ArticlePloS one2021
How well did experts and laypeople forecast the size of the COVID-19 pandemic?
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Assessing human judgment forecasts in the rapid spread of the mpox outbreak: insights and challenges for pandemic preparedness.BMC infectious diseases · 2026Article
- Article
- COVID-19 death toll predictions show that triggering counterfactual thinking deteriorates judgmental performance.Communications medicine · 2025Article
- Delphi method applicability in drug foresight.Substance abuse treatment, prevention, and policy · 2024Article
- Article
- Conventional and frugal methods of estimating COVID-19-related excess deaths and undercount factors.Scientific reports · 2024Article
- Mapping acceptance: micro scenarios as a dual-perspective approach for assessing public opinion and individual differences in technology perception.Frontiers in psychology · 2024Article
- Unraveling the COVID-19 hospitalization dynamics in Spain using Bayesian inference.BMC medical research methodology · 2023Article
- What happened to the predicted COVID-19-induced suicide epidemic, and why?The Australian and New Zealand journal of psychiatry · 2023Article
- Human judgement forecasting of COVID-19 in the UK.Wellcome open research · 2023Article
- Crowdsourced Perceptions of Human Behavior to Improve Computational Forecasts of US National Incident Cases of COVID-19: Survey Study.JMIR public health and surveillance · 2022Article
- Chimeric forecasting: combining probabilistic predictions from computational models and human judgment.BMC infectious diseases · 2022Article
- Comparing human and model-based forecasts of COVID-19 in Germany and Poland.PLoS computational biology · 2022Article
- An expert judgment model to predict early stages of the COVID-19 pandemic in the United States.PLoS computational biology · 2022Article
- Estimating and forecasting the burden and spread of Colombia's SARS-CoV2 first wave.Scientific reports · 2022Article
- Aggregating human judgment probabilistic predictions of the safety, efficacy, and timing of a COVID-19 vaccine.Vaccine · 2022Article
- When Do We Need Massive Computations to Perform Detailed COVID-19 Simulations?Advanced theory and simulations · 2022Article
- Complex correlates of Colombia's COVID-19 surge.Lancet regional health. Americas · 2021Review
- Overall and COVID-19-specific citation impact of highly visible COVID-19 media experts: bibliometric analysis.BMJ open · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Throughout the COVID-19 pandemic, social and traditional media have disseminated predictions from experts and nonexperts about its expected magnitude. How accurate were the predictions of 'experts'-individuals holding occupations or roles in subject-relevant fields, such as epidemiologists and statisticians-compared with those of the public? We conducted a survey in April 2020 of 140 UK experts and 2,086 UK laypersons; all were asked to make four quantitative predictions about the impact of COVID-19 by 31 Dec 2020. In addition to soliciting point estimates, we asked participants for lower and higher bounds of a range that they felt had a 75% chance of containing the true answer. Experts exhibited greater accuracy and calibration than laypersons, even when restricting the comparison to a subset of laypersons who scored in the top quartile on a numeracy test. Even so, experts substantially underestimated the ultimate extent of the pandemic, and the mean number of predictions for which the expert intervals contained the actual outcome was only 1.8 (out of 4), suggesting that experts should consider broadening the range of scenarios they consider plausible. Predictions of the public were even more inaccurate and poorly calibrated, suggesting that an important role remains for expert predictions as long as experts acknowledge their uncertainty.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.