Fairness Correction in COVID-19 Predictive Models Using Demographic Optimization: Algorithm Development and Validation Study

We introduce DemOpts, which reduces error parity differences compared with other approaches and generates fairer forecasting models compared with other approaches in the literature.

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Publication Details

Journal
Online Journal of Public Health Informatics
Published
2025-12-11
DOI
https://doi.org/10.2196/78235
Primary Topic
COVID-19 epidemiological studies
Type
article
Field-Weighted Citation Impact
0.00
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article

Fairness Correction in COVID-19 Predictive Models Using Demographic Optimization: Algorithm Development and Validation Study

Online Journal of Public Health Informatics
COVID-19 epidemiological studies
article

Fairness Correction in COVID-19 Predictive Models Using Demographic Optimization: Algorithm Development and Validation Study

article en

Abstract

We introduce DemOpts, which reduces error parity differences compared with other approaches and generates fairer forecasting models compared with other approaches in the literature.

Online Journal of Public Health InformaticsVol. 18
University of Maryland, College Park (US)
Openalex Percentile: Top 36%
COVID-19 epidemiological studies
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Fairness Correction in COVID-19 Predictive Models Using Demographic Optimization: Algorithm Development and Validation Study · Online Journal of Public Health Informatics (2025) | TGRS Research Map | TGRS