Auditing the fairness of the US COVID-19 forecast hub’s case prediction models

The US COVID-19 Forecast Hub, a repository of COVID-19 forecasts from over 50 independent research groups, is used by the Centers for Disease Control and Prevention (CDC) for their official COVID-19 communications. As such, the Forecast Hub is a critical centralized resource to promote transparent decision making. While the Forecast Hub has provided valuable predictions focused on accuracy, there is an opportunity to evaluate model performance across social determinants such as race and urbanization level that have been known to play a role in the COVID-19 pandemic. In this paper, we carry out a comprehensive fairness analysis of the Forecast Hub model predictions and we show statistically significant diverse predictive performance across social determinants, with minority racial and ethnic groups as well as less urbanized areas often associated with higher prediction errors. We hope this work will encourage COVID-19 modelers and the CDC to report fairness metrics together with accuracy, and to reflect on the potential harms of the models on specific social groups and contexts.

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Publication Details

Journal
PLoS ONE
Published
2025-04-22
DOI
https://doi.org/10.1371/journal.pone.0319383
Citations
1
Primary Topic
COVID-19 epidemiological studies
Type
article
Field-Weighted Citation Impact
1.37

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article

Auditing the fairness of the US COVID-19 forecast hub’s case prediction models

Naman Awasthi, Saad Mohammad Abrar, Nekabari Sigalo, Daniel Smolyak et al.
1 citations
PLoS ONE
COVID-19 epidemiological studies
1.37
article

Auditing the fairness of the US COVID-19 forecast hub’s case prediction models

Naman Awasthi, Saad Mohammad Abrar, Nekabari Sigalo, Daniel Smolyak, Vanessa Martinez
article en
1 citations

Abstract

The US COVID-19 Forecast Hub, a repository of COVID-19 forecasts from over 50 independent research groups, is used by the Centers for Disease Control and Prevention (CDC) for their official COVID-19 communications. As such, the Forecast Hub is a critical centralized resource to promote transparent decision making. While the Forecast Hub has provided valuable predictions focused on accuracy, there is an opportunity to evaluate model performance across social determinants such as race and urbanization level that have been known to play a role in the COVID-19 pandemic. In this paper, we carry out a comprehensive fairness analysis of the Forecast Hub model predictions and we show statistically significant diverse predictive performance across social determinants, with minority racial and ethnic groups as well as less urbanized areas often associated with higher prediction errors. We hope this work will encourage COVID-19 modelers and the CDC to report fairness metrics together with accuracy, and to reflect on the potential harms of the models on specific social groups and contexts.

PLoS ONEVol. 20(4)
University of Maryland, College Park (US)
National Science Foundation
Sustainable cities and communities
Openalex Percentile: Top 23%
COVID-19 epidemiological studies
1.37
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Auditing the fairness of the US COVID-19 forecast hub’s case prediction models — Naman Awasthi, Saad Mohammad Abrar, et al. · PLoS ONE (2025) | TGRS Research Map | TGRS