Evaluation of the 2022 West Nile virus forecasting challenge, USA.
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Abstract
BACKGROUND: West Nile virus (WNV) is the most common cause of mosquito-borne disease in the continental USA, with an average of ~1200 severe, neuroinvasive cases reported annually from 2005 to 2021 (range 386-2873). Despite this burden, efforts to forecast WNV disease to inform public health measures to reduce disease incidence have had limited success. Here, we analyze forecasts submitted to the 2022 WNV Forecasting Challenge, a follow-up to the 2020 WNV Forecasting Challenge. METHODS: Forecasting teams submitted probabilistic forecasts of annual West Nile virus neuroinvasive disease (WNND) cases for each county in the continental USA for the 2022 WNV season. We assessed the skill of team-specific forecasts, baseline forecasts, and an ensemble created from team-specific forecasts. We then characterized the impact of model characteristics and county-specific contextual factors (e.g., population) on forecast skill. RESULTS: Ensemble forecasts for 2022 anticipated a season at or below median long-term WNND incidence for nearly all (> 99%) counties. More counties reported higher case numbers than anticipated by the ensemble forecast median, but national caseload (826) was well below the 10-year median (1386). Forecast skill was highest for the ensemble forecast, though the historical negative binomial baseline model and several team-submitted forecasts had similar forecast skill. Forecasts utilizing regression-based frameworks tended to have more skill than those that did not and models using climate, mosquito surveillance, demographic, or avian data had less skill than those that did not, potentially due to overfitting. County-contextual analysis showed strong relationships with the number of years that WNND had been reported and permutation entropy (historical variability). Evaluations based on weighted interval score and logarithmic scoring metrics produced similar results. CONCLUSIONS: The relative success of the ensemble forecast, the best forecast for 2022, suggests potential gains in community ability to forecast WNV, an improvement from the 2020 Challenge. Similar to the previous challenge, however, our results indicate that skill was still limited with general underprediction despite a relative low incidence year. Potential opportunities for improvement include refining mechanistic approaches, integrating additional data sources, and considering different approaches for areas with and without previous cases.
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Acknowledgements: We first thank all who contributed to the creation of the CDC ArboNET WNV dataset, including data collection, reporting, and cleaning. We also thank all who helped to develop WNND forecasts for the 2022 West Nile Virus Forecasting Challenge, including R.R., A. Prusoki., A. Prusoka., Z.E., M.A., A.G.K., M.L., S.M., A.P.P., P.P., A.V., A.B.B.W., A.Z., K.H.S., P.A., N.D., A.K., J.Sh., R.S., A.T., J.H., L.W.C., C.S., M.E.G., M.B., S.K.M., J.Sp., M.S.J.M., C.L., and M.S.N. We extend our gratitude to the Council of State and Territorial Epidemiologists (CSTE), especially Mimi Huynh and Rebekah Mathew, for their support and administrative support. We finally thank Stanley Benjamin (National Oceanic and Atmospheric Administration, NOAA), Ben Green (NOAA), and Hunter Jones (NOAA) for their helpful ongoing dialogue on forecasting challenges and WNV prediction.
Funder: Coachella Valley Mosquito and Vector Control District
Funder: Centers for Disease Control and Prevention; doi: http://dx.doi.org/10.13039/100000030
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1756-3305
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National Institute of Environmental Health Sciences (P30ES02351)
National Institute of Child Health and Human Development (K25 HD109509-01)
National Institutes of Health (R01AI168097)
Los Alamos National Laboratory (20200682PRD1, 20200682PRD1)

