An investigation of safe and near-optimal strategies for prevention of Covid-19 exposure using stochastic hybrid models and machine learning

Alexander Bilgram, Peter G. Jensen, Kenneth Y. Jørgensen, Kim G. Larsen, Marius Mikučionis, Marco Muñiz*, Danny B. Poulsen, Peter Taankvist

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

2 Citations (Scopus)
51 Downloads (Pure)

Abstract

In this work investigate the use of stochastic hybrid models, statistical model checking and machine learning to analyze, predict and control the rapid spreading of Covid-19. During the pandemic numerous studies using stochastic models have been produced. Most of these studies are used to predict the effect of some restrictions. In contrast, in this paper we focus on the synthesis of strategies which prevent Covid-19 spreading. The computed strategies provide valuable information which can be used by the authorities to design new and more specific restrictions. We consider two large case studies that develop in the Copenhagen area in Denmark. Our experiments show that the computed strategies significantly prevent Covid-19 spreading, and thus provide valuable information e.g. expected social distance to minimize Covid-19 spreading. On the technical side, we demonstrate the applicability of analytical methods for preventing the spreading of Covid-19 in large scenarios.

Original languageEnglish
Article number100141
JournalDecision Analytics Journal
Volume5
DOIs
Publication statusPublished - Dec 2022

Bibliographical note

Publisher Copyright:
© 2022 The Authors

Keywords

  • Covid-19
  • Machine learning
  • Statistical analysis
  • Stochastic hybrid models
  • Strategy synthesis

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