This paper is in the following e-collection/theme issue:

OJPHI Theme Issue: Opportunities and Challenges in the Application of AI in Public Health Informatics (1) Clinical Information and Decision Making (1997) Statistical Methods for Surveillance and Population Health (193) Outbreak and Pandemic Preparedness and Management (1836) Machine Learning (2163) Artificial Intelligence, Machine Learning, and Natural Language Processing for Public Health (93) Systemic Racism and Racial Bias in Health Care and Society (131) JMIR Theme Issue: COVID-19 Special Issue (2559)

Published on in Vol 18 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/78235, first published .
Fairness Correction in COVID-19 Predictive Models Using Demographic Optimization: Algorithm Development and Validation Study

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

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

Authors of this article:

Naman Awasthi1 Author Orcid Image ;   Saad Abrar1 Author Orcid Image ;   Daniel Smolyak1 Author Orcid Image ;   Vanessa Frias-Martinez2 Author Orcid Image
Article Authors Cited by Tweetations Metrics

Naman Awasthi   1 , MS ;   Saad Abrar   1 , MS ;   Daniel Smolyak   1 , PhD ;   Vanessa Frias-Martinez   2 , PhD

1 Department of Computer Science, University of Maryland, College Park, MD, United States

2 College of Information Studies, University of Maryland, College Park, MD, United States

Corresponding Author:

  • Naman Awasthi, MS
  • Department of Computer Science
  • University of Maryland
  • 8125 Paint Branch Ave
  • College Park, MD 20742
  • United States
  • Phone: 1 2402806921
  • Email: nawasthi@umd.edu