Use of temperature to improve West Nile virus forecasts

Nicholas B. DeFelice, Zachary D. Schneider, Eliza Little, Chris Barker, Kevin A. Caillouet, Scott R. Campbell, Dan Damian, Patrick Irwin, Herff M.P. Jones, John Townsend, Jeffrey Shaman

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

Ecological and laboratory studies have demonstrated that temperature modulates West Nile virus (WNV) transmission dynamics and spillover infection to humans. Here we explore whether inclusion of temperature forcing in a model depicting WNV transmission improves WNV forecast accuracy relative to a baseline model depicting WNV transmission without temperature forcing. Both models are optimized using a data assimilation method and two observed data streams: mosquito infection rates and reported human WNV cases. Each coupled model-inference framework is then used to generate retrospective ensemble forecasts of WNV for 110 outbreak years from among 12 geographically diverse United States counties. The temperature-forced model improves forecast accuracy for much of the outbreak season. From the end of July until the beginning of October, a timespan during which 70% of human cases are reported, the temperature-forced model generated forecasts of the total number of human cases over the next 3 weeks, total number of human cases over the season, the week with the highest percentage of infectious mosquitoes, and the peak percentage of infectious mosquitoes that on average increased absolute forecast accuracy 5%, 10%, 12%, and 6%, respectively, over the non-temperature forced baseline model. These results indicate that use of temperature forcing improves WNV forecast accuracy and provide further evidence that temperature influences rates of WNV transmission. The findings provide a foundation for implementation of a statistically rigorous system for real-time forecast of seasonal WNV outbreaks and their use as a quantitative decision support tool for public health officials and mosquito control programs.

Original languageEnglish (US)
Article numbere1006047
JournalPLoS Computational Biology
Volume14
Issue number3
DOIs
StatePublished - Mar 1 2018

Fingerprint

West Nile virus
Viruses
Virus
Forecast
Temperature
virus transmission
mosquito
temperature
Culicidae
Forcing
Disease Outbreaks
Infection
Percentage
Mosquito control
Baseline
Model
Mosquito Control
forecast
Data Assimilation
mosquito control

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Modeling and Simulation
  • Ecology
  • Molecular Biology
  • Genetics
  • Cellular and Molecular Neuroscience
  • Computational Theory and Mathematics

Cite this

DeFelice, N. B., Schneider, Z. D., Little, E., Barker, C., Caillouet, K. A., Campbell, S. R., ... Shaman, J. (2018). Use of temperature to improve West Nile virus forecasts. PLoS Computational Biology, 14(3), [e1006047]. https://doi.org/10.1371/journal.pcbi.1006047

Use of temperature to improve West Nile virus forecasts. / DeFelice, Nicholas B.; Schneider, Zachary D.; Little, Eliza; Barker, Chris; Caillouet, Kevin A.; Campbell, Scott R.; Damian, Dan; Irwin, Patrick; Jones, Herff M.P.; Townsend, John; Shaman, Jeffrey.

In: PLoS Computational Biology, Vol. 14, No. 3, e1006047, 01.03.2018.

Research output: Contribution to journalArticle

DeFelice, NB, Schneider, ZD, Little, E, Barker, C, Caillouet, KA, Campbell, SR, Damian, D, Irwin, P, Jones, HMP, Townsend, J & Shaman, J 2018, 'Use of temperature to improve West Nile virus forecasts', PLoS Computational Biology, vol. 14, no. 3, e1006047. https://doi.org/10.1371/journal.pcbi.1006047
DeFelice NB, Schneider ZD, Little E, Barker C, Caillouet KA, Campbell SR et al. Use of temperature to improve West Nile virus forecasts. PLoS Computational Biology. 2018 Mar 1;14(3). e1006047. https://doi.org/10.1371/journal.pcbi.1006047
DeFelice, Nicholas B. ; Schneider, Zachary D. ; Little, Eliza ; Barker, Chris ; Caillouet, Kevin A. ; Campbell, Scott R. ; Damian, Dan ; Irwin, Patrick ; Jones, Herff M.P. ; Townsend, John ; Shaman, Jeffrey. / Use of temperature to improve West Nile virus forecasts. In: PLoS Computational Biology. 2018 ; Vol. 14, No. 3.
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