Author:
Holcomb Karen M.,Mathis Sarabeth,Staples J. Erin,Fischer Marc,Barker Christopher M.,Beard Charles B.,Nett Randall J.,Keyel Alexander C.,Marcantonio Matteo,Childs Marissa L.,Gorris Morgan E.,Rochlin Ilia,Hamins-Puértolas Marco,Ray Evan L.,Uelmen Johnny A.,DeFelice Nicholas,Freedman Andrew S.,Hollingsworth Brandon D.,Das Praachi,Osthus Dave,Humphreys John M.,Nova Nicole,Mordecai Erin A.,Cohnstaedt Lee W.,Kirk Devin,Kramer Laura D.,Harris Mallory J.,Kain Morgan P.,Reed Emily M. X.,Johansson Michael A.
Abstract
Abstract
Background
West Nile virus (WNV) is the leading cause of mosquito-borne illness in the continental USA. WNV occurrence has high spatiotemporal variation, and current approaches to targeted control of the virus are limited, making forecasting a public health priority. However, little research has been done to compare strengths and weaknesses of WNV disease forecasting approaches on the national scale. We used forecasts submitted to the 2020 WNV Forecasting Challenge, an open challenge organized by the Centers for Disease Control and Prevention, to assess the status of WNV neuroinvasive disease (WNND) prediction and identify avenues for improvement.
Methods
We performed a multi-model comparative assessment of probabilistic forecasts submitted by 15 teams for annual WNND cases in US counties for 2020 and assessed forecast accuracy, calibration, and discriminatory power. In the evaluation, we included forecasts produced by comparison models of varying complexity as benchmarks of forecast performance. We also used regression analysis to identify modeling approaches and contextual factors that were associated with forecast skill.
Results
Simple models based on historical WNND cases generally scored better than more complex models and combined higher discriminatory power with better calibration of uncertainty. Forecast skill improved across updated forecast submissions submitted during the 2020 season. Among models using additional data, inclusion of climate or human demographic data was associated with higher skill, while inclusion of mosquito or land use data was associated with lower skill. We also identified population size, extreme minimum winter temperature, and interannual variation in WNND cases as county-level characteristics associated with variation in forecast skill.
Conclusions
Historical WNND cases were strong predictors of future cases with minimal increase in skill achieved by models that included other factors. Although opportunities might exist to specifically improve predictions for areas with large populations and low or high winter temperatures, areas with high case-count variability are intrinsically more difficult to predict. Also, the prediction of outbreaks, which are outliers relative to typical case numbers, remains difficult. Further improvements to prediction could be obtained with improved calibration of forecast uncertainty and access to real-time data streams (e.g. current weather and preliminary human cases).
Graphical Abstract
Funder
University Corporation for Atmospheric Research
Centers for Disease Control and Prevention
Stanford Interdisciplinary Graduate Fellowship
Los Alamos National Laboratory
Laboratory Directed Research and Development
Director’s Postdoc Fellowship
National Institutes of Health
Stanford Data Science Scholars Program
Stanford Center for Computational, Evolutionary and Human Genomics Predoctoral Fellowship
Fogarty International Center
Stanford Woods Institute for the Environment
King Center on Global Development
Knight-Hennessey Scholars Program
Publisher
Springer Science and Business Media LLC
Subject
Infectious Diseases,Parasitology