A Valley Fever (Coccidioidomycosis) Public Health Decision Support System Based on Climate and Environmental Changes

EPA Grant Number: R832754
Title: A Valley Fever (Coccidioidomycosis) Public Health Decision Support System Based on Climate and Environmental Changes
Investigators: Comrie, Andrew C. , Yool, Stephen R.
Institution: University of Arizona
EPA Project Officer: Hunt, Sherri
Project Period: December 14, 2005 through December 13, 2007 (Extended to December 13, 2009)
Project Amount: $265,004
RFA: Decision Support Systems Involving Climate Change and Public Health (2005) RFA Text |  Recipients Lists
Research Category: Global Climate Change , Health Effects , Health , Climate Change

Objective:

Valley fever (coccidioidomycosis) is a disease endemic to arid regions in the Western Hemisphere, and is caused by the soil-dwelling fungi Coccidioides immitis and Coccidioides posadasii. Arizona is currently experiencing an epidemic with almost 4000 cases in 2004, greatly exceeding other climate-related diseases such hantavirus or West Nile Virus. Previous research has indicated relationships linking temperature and precipitation to outbreaks of coccidioidomycosis. Our latest research results have identified very strong links between climate and environmental conditions and seasonal coccidioidomycosis changes. Our statistical models can explain up to 80 percent of the seasonal variability in coccidioidomycosis based on antecedent precipitation and atmospheric dust concentrations. We will develop a public health decision-support system for Arizona that capitalizes on our recent findings.

Approach:

We are collaborating with stakeholders at the Arizona Department of Health Services to put in place a set of seasonal models and associated databases that will enable anticipation of coccidioidomycosis outbreaks and improved public health actions to mitigate them. The specific tools will include seasonal coccidioidomycosis incidence forecast models for the major metropolitan areas, and integrated Geographic Information System models incorporating satellite-derived surface moisture and surface land cover disturbance maps. We will provide temporal and spatial information on historic and forecast coccidioidomycosis in tandem with the key climatic and other environmental variables controlling outbreaks of the disease. This decision support system will include validation statistics and uncertainty estimates.

Expected Results:

The package of decision support tools will become part of the management environment for coccidioidomycosis control and prevention at the Arizona Department of Health Services. The proposed work develops novel climate-based forecast models of coccidioidomycosis incidence, and it directly applies and increases the use of climate information for public health decision makers. This information will result in a range of public health responses tailored by place and time to the level of potential risk.

Publications and Presentations:

Publications have been submitted on this project: View all 29 publications for this project

Journal Articles:

Journal Articles have been submitted on this project: View all 5 journal articles for this project

Supplemental Keywords:

Air, atmosphere, global climate, exposure, risk, human health, sensitive populations, organism, elderly, particulates, pathogens, decision making, ecology, epidemiology, modeling, monitoring, climate models, satellite, Landsat, remote sensing, Southwest, West, Arizona, California, AZ, CA, Region 9,, RFA, Health, Scientific Discipline, Air, Health Risk Assessment, climate change, Air Pollution Effects, Risk Assessments, Environmental Monitoring, Ecological Risk Assessment, Atmosphere, air quality modeling, ecosystem models, decision making database tool, public health decision support system, climatic influence, modeling, human exposure, climate models, demographics, coccidioidomycosis, regional climate model, Global Climate Change, human health risk

Progress and Final Reports:

2006 Progress Report
2007 Progress Report
2008 Progress Report
Final Report