Increasing Uptake of Social Distancing during COVID-19: Machine Learning Strategies for Targeted Interventions
27 Pages Posted: 22 May 2020 Last revised: 24 Oct 2020
Date Written: May 15, 2020
Social distancing emerged as one of the early critical non-pharmaceutical interventions to fighting the spread of COVID-19. However, in the US, this behavior was not universally adopted. In late March of 2020, we surveyed 2,500 US respondents to better understand the drivers of social distancing behavior. Our survey measured demographics, social distancing awareness, beliefs, barriers, and behaviors. We first used predictive modeling to identify a broad set of factors correlated with social distancing. However, it could not reveal which variables were the critical causal drivers of this behavior. We used Bayesian network (BN) to map the causal relationships between variables. Our BN pinpointed which variables were causal drivers of social distancing intentions and behavior: higher financial security, higher information seeking, and higher worry about the coronavirus. The BN cast doubt on the effectiveness of potential interventions that would have been suggested by the predictive model alone, such as interventions on community norms perceptions, as well as factors that have previously received attention in the media, such as religion and political affiliation. Finally, to more easily identify target groups for policy recommendations, we performed K-means clustering that distinguished population segments based on social distancing beliefs and behavior. We identified four segments ranging from a 'worried social distancers' (55.3% always social distanced), to 'uninformed skeptics' (25.9% always practiced). Taken together, our results demonstrate how a precision public health approach can help policymakers design more targeted and efficient public health interventions for social distancing. This approach can help prioritize messages most effective for matched population targets, increasing desirable outcomes while potentially saving resources.
Note: Funding: The authors received no specific funding for this work.
Conflict of Interest: Authors declare no competing interests.
Ethical Approval: This research was approved by Surgo Foundation based on the following criteria: this research (1) presents no more than minimal risk, (2) is a benign online survey of opinions and self-reported behaviors, and (3) the participants cannot be identified, either in the aggregate data reported in the manuscript or from the raw data. All ethical guidelines were followed in accordance with the Helsinki Declaration of 1975, as revised in 1983. Informed consent was obtained from all participants and no private identifying information was collected.
Keywords: social distancing, COVID-19, coronavirus, public health, behavioral science, segmentation, cluster analysis, causal Bayesian network
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