Evidence map›Paper›PMID 40262087›Full record

ArticlePloS one2025

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

Saad Mohammad Abrar, Naman Awasthi, Daniel Smolyak, Nekabari Sigalo, Vanessa Frias Martinez

Abstract read
In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Saad Mohammad AbrarDepartment of Computer Science, University of Maryland, College Park, Maryland, United States of America.ORCID 0000-0002-3037-0016
Naman AwasthiDepartment of Computer Science, University of Maryland, College Park, Maryland, United States of America.
Daniel SmolyakDepartment of Computer Science, University of Maryland, College Park, Maryland, United States of America.
Nekabari SigaloCollege of Information and UMIACS, University of Maryland, College Park, Maryland, United States of America.
Vanessa Frias MartinezDepartment of Computer Science, University of Maryland, College Park, Maryland, United States of America.

Funding

National Science Foundation 1750102National Science Foundation 2210572
6 · The paper itself

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.

Indexed as

COVID-19Centers for Disease Control and Prevention, U.S.ForecastingHumansModels, StatisticalPandemicsSARS-CoV-2United States

Identifiers

PMID40262087
PMCPMC12014149

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.