Use of temperature to improve West Nile virus forecasts

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 ba...

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Bibliographic Details
Published in:PLoS computational biology 2018-03, Vol.14 (3), p.e1006047
Main Authors: DeFelice, Nicholas B, Schneider, Zachary D, Little, Eliza, Barker, Christopher, Caillouet, Kevin A, Campbell, Scott R, Damian, Dan, Irwin, Patrick, Jones, Herff M P, Townsend, John, Shaman, Jeffrey
Format: Article
Language:English
Subjects:
Accuracy
Animals
Aquatic insects
Arthropods
Atmospheric temperature
Biology and life sciences
Control programs
Culicidae
Data collection
Data transmission
Decision making
Disease control
Disease Outbreaks
Disease transmission
Earth Sciences
Ecological monitoring
Environmental health
Epidemics
Epidemiology
Forecasting - methods
Health aspects
Health sciences
Health services
Humans
Infections
Infectious diseases
Insect control
Insect Vectors - virology
Mathematical models
Medical laboratories
Medicine and health sciences
Model accuracy
Models, Theoretical
Mosquito Control - trends
Mosquitoes
Outbreaks
Physical Sciences
Prevalence studies (Epidemiology)
Public health
Public Health - trends
Research and Analysis Methods
Retrospective Studies
Seasons
Software
Statistics
Temperature
Temperature effects
Viruses
Weather forecasting
West Nile Fever - epidemiology
West Nile Fever - prevention & control
West Nile Fever - transmission
West Nile virus
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Summary: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.
ISSN:1553-7358
1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1006047