Abstract
Introduction:Predicting the timing and locations of future mosquito-borne disease outbreaks has the potential to improve the targeting of mosquito control and disease prevention efforts. Here, we present and evaluate prospective forecasts made prior to and during the 2016 West Nile virus (WNV) season in South Dakota, a hotspot for human WNV transmission in the United States.Methods:We used a county-level logistic regression model to predict the weekly probability of human WNV case occurrence as a function of temperature, precipitation, and an index of mosquito infection status. The model was specified and fitted using historical data from 2004-2015 and was applied in 2016 to make short-term forecasts of human WNV cases in the upcoming week as well as whole-year forecasts of WNV cases throughout the entire transmission season. These predictions were evaluated at the end of the 2016 WNV season by comparing them with spatial and temporal patterns of the human cases that occurred.Results:There was an outbreak of WNV in 2016, with a total of 167 human cases compared to only 40 in 2015. Model results were generally accurate, with an AUC of 0.856 for short-term predictions. Early-season temperature data were sufficient to predict an earlier-than-normal start to the WNV season and an above-average number of cases, but underestimated the overall case burden. Model predictions improved throughout the season as more mosquito infection data were obtained, and by the end of July the model provided a close estimate of the overall magnitude of the outbreak.Conclusions:An integrated model that included meteorological variables as well as a mosquito infection index as predictor variables accurately predicted the resurgence of WNV in South Dakota in 2016. Key areas for future research include refining the model to improve predictive skill and developing strategies to link forecasts with specific mosquito control and disease prevention activities.
Highlights
Predicting the timing and locations of future mosquito-borne disease outbreaks has the potential to improve the targeting of mosquito control and disease prevention efforts
The mosquito-borne West Nile virus (WNV) has caused more than 43,937 human cases reported to the CDC in North America since its first detection in New York State in 1999 1
Human WNV disease burden is high in the Great Plains region of the United States, in South Dakota (SD)
Summary
Predicting the timing and locations of future mosquito-borne disease outbreaks has the potential to improve the targeting of mosquito control and disease prevention efforts. We present and evaluate prospective forecasts made prior to and during the 2016 West Nile virus (WNV) season in South Dakota, a hotspot for human WNV transmission in the United States. The mosquito-borne West Nile virus (WNV) has caused more than 43,937 human cases reported to the CDC in North America since its first detection in New York State in 1999 1. Human WNV disease burden is high in the Great Plains region of the United States, in South Dakota (SD). The ability to predict human cases in advance would permit public health and mosquito control officials to allocate resources more efficiently and inform the public of its actual risk, without the risk of “message fatigue” associated with constant warnings of high danger. Gridded meteorological datasets are especially suitable for predictive efforts, as they provide complete geographic coverage, are freely accessible via online archives, and have been shown to correlate with human cases [13, 14]
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