Crowdsourced perceptions and COVID-19: Improving computational forecasts of US national incident cases of COVID-19 with crowdsourced perceptions of human behavior.

Abstract

Past research has shown that various signals associated with human behavior (e.g., social media engagement) can benefit computational forecasts of COVID-19. One behavior that has been shown to reduce the spread of infectious agents is compliance with non-pharmaceutical interventions (NPIs). However, the extent to which the public adheres to NPIs is difficult to measure and consequently difficult to incorporate into computational forecasts of infectious disease. Soliciting judgments from many individuals (i.e., crowdsourcing) can lead to surprisingly accurate estimates of both current and future targets of interest. Therefore, asking a crowd to estimate community-level compliance with NPIs may prove to be an accurate and predictive signal of an infectious agent, such as COVID-19. We aimed to show that crowdsourced perceptions of compliance with NPIs can be a fast, reliable signal that can predict the spread of an infectious agent. We showed this by measuring the correlation between crowdsourced perceptions of NPI and one- through four-week-ahead US incident cases of COVID-19 and evaluating whether incorporating crowdsourced perceptions improves predictive performance of a computational forecast of incident cases. 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. Self-reported state residency was compared to estimates from the U.S. census to determine representativeness of the crowds. Crowdsourced NPI signals were mapped to 21 mean perception of adherence signals-or MEPA-and analyzed descriptively to investigate features such as how MEPA signals changed over time and whether MEPA timeseries clustered into groups based on patterns of responding. We investigated whether MEPA signals were associated with one- through four-week-ahead incident cases of COVID-19 by (i) estimating correlations between MEPA and incident cases, and (ii) including MEPA into computational forecasts. The crowds were mostly geographically representative of the U.S. population with slight overrepresentation in the Northeast. MEPA signals tended to converge toward moderate levels of compliance throughout the survey period, and an unsupervised analysis revealed signals clustered into four groups roughly based on the type of question being asked. 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 social distancing, 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. 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.