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[Preprint]. 2022 Apr 5:arXiv:2204.02466v2.

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

Allison Codi  1 , Damon Luk  1 , David Braun  1 , Juan Cambeiro  2   3 , Tamay Besiroglu  2   4 , Eva Chen  5 , Luis Enrique Urtubey de Cèsaris  5 , Paolo Bocchini  6 , Thomas McAndrew  1
Affiliations

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

Allison Codi et al. ArXiv. .

Update in

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.

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Figures

FIG. 1:
FIG. 1:
Consensus median (black dot), 25th and 75th percentiles (bottom and top of solid black bar), and the 2.5th and 97.5th (bottom and top of rectangle) for 1 through 3 week ahead predictive distributions of aggregate human judgment forecasts of weekly incident cases, hospitalizations, and deaths, cumulative first and full-dose vaccinations, and prevalence of immunity evading variants at the US national level. Predictions were submitted between Jan. 2021 and Jun. 2021. Predictions for survey 6 were made for the week starting on Jun. 27 and ending on Jul. 3. The ground truth is a solid black line or a dashed black line. Lighter rectangles correspond to higher percent error.
See this image and copyright information in PMC

References

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