Kelly Helm Smith https://orcid.org/0000-0001-5229-984X
Michael J. Hayes https://orcid.org/0000-0001-5006-166X
Date of this Version
Smith, K. H., Tyre, A. J., Hamik, J., Hayes, M. J., Zhou, Y., & Dai, L. (2020). Using climate to explain and predict West Nile Virus risk in Nebraska. GeoHealth, 4, e2020GH000244. https:// doi.org/10.1029/2020GH000244
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.
Plain Language Summary We wanted to see whether we could predict years with higher risk of West Nile Virus in Nebraska using publicly available data on temperature, precipitation, human cases, and population. We used a type of model that lets the data speak for itself, identifying which intervals of time leading up to an infection season are most important. We found that a dry year following a wet year, often in combination with warm temperatures, increased the likelihood of infection. Drought accounted for about 26% of the number of cases from 2002 to 2018.
Key Points: • A dry year preceded by a wet year in combination with warm temperatures increases the number of human cases of West Nile Virus in Nebraska • We found that drought accounted for 26% of WNV cases 2002–2018, and drought and temperature together accounted for 45% of cases