Vector Surveillance, Host Species Richness, and Demographic Factors as West Nile Disease Risk Indicators

• Published in: Viruses Vol. 13; no. 5; p. 934
• Main Authors: Humphreys, John M., Young, Katherine I., Cohnstaedt, Lee W., Hanley, Kathryn A., Peters, Debra P. C.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 18-05-2021; MDPI
• Subjects: Aging; Asymptomatic; Bayesian; Bayesian analysis; Disease; Economic indicators; Economic statistics; Encephalitis; Family income; Invasiveness; mosquito; Mosquitoes; neuroinvasive disease; Precipitation; Public health; Risk assessment; Risk factors; spatiotemporal; Species richness; Surveillance; Variables; vector; Viruses; West Nile; West Nile virus; United States > US
• ISSN: 1999-4915; 1999-4915
• DOI: 10.3390/v13050934

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.