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Residuals and Least-Squares Property

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Related Experiment Video

Updated: Dec 10, 2025

Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses

Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses

Published on: November 10, 2023

Forecasting for COVID-19 has failed.

John P A Ioannidis¹, Sally Cripps², Martin A Tanner³

¹Stanford Prevention Research Center, Department of Medicine, and Departments of Epidemiology and Population Health, of Biomedical Data Science, and of Statistics, Stanford University, and Meta-Research Innovation Center at Stanford (METRICS), Stanford, CA, USA.

International Journal of Forecasting

|September 1, 2020

Summary

This summary is machine-generated.

Epidemic forecasting has a history of failures, exacerbated by COVID-19. Addressing data, modeling, and transparency issues can improve future epidemic predictions and decision-making.

Keywords:

Bayesian models Bias COVID-19 Forecasting Hospital bed utilization Mortality SIR models Validation

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Area of Science:

Epidemiology
Public Health
Data Science

Background:

Epidemic forecasting has a poor track record, notably during the COVID-19 pandemic.
Numerous factors contribute to forecasting failures, including data quality, model assumptions, and lack of transparency.

Purpose of the Study:

To analyze the causes of epidemic forecasting failures.
To propose improvements for more reliable epidemic forecasting and decision-making.

Main Methods:

Analysis of common pitfalls in epidemic forecasting models.
Review of factors influencing forecast accuracy and impact.

Main Results:

Failures stem from issues like poor data input, flawed modeling, and insufficient consideration of epidemiological factors.
Lack of transparency, groupthink, and selective reporting also contribute significantly.

Conclusions:

Despite challenges, epidemic forecasting remains essential and can be improved.
Future efforts should focus on better data, robust modeling of predictive distributions, incorporating multiple dimensions, and continuous performance validation.