Nash epidemics

TL;DR

This paper analyzes Nash equilibria in epidemic models where individuals choose social distancing based on infection levels, revealing key relationships between behavior, infection peaks, and costs, aiding policy design.

Contribution

It provides a new analytic solution linking social distancing behavior to infection levels and derives scaling laws for epidemic outcomes based on behavioral costs.

Findings

Derived a simple relation between social distancing and infection levels.

Established scaling laws for infection peak and total cases.

Identified characteristic costs dividing behavioral response regimes.

Abstract

Faced with a dangerous epidemic humans will spontaneously social distance to reduce their risk of infection at a socio-economic cost. Compartmentalised epidemic models have been extended to include this endogenous decision making: Individuals choose their behaviour to optimise a utility function, self-consistently giving rise to population behaviour. Here we study the properties of the resulting Nash equilibria, in which no member of the population can gain an advantage by unilaterally adopting different behaviour. We leverage a new analytic solution to obtain, (1) a simple relationship between rational social distancing behaviour and the current number of infections; (2) new scaling results for how the infection peak and number of total cases depend on the cost of contracting the disease; (3) characteristic infection costs that divide regimes of strong and weak behavioural response and depend only on the basic reproduction number of the disease; (4) a closed form expression for the value of the utility. We discuss how these analytic results provide a deep and intuitive understanding into the disease dynamics, useful for both individuals and policymakers. In particular the relationship between social distancing and infections represents a heuristic that could be communicated to the population to encourage, or "bootstrap", rational behaviour.