Statistical dynamics of social distancing in SARS-CoV-2 as a differential game

TL;DR

This paper models social distancing during COVID-19 as a differential game, quantitatively analyzing its effectiveness and costs to inform public health strategies.

Contribution

It introduces a quantitative differential game framework to evaluate social distancing strategies against SARS-CoV-2.

Findings

Social distancing is generally a dominant strategy unless its costs are extremely high.

The model estimates the marginal utility of distancing based on epidemiological data.

Results support social distancing as an effective public health measure.

Abstract

The novel coronavirus SARS-CoV-2 has rapidly emerged as a significant threat to global public health, in particular because -- as is not uncommon with novel pathogens -- there is no effective pharmaceutical treatment or prophylaxis to the viral syndrome it causes. In the absence of such specific treatment modalities, the mainstay of public health response rests on non-pharmaceutical interventions (NPIs), such as social distancing. This paper contributes to the understanding of social distancing against SARS-CoV-2 by quantitatively analysing the statistical dynamics of disease propagation as a differential game, and estimating the relative costs of distancing versus not distancing, identifying marginal utility of distancing based on known population epidemiological data about SARS-CoV-2 and concluding that unless the costs of distancing vastly exceed the cost of illness per unit time, social distancing remains a dominant strategy. These findings can assist in solidly anchoring public health responses based on social distancing within a quantitative framework attesting to their effectiveness.