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An investigation of safe and near-optimal strategies for prevention of Covid-19 exposure using stochastic hybrid models and machine learning()
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 th...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Authors. Published by Elsevier Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9671620/ http://dx.doi.org/10.1016/j.dajour.2022.100141 |
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author | Bilgram, Alexander Jensen, Peter G. Jørgensen, Kenneth Y. Larsen, Kim G. Mikučionis, Marius Muñiz, Marco Poulsen, Danny B. Taankvist, Peter |
author_facet | Bilgram, Alexander Jensen, Peter G. Jørgensen, Kenneth Y. Larsen, Kim G. Mikučionis, Marius Muñiz, Marco Poulsen, Danny B. Taankvist, Peter |
author_sort | Bilgram, Alexander |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9671620 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | The Authors. Published by Elsevier Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-96716202022-11-18 An investigation of safe and near-optimal strategies for prevention of Covid-19 exposure using stochastic hybrid models and machine learning() Bilgram, Alexander Jensen, Peter G. Jørgensen, Kenneth Y. Larsen, Kim G. Mikučionis, Marius Muñiz, Marco Poulsen, Danny B. Taankvist, Peter Decision Analytics Journal Article 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. The Authors. Published by Elsevier Inc. 2022-12 2022-11-17 /pmc/articles/PMC9671620/ http://dx.doi.org/10.1016/j.dajour.2022.100141 Text en © 2022 The Authors Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Bilgram, Alexander Jensen, Peter G. Jørgensen, Kenneth Y. Larsen, Kim G. Mikučionis, Marius Muñiz, Marco Poulsen, Danny B. Taankvist, Peter An investigation of safe and near-optimal strategies for prevention of Covid-19 exposure using stochastic hybrid models and machine learning() |
title | An investigation of safe and near-optimal strategies for prevention of Covid-19 exposure using stochastic hybrid models and machine learning() |
title_full | An investigation of safe and near-optimal strategies for prevention of Covid-19 exposure using stochastic hybrid models and machine learning() |
title_fullStr | An investigation of safe and near-optimal strategies for prevention of Covid-19 exposure using stochastic hybrid models and machine learning() |
title_full_unstemmed | An investigation of safe and near-optimal strategies for prevention of Covid-19 exposure using stochastic hybrid models and machine learning() |
title_short | An investigation of safe and near-optimal strategies for prevention of Covid-19 exposure using stochastic hybrid models and machine learning() |
title_sort | investigation of safe and near-optimal strategies for prevention of covid-19 exposure using stochastic hybrid models and machine learning() |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9671620/ http://dx.doi.org/10.1016/j.dajour.2022.100141 |
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