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

*Kontaktforfatter

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

2 Citationer (Scopus)
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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.

OriginalsprogEngelsk
Artikelnummer100141
TidsskriftDecision Analytics Journal
Vol/bind5
DOI
StatusUdgivet - dec. 2022

Bibliografisk note

Funding Information:
The project was funded by Poul Due Jensens Foundation, Denmark grant number [ 2020-030 ].

Publisher Copyright:
© 2022 The Authors

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