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A Regionally Determined Climate-Informed West Nile Virus Forecast Technique
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AbstractBackgroundWest Nile virus (WNV) infection has caused over 30,000 human cases of the severe, neuroinvasive form of the disease (West Nile virus Neuroinvasive Disease; WNND) and nearly 3,000 deaths in the U.S. since its introduction in 1999. Despite spatiotemporal variation in the impact of WNV and its known links to various climate factors, no effective nationwide WNV or WNND forecast exists.ObjectivesWe aim to produce a skillful, nationwide WNND forecast built upon regionally varying relationships between climate factors and WNND.MethodsWe examined the impact of climate conditions on annual WNND caseload for 11 ecologically meaningful regions in the U.S. The most salient climate factors were incorporated into a regionally determined nationwide WNND forecast model. We retrospectively generated forecasts from 2005-2022 using observed climate conditions and compared forecast skill against various benchmarks, including a simple, historical case-driven model. Forecast skill was assessed by weighted interval scoring.ResultsRegional, climate-informed WNND retrospective forecasts outperformed a benchmark model only informed by historical WNND case data across all regions, as well as in a nationally aggregated score (univariate: 18.1% [3.7–27.0%], bivariate: 23.9% [9.0–32.8%]). Additionally, the regional forecasts outperformed an ensemble model generated from the 2022 CDC WNV Forecasting Challenge and a parallel, county-level, regional climate-informed forecast outperformed forecasts from the same Challenge. Drought and temperature were the climate factors most consistently linked to WNND and incorporated into our forecast model.DiscussionWe show a retrospectively generated WNND forecast for the continental U.S. that considerably improved upon simple forecasts based on historical case distributions. This forecast aggregated county-level data to broader regions to boost statistical signal and capture the regionally varying influences of climate conditions on annual WNND caseload. The advances here represent a potential path toward actionable broad-scale WNV forecasts.
Cold Spring Harbor Laboratory
Title: A Regionally Determined Climate-Informed West Nile Virus Forecast Technique
Description:
AbstractBackgroundWest Nile virus (WNV) infection has caused over 30,000 human cases of the severe, neuroinvasive form of the disease (West Nile virus Neuroinvasive Disease; WNND) and nearly 3,000 deaths in the U.
S.
since its introduction in 1999.
Despite spatiotemporal variation in the impact of WNV and its known links to various climate factors, no effective nationwide WNV or WNND forecast exists.
ObjectivesWe aim to produce a skillful, nationwide WNND forecast built upon regionally varying relationships between climate factors and WNND.
MethodsWe examined the impact of climate conditions on annual WNND caseload for 11 ecologically meaningful regions in the U.
S.
The most salient climate factors were incorporated into a regionally determined nationwide WNND forecast model.
We retrospectively generated forecasts from 2005-2022 using observed climate conditions and compared forecast skill against various benchmarks, including a simple, historical case-driven model.
Forecast skill was assessed by weighted interval scoring.
ResultsRegional, climate-informed WNND retrospective forecasts outperformed a benchmark model only informed by historical WNND case data across all regions, as well as in a nationally aggregated score (univariate: 18.
1% [3.
7–27.
0%], bivariate: 23.
9% [9.
0–32.
8%]).
Additionally, the regional forecasts outperformed an ensemble model generated from the 2022 CDC WNV Forecasting Challenge and a parallel, county-level, regional climate-informed forecast outperformed forecasts from the same Challenge.
Drought and temperature were the climate factors most consistently linked to WNND and incorporated into our forecast model.
DiscussionWe show a retrospectively generated WNND forecast for the continental U.
S.
that considerably improved upon simple forecasts based on historical case distributions.
This forecast aggregated county-level data to broader regions to boost statistical signal and capture the regionally varying influences of climate conditions on annual WNND caseload.
The advances here represent a potential path toward actionable broad-scale WNV forecasts.
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