Models and Surveillance Systems to Detect and Predict West Nile Virus Outbreaks

Research output: Contribution to journalReview article

2 Citations (Scopus)

Abstract

Over the past 20 yr, many models have been developed to predict risk for West Nile virus (WNV; Flaviviridae: Flavivirus) disease in the human population. These models have aided our understanding of the meteorological and land-use variables that drive spatial and temporal patterns of human disease risk. During the same period, electronic data systems have been adopted by surveillance programs across much of the United States, including a growing interest in integrated data services that preserve the autonomy and attribution of credit to originating agencies but facilitate data sharing, analysis, and visualization at local, state, and national scales. At present, nearly all predictive models have been limited to the scientific literature, with few having been implemented for use by public-health and vector-control decision makers. The current article considers the development of models for spatial patterns, early warning, and early detection of WNV over the last 20 yr and considers some possible paths toward increasing the utility of these models for guiding interventions.

Original languageEnglish (US)
Pages (from-to)1508-1515
Number of pages8
JournalJournal of medical entomology
Volume56
Issue number6
DOIs
StatePublished - Oct 28 2019

Fingerprint

West Nile virus
Disease Outbreaks
Flaviviridae
Literature
Flavivirus
Information Dissemination
monitoring
Information Systems
Public Health
vector control
Population
credit
human diseases
human population
electronics
preserves
public health
land use

Keywords

  • decision support
  • early warning
  • modeling
  • prediction
  • West Nile virus

ASJC Scopus subject areas

  • Parasitology
  • veterinary(all)
  • Insect Science
  • Infectious Diseases

Cite this

Models and Surveillance Systems to Detect and Predict West Nile Virus Outbreaks. / Barker, Christopher M.

In: Journal of medical entomology, Vol. 56, No. 6, 28.10.2019, p. 1508-1515.

Research output: Contribution to journalReview article

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