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Related Experiment Video

Updated: Feb 5, 2026

Dynamic Monitoring of Seroconversion using a Multianalyte Immunobead Assay for Covid-19
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Fairness Correction in COVID-19 Predictive Models Using Demographic Optimization: Algorithm Development and

Naman Awasthi1, Saad Abrar1, Daniel Smolyak1

  • 1Department of Computer Science, University of Maryland, 8125 Paint Branch Ave, College Park, MD, 20742, United States, 1 2402806921.

Online Journal of Public Health Informatics
|February 3, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces Demographic Optimization (DemOpts), a new method to improve fairness in COVID-19 case forecasting. DemOpts reduces prediction errors across racial and ethnic groups, leading to more equitable public health resource allocation.

Keywords:
COVID-19 forecastingdeep learning modelfairnessregressiontime series model

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Area of Science:

  • Epidemiology
  • Data Science
  • Public Health

Background:

  • COVID-19 forecasting models are crucial for resource allocation and intervention strategies.
  • State-of-the-art models utilize multimodal data but suffer from under-reporting and sampling biases affecting minority groups.
  • These biases lead to unfairness in COVID-19 predictions across different racial and ethnic demographics.

Purpose of the Study:

  • To introduce a novel fairness correction method for aggregate-level COVID-19 case forecasting.
  • To enhance the equity of predictive models used in public health decision-making.

Main Methods:

  • Utilized hard and soft error parity analyses to evaluate fairness frameworks.
  • Proposed and implemented Demographic Optimization (DemOpts), a debiasing method for deep learning models.
  • Tested DemOpts against existing fairness correction approaches.

Main Results:

  • Demonstrated significant differences in mean prediction errors across racial and ethnic groups in state-of-the-art COVID-19 models.
  • Showcased that DemOpts achieves superior error parity compared to other debiasing methods.
  • Confirmed that DemOpts effectively reduces disparities in mean error distributions across demographic groups.

Conclusions:

  • Demographic Optimization (DemOpts) is introduced as an effective method to reduce error parity differences.
  • DemOpts generates fairer COVID-19 forecasting models compared to existing literature approaches.
  • The method enhances the reliability of predictions for equitable public health planning.