Aggregating Human Judgment Probabilistic Predictions of Coronavirus Disease 2019 Transmission, Burden, and Preventive Measures

Allison Codi; Damon Luk; David Braun; Juan Cambeiro; Tamay Besiroglu; Eva Chen; Luis Enrique Urtubey de Cesaris; Paolo Bocchini; Thomas McAndrew

doi:10.1093/ofid/ofac354

Aggregating Human Judgment Probabilistic Predictions of Coronavirus Disease 2019 Transmission, Burden, and Preventive Measures

Open Forum Infect Dis. 2022 Jul 25;9(8):ofac354. doi: 10.1093/ofid/ofac354. eCollection 2022 Aug.

Authors

Allison Codi¹, Damon Luk¹, David Braun², Juan Cambeiro³, Tamay Besiroglu³, Eva Chen⁴, Luis Enrique Urtubey de Cesaris⁴, Paolo Bocchini⁵, Thomas McAndrew¹

Affiliations

¹ College of Health, Lehigh University, Bethlehem, Pennsylvania, USA.
² Department of Psychology, College of Arts and Sciences, Lehigh University, Bethlehem, Pennsylvania, USA.
³ Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, USA.
⁴ Good Judgment Inc, New York, New York, USA.
⁵ Department of Civil and Environmental Engineering, P.C. Rossin College of Engineering and Applied Science, Lehigh University, Bethlehem, Pennsylvania, USA.

Abstract

Aggregated human judgment forecasts for coronavirus disease 2019 (COVID-19) targets of public health importance are accurate, often outperforming computational models. Our work shows that aggregated human judgment forecasts for infectious agents are timely, accurate, and adaptable, and can be used as a tool to aid public health decision making during outbreaks.

Keywords: coronavirus disease 2019; forecasting; human judgment.

Grants and funding

U24 GM132013/GM/NIGMS NIH HHS/United States