Aggregating human judgment probabilistic predictions of the safety, efficacy, and timing of a COVID-19 vaccine
Vaccine·
- DOI
- 10.1016/j.vaccine.2022.02.054
- PMID
- 35292162
- PMCID
- PMC8882426
- OpenAlex
- W4214519304
- Study type
- Journal article
- Publisher
- Elsevier BV
- Article type
- journal-article
- Integrity
- current
Publication version
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Open linked preprint →Why this research matters now
The authors suggest linear-pool aggregation of expert and forecaster judgments may be a fast, versatile tool to support situational awareness for public health officials and to communicate vaccine risks, benefits, and timing to the public.
Structured evidence summary
Research question
The study examined whether aggregating probabilistic predictions from trained forecasters and subject-matter experts could quantify uncertainty around the safety, efficacy, and approval timing of a COVID-19 vaccine during mid-2020.
Study design
Forecasters and experts were solicited to provide monthly probabilistic predictions between July and September 2020; these judgments were combined using a linear-pool aggregation method.
Population and setting
Trained forecasters plus experts in vaccinology and infectious disease produced predictions about SARS-CoV-2 vaccine attributes and FDA approval timing in 2020.
Main findings
A linear pool of human judgment predictions quantified uncertainty in clinical significance and timing despite sparse historical data. It accurately predicted the FDA approval date but underestimated both the speed of development and the eventual high efficacy of approved vaccines. The aggregated pool consistently performed above the median of the most accurate individual forecasts.
Public-health relevance
The authors suggest linear-pool aggregation of expert and forecaster judgments may be a fast, versatile tool to support situational awareness for public health officials and to communicate vaccine risks, benefits, and timing to the public.
Important limitations
The forecasters and experts underestimated the speed of vaccine development and the high efficacy of approved vaccines. The summary is limited to the supplied single-article abstract and metadata; decision-grade interpretation requires the original paper.
GIDS interpretation
The article is indexed under COVID-19 vaccination and vaccine effectiveness topics; it relates to discoverability of forecasting methods for vaccine timelines rather than to a specific surveillance signal, and no surveillance confirmation is implied.
Related GIDS surveillance
Literature context does not validate, explain, or change a surveillance signal. Exact and contextual relationships are shown separately.
Evidence relationships
This article has 12 auditable classifier relationships to diseases, places, topics, and study design.