Optimal control of epidemic spreading in presence of social heterogeneity

TL;DR

This paper develops an optimal control model based on social contact heterogeneity to design targeted restrictions that effectively reduce COVID-19 spread while minimizing economic impact.

Contribution

It introduces a data-driven, kinetic-type model incorporating social heterogeneity for optimizing epidemic control measures.

Findings

Selective social contact restrictions can significantly reduce infection rates.

The model effectively assesses the impact of targeted interventions.

Proposed strategies outperform uniform lockdowns in efficiency.

Abstract

The spread of COVID-19 has been thwarted in most countries through non-pharmaceutical interventions. In particular, the most effective measures in this direction have been the stay-at-home and closure strategies of businesses and schools. However, population-wide lockdowns are far from being optimal carrying heavy economic consequences. Therefore, there is nowadays a strong interest in designing more efficient restrictions. In this work, starting from a recent kinetic-type model which takes into account the heterogeneity described by the social contact of individuals, we analyze the effects of introducing an optimal control strategy into the system, to limit selectively the mean number of contacts and reduce consequently the number of infected cases. Thanks to a data-driven approach, we show that this new mathematical model permits to assess the effects of the social limitations. Finally, using the model introduced here and starting from the available data, we show the effectivity of the proposed selective measures to dampen the epidemic trends.