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Related Experiment Video

Updated: Sep 24, 2025

Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses

Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses

Published on: November 10, 2023

Algorithmic fairness in pandemic forecasting: lessons from COVID-19.

Thomas C Tsai^1,2, Sercan Arik³, Benjamin H Jacobson^4,5

¹Department of Health Policy and Management, Harvard T.H. Chan School of Public Health, Boston, MA, USA. ttsai@bwh.harvard.edu.

NPJ Digital Medicine

|May 10, 2022

Summary

This summary is machine-generated.

Bias in COVID-19 forecasting models can worsen health disparities for minority groups. Addressing data inequities and implementing fairness strategies are crucial for equitable machine learning and public health innovation.

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Last Updated: Sep 24, 2025

Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses

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Published on: November 10, 2023

An R-Based Landscape Validation of a Competing Risk Model

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Published on: September 16, 2022

Large-Scale SARS-CoV-2 Testing Utilizing Saliva and Transposition Sample Pooling

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Published on: June 23, 2022

Area of Science:

Public Health
Epidemiology
Machine Learning

Background:

Racial and ethnic minorities disproportionately impacted by COVID-19.
Growing need for fairness in public health forecasting models.
Data inequities can perpetuate disparities in model predictions.

Purpose of the Study:

Outline key modeling domains for introducing unfairness.
Illustrate challenges using the Google-Harvard COVID-19 Public Forecasting model.
Offer strategies for bias mitigation in pandemic forecasting.

Main Methods:

Statistical and sociological analysis of data biases.
Development and testing of hierarchical models with limited data.
Examining structural inequities reflected in data.

Main Results:

Identified domains where bias can be introduced in forecasting models.
Demonstrated challenges in building equitable models.
Proposed strategies for fairness in machine learning.

Conclusions:

Fairness in forecasting is critical to mitigate pandemic harms.
Addressing data bias is essential for equitable machine learning.
Considerations for equitable innovation in public health modeling.