The Dual Spread of Disease and Social Norms. Modeling Social Networks and Mechanisms of Stay-at-Home Orders during the Covid-19 Pandemic.

This study examines mechanisms of public compliance with stay-at-home orders implemented in response to the Covid-19 pandemic. Scholars has proposed various hypothesis to explain compliance patterns as the result of media coverage, public policies enforcement and civic capital. However, they fail to explain why some countries such as France had high level of compliance while having low levels of trust in policy makers and medias. We hypothesize that, through their daily interactions, individuals have contributed significantly to the emergence and reinforcement adherence to stay-at-home orders through social control. Using the french 2020 spring lockdown as an example, this article aims at answering to what extent social networks and interactions have an impact on the dynamics of compliance with public health policies.

Using data from Google Community Mobility Reports and social surveys conducted in France, we first perform a statistical analysis to find evidence of the existence of such mechanisms. Then, we develop a counterfactual reasoning, using data-driven agent-based simulations to explore compliance mechanisms by systematically comparing model outputs with the actual lockdown compliance curve. Our results reveal significant variation in compliance levels resulting from social interactions, showing that individual compliance to lockdown cannot be properly understood without taking into account social networks and interactions. Our model allows us to better interpret compliance with public health measures as the result of the combined effect of policies, media coverage and social interactions.