Optimal feedback policies in stochastic epidemic models

Giovanni Pugliese Carratelli*, Xiaodong Cheng, Kris V. Parag, Ioannis Lestas

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference paperAcademicpeer-review

Abstract

We consider the problem of finding optimal policies that mitigate the effects of an epidemic. We develop computational tools for finding such policies for broad classes of stochastic epidemic models and investigate various features of such policies. In particular, we observe that optimal policies are predominantly constant for epidemics where the mitigation measures are associated with the infected population.

Original languageEnglish
Title of host publication2024 IEEE 63rd Conference on Decision and Control, CDC 2024
PublisherIEEE
Pages7062-7066
ISBN (Electronic)9798350316339
ISBN (Print)9798350316346
DOIs
Publication statusPublished - 2024
Event63rd IEEE Conference on Decision and Control, 2024 - Milan, Italy
Duration: 16 Dec 202419 Dec 2024

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0743-1546
ISSN (Electronic)2576-2370

Conference/symposium

Conference/symposium63rd IEEE Conference on Decision and Control, 2024
Abbreviated titleCDC 2024
Country/TerritoryItaly
CityMilan
Period16/12/2419/12/24

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