Aggregating human judgment probabilistic predictions of COVID-19 transmission, burden, and preventative measures

TL;DR

This paper demonstrates that aggregated human judgment forecasts for COVID-19 are accurate, timely, and adaptable, often surpassing computational models, and can effectively support public health decision-making during outbreaks.

Contribution

It shows that aggregated human judgment forecasts are a valuable, real-time tool for predicting COVID-19 outcomes, outperforming some computational models.

Findings

Human judgment forecasts are accurate for COVID-19

Aggregated forecasts outperform some computational models

Forecasts are timely and adaptable during outbreaks

Abstract

Aggregated human judgment forecasts for COVID-19 targets of public health importance are accurate, often outperforming computational models. Our work shows aggregated human judgment forecasts for infectious agents are timely, accurate, and adaptable, and can be used as tool to aid public health decision making during outbreaks.