Incorporating crowdsourced perceptions of human behavior into computational forecasts of US national incident cases of COVID-19

David Braun, Daniel Ingram, David Ingram, Bilal Khan, Jessecae Marsh and Thomas McAndrew
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Jessecae Marsh: Lehigh University

No 7vrmy, OSF Preprints from Center for Open Science

Abstract: Computational forecasts of COVID-19 targets may benefit from temporal signals associated with human behavior. Non-pharmaceutical interventions (NPI) have been shown to reduce the spread of an infectious agent, but accurate information about how the general public interprets and acts upon guidelines developed by public health officials is difficult to collect. For 36 weeks from September, 2020 to April, 2021, we asked two crowds twenty one questions about their perceptions of their communities adherence to NPI and public health guidelines and collected 10,120 responses. Crowdsourced NPI signals were mapped to a mean perception of adherence—or MEPA—and included in computational forecasts. Several MEPA signals linearly correlated with one through four week ahead incident cases of COVID-19 at the US national level. Including questions related to masking, testing, and limiting large gatherings increased out of sample predictive performance for 1-3 week ahead probabilistic forecasts of incident cases of COVID-19 when compared to a model that was trained on only past incident cases. In addition, we found that MEPA signals could be clustered which suggests a more focused survey may have sufficed and provided similar performance. Crowdsourced perceptions of non-pharmaceutical adherence may be an important signal to improve forecasts of the trajectory of an infectious agent and increase public health situational awareness.

Date: 2022-05-05
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Persistent link: https://EconPapers.repec.org/RePEc:osf:osfxxx:7vrmy

DOI: 10.31219/osf.io/7vrmy

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