Abstract

West Nile virus (WNV) is the most common arthropod-borne virus (arbovirus) in the United States (US) and is the leading cause of viral encephalitis in the country. The virus has affected tens of thousands of US persons total since its 1999 North America introduction, with thousands of new infections reported annually. Approximately 1% of humans infected with WNV acquire neuroinvasive West Nile Disease (WND) with severe encephalitis and risk of death. Research describing WNV ecology is needed to improve public health surveillance, monitoring, and risk assessment. We applied Bayesian joint-spatiotemporal modeling to assess the association of vector surveillance data, host species richness, and a variety of other environmental and socioeconomic disease risk factors with neuroinvasive WND throughout the conterminous US. Our research revealed that an aging human population was the strongest disease indicator, but climatic and vector-host biotic interactions were also significant in determining risk of neuroinvasive WND. Our analysis also identified a geographic region of disproportionately high neuroinvasive WND disease risk that parallels the Continental Divide, and extends southward from the US–Canada border in the states of Montana, North Dakota, and Wisconsin to the US–Mexico border in western Texas. Our results aid in unraveling complex WNV ecology and can be applied to prioritize disease surveillance locations and risk assessment.

Highlights

  • West Nile virus (WNV) is the most common arthropod borne virus in the United States (US) and is the leading cause of viral encephalitis in the country [1].Since the first US identification in New York of 1999 [2], WNV has spread from Canada to South America, and autochthonous transmission has occurred in every state in the continental US [1,3]

  • Comparison of parsimony metrics indicated that spatiotemporal random effects (Model2) explain more of the observed disease variation than environmental covariates alone (Model1)

  • Central to our methodology was integrated modeling of neuroinvasive cases with those reported for non-neuroinvasive West Nile Disease (WND)

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Summary

Introduction

Since the first US identification in New York of 1999 [2], WNV has spread from Canada to South America, and autochthonous transmission has occurred in every state in the continental US [1,3]. During this period, West Nile Disease (WND) has affected tens of thousands of US persons, with thousands of new cases continuing to be reported annually [4]. Due to the high incidence of asymptomatic cases and uneven testing across the US, tracking and forecasting WNV occurrence and WND spatial and temporal spread has been problematic [9]

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