Differential Games in the spread of Covid-19

TL;DR

This paper models Covid-19 spread as a differential game between governments and individuals, incorporating disease dynamics, asymptomatic carriers, and detection issues to analyze equilibrium behaviors and control strategies.

Contribution

It introduces a novel differential game framework for Covid-19 spread, including undetected infections and individual decision-making based on reported data.

Findings

Population behavior significantly impacts disease spread.

Detection rates and trust influence the effectiveness of control measures.

Extended model captures the delay in optimal control due to undetected cases.

Abstract

Given the ongoing Covid-19 pandemic, it is of interest to understand how the infections spread as the combined result of measures taken by central planners (governments) and individual behavior. In this work, the spread of Covid-19 is modelled as a differentiable game between the planner and population with appropriate disease spread dynamical equations. We first characterise the equilibrium dynamics of only the population with modifed Susceptible-Infected-Recovered (SIR) equations to highlight the qualitative nature of the equilbrium. Using this result, we formulate the joint equilibrium exposure profile between the planner and population. Additionally, as in case of Covid-19, the role of asymptomatic carriers, inadequacies in testing, contact tracing and quarantining can lead to a significant underestimate of the true infected numbers as compared to just the detected numbers. Therefore, it is vital to model the true infected numbers within the context of choices made by individuals within the population. To incorporate this, we extend our framework by modifying the dynamics to include additional sub-compartments of `undetected infected' and `detected infected' in the disease dynamics. The individuals make their own estimates of the total infected from the detected numbers and base their strategies on those estimates. We show that these considerations lead to a retarded optimal control problem for the players. We present some simulation results based on these results to demonstrate how population behavior, planner control, detection rates and trust in the reported numbers play a key role in how the disease spreads.