ArticleGeoHealth2020
Using Climate to Explain and Predict West Nile Virus Risk in Nebraska.
Article in GeoHealth, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- An Explainable Machine Learning Framework for Predicting West Nile Virus and Uncovering Key Transmission Drivers.GeoHealth · 2026Article
- Accounting for spatial variation in weather factors predicts spatial variations in Culex quinquefasciatus abundance in the Desert Southwest.Parasites & vectors · 2026Article
- Evaluating Associations Between Drought and West Nile Virus Epidemics: A Systematic Review.Microorganisms · 2025Review
- Accounting for spatial variation in climatic factors predicts spatial variations in mosquito abundance in the desert southwest.Research square · 2025Article
- Integrating Wind Speed Into Climate-Based West Nile Virus Models: A Comparative Analysis in Two Distinct Regions.GeoHealth · 2025Article
- A real-time forecasting and estimating system of West Nile virus: a case study of the 2023 WNV outbreak in Colorado, USA.Royal Society open science · 2024Article
- Existing Challenges and Opportunities for Advancing Drought and Health Research.Current environmental health reports · 2024Review
- Impact of climate change on the global circulation of West Nile virus and adaptation responses: a scoping review.Infectious diseases of poverty · 2024Article
- Multi-Model Prediction of West Nile Virus Neuroinvasive Disease With Machine Learning for Identification of Important Regional Climatic Drivers.GeoHealth · 2023Article
- Assessing the Influence of Climate on the Spatial Pattern of West Nile Virus Incidence in the United States.Environmental health perspectives · 2023Article
- Article
- The utility of a Bayesian predictive model to forecast neuroinvasive West Nile virus disease in the United States of America, 2022.PloS one · 2023Article
- Integrated Forecasts Based on Public Health Surveillance and Meteorological Data Predict West Nile Virus in a High-Risk Region of North America.Environmental health perspectives · 2022Article
- A proposed framework for the development and qualitative evaluation of West Nile virus models and their application to local public health decision-making.PLoS neglected tropical diseases · 2021Review
- West Nile Virus in the State of Ceará, Northeast Brazil.Microorganisms · 2021Article
- Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We used monthly precipitation and temperature data to give early warning of years with higher West Nile Virus (WNV) risk in Nebraska. We used generalized additive models with a negative binomial distribution and smoothing curves to identify combinations of extremes and timing that had the most influence, experimenting with all combinations of temperature and drought data, lagged by 12, 18, 24, 30, and 36 months. We fit models on data from 2002 through 2011, used Akaike's Information Criterion (AIC) to select the best-fitting model, and used 2012 as out-of-sample data for prediction, and repeated this process for each successive year, ending with fitting models on 2002-2017 data and using 2018 for out-of-sample prediction. We found that warm temperatures and a dry year preceded by a wet year were the strongest predictors of cases of WNV. Our models did significantly better than random chance and better than an annual persistence naïve model at predicting which counties would have cases. Exploring different scenarios, the model predicted that without drought, there would have been 26% fewer cases of WNV in Nebraska through 2018; without warm temperatures, 29% fewer; and with neither drought nor warmth, 45% fewer. This method for assessing the influence of different combinations of extremes at different time intervals is likely applicable to diseases other than West Nile, and to other annual outcome variables such as crop yield.
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