West Nile virus : forecasting models for a resurging vector-borne disease in Arizona, U.S.A. Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/db78tf38p

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  • West Nile Virus (WNV), a vector-borne disease continues to be a serious threat to public health in the United States, particularly in the Southwest region. While all the states in the U.S. experienced a decreasing trend of WNV disease in 2010, the state of Arizona experienced a sharp increase from 20 in 2009 to 166 cases the following year. This dissertation endeavored to develop forecasting models to predict future cases of disease and identify counties with increased propensity for WNV. Furthermore, this study aimed to identify environmental and economic factors that contributed to the increase in WNV cases in Maricopa County, Arizona. A spatiotemporal stochastic regression model was developed using Bayesian principles and was successful in calculating the annual mean cases of disease from 2003 to 2011 for all counties. The model was also able to predict future cases of disease by fitting historical data. The model-based inference identified counties in the southern region of Arizona as having an elevated propensity for disease compared to counties in the northern region. A Seasonal Autoregressive Integrated Moving Average (SARIMA) model was developed and effectively forecasted monthly cases of human WNV in Maricopa County, Arizona. By fitting the SARIMA model to monthly historical disease data from 2005 to 2011, the temporal model presented a decreasing trend of monthly incidence of disease for 2012. The impact of home foreclosures, climate variability, and population growth on the resurgence of human WNV disease cases in Maricopa County during the 2010 epidemic was investigated. These factors were found to have contributed to the resurgence of the disease by creating the optimal environmental conditions that allowed the amplification of mosquito populations, thus increasing the risk of disease transmission to humans. As spatiotemporal disease data become readily available, forecasting models can be an important and viable risk assessment tool for public health practitioners. Forecasting models allow the mobilization and distribution of limited resources to areas with elevated propensity for disease, and the timely deployment of intervention programs to reduce the overall risk of disease.
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