The economic cost of social distancing during a pandemic: an optimal control approach in the SVIR model

TL;DR

This paper develops a theoretical optimal control model using the SVIR framework to balance social distancing, healthcare costs, and vaccination expenses during a pandemic, with numerical analysis based on COVID-19 data.

Contribution

It introduces a novel optimal control approach for the SVIR model incorporating economic and social costs, with explicit solutions for different social cost functions.

Findings

Optimal policies depend on the form of the social cost function.

Numerical simulations demonstrate the model's applicability to COVID-19 data.

The model provides insights into balancing health and economic impacts during pandemics.

Abstract

We devise a theoretical model for the optimal dynamical control of an infectious disease whose diffusion is described by the SVIR compartmental model. The control is realized through implementing social rules to reduce the disease's spread, which often implies substantial economic and social costs. We model this trade-off by introducing a functional depending on three terms: a social cost function, the cost supported by the healthcare system for the infected population, and the cost of the vaccination campaign. Using the Pontryagin's Maximum Principle, we give conditions for the existence of the optimal policy, which we characterize explicitly in three instances of the social cost function, the linear, quadratic, and exponential models, respectively. Finally, we present a set of results on the numerical solution of the optimally controlled system by using Italian data from the recent Covid--19 pandemic for the model calibration.