  Social distancing as one of the main non-pharmaceutical interventions can
help slow down the spread of diseases, like in the COVID-19 pandemic. Effective
social distancing, unless enforced as drastic lockdowns and mandatory cordon
sanitaire, requires consistent strict collective adherence. However, it remains
unknown what the determinants for the resultant compliance of social distancing
and their impact on disease mitigation are. Here, we incorporate into the
epidemiological process with an evolutionary game theory model that governs the
evolution of social distancing behavior. In our model, we assume an individual
acts in their best interest and their decisions are driven by adaptive social
learning of the real-time risk of infection in comparison with the cost of
social distancing. We find interesting oscillatory dynamics of social
distancing accompanied with waves of infection. Moreover, the oscillatory
dynamics are dampened with a nontrivial dependence on model parameters
governing decision-makings and gradually cease when the cumulative infections
exceed the herd immunity. Compared to the scenario without social distancing,
we quantify the degree to which social distancing mitigates the epidemic and
its dependence on individuals' responsiveness and rationality in their behavior
changes. Our work offers new insights into leveraging human behavior in support
of pandemic response.
