An expert judgment model to predict early stages of the COVID-19 pandemic in the United States

• Published in: PLoS computational biology Vol. 18; no. 9; p. e1010485
• Main Authors: McAndrew, Thomas, Reich, Nicholas G
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.09.2022; Public Library of Science (PLoS)
• Subjects: Assessments; Biology and Life Sciences; Computer applications; Consent; Coronaviruses; COVID-19; COVID-19 - epidemiology; Crowdsourcing; Decision theory; Disease Outbreaks; Disease transmission; Epidemics; Estimates; Expert systems; Forecasting; Humans; Infectious diseases; Judgment; Mathematical models; Medical research; Medicine and Health Sciences; Pandemics; Physical Sciences; Predictions; Public health; Research and Analysis Methods; Severe acute respiratory syndrome coronavirus 2; Trajectory analysis; United States; United States - epidemiology; Viral diseases; Virtual communities; United States; United States > US; Washington (state)
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1010485

From February to May 2020, experts in the modeling of infectious disease provided quantitative predictions and estimates of trends in the emerging COVID-19 pandemic in a series of 13 surveys. Data on existing transmission patterns were sparse when the pandemic began, but experts synthesized information available to them to provide quantitative, judgment-based assessments of the current and future state of the pandemic. We aggregated expert predictions into a single "linear pool" by taking an equally weighted average of their probabilistic statements. At a time when few computational models made public estimates or predictions about the pandemic, expert judgment provided (a) falsifiable predictions of short- and long-term pandemic outcomes related to reported COVID-19 cases, hospitalizations, and deaths, (b) estimates of latent viral transmission, and (c) counterfactual assessments of pandemic trajectories under different scenarios. The linear pool approach of aggregating expert predictions provided more consistently accurate predictions than any individual expert, although the predictive accuracy of a linear pool rarely provided the most accurate prediction. This work highlights the importance that an expert linear pool could play in flexibly assessing a wide array of risks early in future emerging outbreaks, especially in settings where available data cannot yet support data-driven computational modeling.