Approximate Testing in Uncertain Epidemic Processes

Xiaoqi Bi, Erik Miehling, Carolyn Beck, Tamer Basar

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Diagnostic tests have proven to be a critical tool in controlling the progression of a virus. In this paper, we formulate the testing of a homogeneous population as an optimal control problem. The population state, given by the distribution of agents' viral states in a compartmental model, is assumed to be unknown. Information regarding the population state is provided via noisy tests, which are allocated from a stockpile whose size is updated via a stochastic process. The objective of the control problem is to allocate tests so as to minimize uncertainty of the underlying population state over a finite horizon. As such, the control problem is cast as a POMDP with a negative entropy reward function. We study various heuristic policies and investigate conditions under which each heuristic performs best.

Original languageEnglish (US)
Title of host publication2022 IEEE 61st Conference on Decision and Control, CDC 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781665467612
StatePublished - 2022
Event61st IEEE Conference on Decision and Control, CDC 2022 - Cancun, Mexico
Duration: Dec 6 2022Dec 9 2022

Publication series

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


Conference61st IEEE Conference on Decision and Control, CDC 2022

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization


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