Optimal control of a kinetic model describing social interactions on a graph

TL;DR

This paper develops an optimal control framework for a kinetic model of social interactions on a graph, focusing on minimizing disease spread through mobility and interaction controls, with applications to epidemic management.

Contribution

It introduces a novel optimal control approach for kinetic models on graphs, analyzing different interaction processes and their impact on disease eradication strategies.

Findings

Controlling both mobility and interactions can effectively reduce viral load.

Eradication of disease is possible with appropriate controls in the second interaction model.

High control costs are associated with stopping disease spread in the first model.

Abstract

In this paper we introduce the optimal control of a kinetic model describing agents who migrate on a graph and interact within its nodes exchanging a physical quantity. As a prototype model, we consider the spread of an infectious disease on a graph, so that the exchanged quantity is the viral-load. The control, exerted on both the mobility and on the interactions separately, aims at minimising the average macroscopic viral-load. We prove that minimising the average viral-load weighted by the mass in each node is the most effective and convenient strategy. We consider two different interactions: in the first one the infection (gain) and the healing (loss) processes happen within the same interaction, while in the second case the infection and healing result from two different processes. With the appropriate controls, we prove that in the first case it is possible to stop the increase of the disease, but paying a very high cost in terms of control, while in the second case it is possible to eradicate the disease. We test numerically the role of each intervention and the interplay between the mobility and the interaction control strategies in each model.