Eastern Equine Encephalitis Virus Transmission in a Swamp Forest Ecosystem
Investigators: Estep, Laura K.
Institution: Yale University
EPA Project Officer: Lee, Sonja
Project Period: July 1, 2005 through June 1, 2006
Project Amount: $111,172
RFA: STAR Graduate Fellowships (2005) RFA Text | Recipients Lists
Research Category: Academic Fellowships
Objective:
Recent research has indicated increasing rates of urbanization and habitat fragmentation may put mammalian populations at greater risk in the future for epidemics caused by arboviruses. Increasing knowledge of vector and host activity patterns and foraging behavior has set the stage for using use agent-base models to more accurately predict the occurrence in space and time of arboviral activity and epidemics. This research will focus of developing an agent-based model of the transmission cycle of the Eastern Equine Encephalitis (EEE) virus in the southeastern swamp forest ecosystem.
This research will add a critical spatial component to our understanding of the EEE virus. This will allow for the prediction of potential areas of outbreak of EEE in mammalian populations in the Southeast.
Approach:
Agent-based models coupled with GIS software will be used to simulate the spread of the EEE virus throughout our study site of Tuskegee National Forest (TNF) in Alabama. The Swarm modeling environment will be used in conjunction with GRASS GIS. Mosquitoes and their avian and mammalian hosts will be represented as agents in the program with specific movement and foraging patterns based upon previous research. Model outputs of the EEE transmission in the historic, current, and future fragmented TNF landscapes will be analyzed to detect factors leading to EEE outbreaks.
Expected Results:
The most important outcome of this study will be a working model of the virus in space and time that will provide critical information to local governments in the Southeastern United States regarding EEE epidemic risk under various urbanization scenarios. Additionally, the results of this study will provide insight into the applicability of this modeling approach to arboviral transmission modeling in general, and further our knowledge of the role of the landscape structure and vector and host behavior in fueling the transmission of arboviruses.