Integrating Environmental Monitoring and Mosquito Surveillance to Predict Vector-borne Disease: Prospective Forecasts of a West Nile Virus Outbreak

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.

A 15 Year Evaluation of West Nile Virus in Wisconsin: Effects on Wildlife and Human Health

“…The positive association of WNV risk with increased air temperature aligns with studies in the Upper Midwestern U.S. and Ontario Province [37,[52][53][54]. However, increases in precipitation had mixed effects by species and may be more reliable with the incorporation of timing by temporal lags [54][55][56][57]. The Intergovernmental Panel on Climate Change [58] assessment reports indicate rapid and visible changes to temperature and precipitation globally.…”

Fighting the Bite during a Crisis: Capabilities of Florida Mosquito Control Districts during the COVID-19 Pandemic

“…The majority of programs also engaged in routine control of domestic mosquitoes such as Aedes species of mosquitoes that can cause Aedes-borne arboviruses like dengue virus (DENV), chikungunya virus (CHIKV), yellow fever virus (YFV), and Zika virus (ZIKV) including Culex species of mosquitoes that can cause Culex-arboviruses like SLEV and WNV. In addition, mosquito surveillance is enhanced by the existence of ongoing meteorological, climatological, and water table monitoring (26). This demonstrates that although Florida mosquito control programs have a long history and experience with the Culex-arbovirus systems, they are also capable of providing mosquito control against Culex species as evidenced by the quick mitigation of the 2016 ZIKV outbreak (27).…”

Surveillance and Diagnosis of West Nile Virus in the Face of Flavivirus Cross-Reactivity

“…In addition to state specific mosquito surveillance programs ( White et al, 2001 ; Barker et al, 2003 ; Tesh et al, 2004 ; Poh et al, 2018 ), the Centers for disease control and prevention (CDC) has established in 2000 a comprehensive and robust national surveillance data capture platform termed ArboNET in order to monitor WNV patterns in humans, mosquitoes, birds, and other animals and track the progression of WNV activity across the United States ( Hadler et al, 2015 ). The use of ArboNET, state specific arboviral surveillance systems and environmental monitoring has allowed prediction of a WNV outbreak in the Great Plains, an area with high levels of WNV circulation ( Chuang and Wimberly, 2012 ; Davis et al, 2017 ). However, despite association of WNV outbreaks in the United States with parameters such as urban and ecological habitats ( Bowden et al, 2011 ), rural irrigated landscapes ( DeGroote and Sugumaran, 2012 ), increased temperature ( Hartley et al, 2012 ), several socioeconomic factors such as housing age and community drainage patterns ( Ruiz et al, 2007 ), per capita income ( DeGroote and Sugumaran, 2012 ), and neglected swimming pool density ( Reisen et al, 2008 ; Harrigan et al, 2010 ), no models have been developed that predict how these factors combine to produce outbreaks.…”