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United States Department of Agriculture

Agricultural Research Service

Research Project: SURVEILLANCE AND ECOLOGY OF MOSQUITO, BITING AND FILTH BREEDING INSECTS

Location: Mosquito and Fly Research Unit

Title: GIS Early-Warning System for Vectors of Rift Valley Fever: Anomaly Analysis of Climate-Population Associations

Authors
item Britch, Seth
item Linthicum, Kenneth

Submitted to: California Mosquito and Vector Control Association Proceedings
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: October 15, 2007
Publication Date: November 15, 2007
Citation: Britch, S.C., Linthicum, K. 2007. Gis early-warning system for vectors of rift valley fever: anomaly analysis of climate-population associations. California Mosquito and Vector Control Association Proceedings.

Interpretive Summary: Emerging mosquito-borne pathogen threats such as Rift Valley fever are of great concern. However, there are steps we can take to strategically protect the U.S. against disease transmission, such as learning more about population dynamics of U.S. mosquitoes and the environmental factors affecting them. High quality satellite climate data and abundant records of mosquito populations exist and are being analyzed in a geographic information system (GIS). We found that examining climate and population anomalies from long-term means reveals potentially powerful predictive associations. These associations will be the driving core of a GIS designed to spatially predict unusually large populations of mosquitoes using near real-time climate data. With an understanding of environmental forces that drive the distribution, timing, and abundance of mosquitoes in the U.S. we may design GIS-based early warning systems. The project is initially focused on potential vectors of Rift Valley fever in the U.S., but since we include trap data for all mosquito species the GIS will be informative for vectors of nearly any emerging or established mosquito-borne disease threat. By modeling U.S. climate and vector population data in the context of RVF activity and movement worldwide, the GIS could flag regions at high risk of mosquito-borne pathogen transmission and more effectively guide the distribution of surveillance and control measures at critical areas of the U.S. and avert an RVF event.

Technical Abstract: A critical component of predicting the risk of transmission of mosquito-borne viruses is knowing the status of vector populations. Mosquito control agencies have good systems for measuring mosquito populations at county or district levels, but these data are not synthesized to regional or national levels – a strategic disadvantage in the face of emerging human and animal disease threats such as Rift Valley fever, dengue, or chikungunya. We are creating a Geographic Information System (GIS) of mosquito surveillance records compiled from mosquito and vector control districts and public health agencies throughout the U.S. This GIS of historic and current surveillance data will be used to develop an understanding of the effects of climate on mosquito distribution, timing, and abundance. The principal goal of the project is to provide early warning and flag areas of the U.S. at risk of unusually large mosquito populations based on historical climate-population associations. The project is initially focused on potential vectors of Rift Valley fever in the U.S., but since we include trap data for all mosquito species the GIS will be informative for vectors of nearly any emerging or established mosquito-borne disease threat. At local and national levels the spatial information product of this GIS is an important resource for the public and animal health communities, particularly in coordinating and targeting vector control, vaccines, diagnostics, and educational resources during routine, emergency, or disaster situations. By modeling U.S. vector population data in the context of RVF activity and movement worldwide, we may be able to enhance surveillance and control measures at critical areas of the U.S. and avert an RVF event. Here we discuss one early phase of the model, that of developing effective ways to compare climate and population data by calculating anomalies.

Last Modified: 4/16/2014
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