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Despite the availability of medical data, environmental surveillance tools, and heightened public awareness, West Nile Virus (WNv) remains a global health hazard. Reliable methods for predicting WNv outbreaks remain elusive, and environmental health managers must take preventive actions without the benefit of simple predictive tools. The purpose of this ex post facto research was to examine the accuracy and timeliness of exogenous data in predicting outbreaks of WNv in South Carolina. Decision theory, the CYNEFIN construct, and systems theory provided the theoretical framework for this study, allowing the researcher to broaden traditional decision theory concepts with powerful system-level precepts. Using WNv as an example of decision making in complex environments, a statistical model for predicting the likelihood of the presence of WNv was developed through the exclusive use of exogenous explanatory variables (EEVs). The key research questions were focused on whether EEVs alone can predict the likelihood of WNv presence with the statistical confidence to make timely preventive resource decisions. Results indicated that publicly accessible EEVs such as average temperature, average wind speed, and average population can be used to predict the presence of WNv in a South Carolina locality 30 days prior to an incident, although they did not accurately predict incident counts higher than four. The social implications of this research can be far-reaching. The ability to predict emerging infectious diseases (EID) for which there are no vaccines or cure can provide decision makers with the ability to take pro-active measures to mitigate EID outbreaks.
Glaze, Christopher Lee, "The Power of Exogenous Variables in Predicting West Nile Virus in South Carolina" (2021). Walden Dissertations and Doctoral Studies. 11075.