Optimal control of normalized SIMR models with vaccination and treatment

Abstract

We study a model based on the so called SIR model to control the spreading of a disease in a varying population via vaccination and treatment. Since we assume that medical treatment is not immediate we add a new compartment, M , to the SIR model. We work with the normalized version of the proposed model. For such model we consider the problem of steering the system to a specified target. We consider both a fixed time optimal control problem with L 1 cost and the minimum time problem to drive the system to the target. In contrast to the literature, we apply different techniques of optimal control to our problems of interest. Using the direct method, we first solve the fixed time problem and then proceed to validate the computed solutions using both necessary conditions and second order sufficient conditions. Noteworthy, we perform a sensitivity analysis of the solutions with respect to some parameters in the model. We also use the Hamiltonian Jacobi approach to study how the minimum time function varies with respect to perturbations of the initial conditions. Additionally, we consider a multi-objective approach to study the trade off between the minimum time and the social costs of the control of diseases. Finally, we propose the application of Model Predictive Control to deal with uncertainties of the model.

Towards Disease Eradication: Long-Term Control with Constant Vaccination Rates in the Normalized SIR Model

“…As in [5][6][7][8], our aim is not to study a specific disease or population but rather to investigate how optimal control techniques can be of help designing vaccination policies.…”

COVID-19 optimal vaccination policies: A modeling study on efficacy, natural and vaccine-induced immunity responses

“…Mainly, the literature report models that differ only in the compartmental model or functional cost. Although optimization problems in Engineering with mixed constraints—as boundary conditions or path restrictions—are routine, in Mathematical Epidemiology are uncommon [24] .…”