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Large-deviations of disease spreading dynamics with vaccination
We numerically simulated the spread of disease for a Susceptible-Infected-Recovered (SIR) model on contact networks drawn from a small-world ensemble. We investigated the impact of two types of vaccination strategies, namely random vaccination and high-degree heuristics, on the probability density f...
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Published in: | PloS one 2023-07, Vol.18 (7), p.e0287932-e0287932 |
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description | We numerically simulated the spread of disease for a Susceptible-Infected-Recovered (SIR) model on contact networks drawn from a small-world ensemble. We investigated the impact of two types of vaccination strategies, namely random vaccination and high-degree heuristics, on the probability density function (pdf) of the cumulative number C of infected people over a large range of its support. To obtain the pdf even in the range of probabilities as small as 10-80, we applied a large-deviation approach, in particular the 1/t Wang-Landau algorithm. To study the size-dependence of the pdfs within the framework of large-deviation theory, we analyzed the empirical rate function. To find out how typical as well as extreme mild or extreme severe infection courses arise, we investigated the structures of the time series conditioned to the observed values of C. |
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We investigated the impact of two types of vaccination strategies, namely random vaccination and high-degree heuristics, on the probability density function (pdf) of the cumulative number C of infected people over a large range of its support. To obtain the pdf even in the range of probabilities as small as 10-80, we applied a large-deviation approach, in particular the 1/t Wang-Landau algorithm. To study the size-dependence of the pdfs within the framework of large-deviation theory, we analyzed the empirical rate function. To find out how typical as well as extreme mild or extreme severe infection courses arise, we investigated the structures of the time series conditioned to the observed values of C.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0287932</identifier><identifier>PMID: 37428751</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Algorithms ; Analysis ; Biology and Life Sciences ; Computer and Information Sciences ; Deviation ; Disease ; Disease susceptibility ; Disease Susceptibility - epidemiology ; Disease transmission ; Distribution (Probability theory) ; Empirical analysis ; Epidemics ; Health aspects ; Humans ; Likelihood Functions ; Medical research ; Medicine and Health Sciences ; Medicine, Experimental ; Models, Biological ; Physical Sciences ; Prevention ; Probability ; Probability density function ; Probability density functions ; Research and Analysis Methods ; Statistical physics ; Vaccination ; Vaccines</subject><ispartof>PloS one, 2023-07, Vol.18 (7), p.e0287932-e0287932</ispartof><rights>Copyright: © 2023 Feld, Hartmann. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</rights><rights>COPYRIGHT 2023 Public Library of Science</rights><rights>2023 Feld, Hartmann. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2023 Feld, Hartmann 2023 Feld, Hartmann</rights><rights>2023 Feld, Hartmann. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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We investigated the impact of two types of vaccination strategies, namely random vaccination and high-degree heuristics, on the probability density function (pdf) of the cumulative number C of infected people over a large range of its support. To obtain the pdf even in the range of probabilities as small as 10-80, we applied a large-deviation approach, in particular the 1/t Wang-Landau algorithm. To study the size-dependence of the pdfs within the framework of large-deviation theory, we analyzed the empirical rate function. 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Feld, Yannick</au><au>Hartmann, Alexander K</au><au>Pereira, Tiago</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Large-deviations of disease spreading dynamics with vaccination</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2023-07-10</date><risdate>2023</risdate><volume>18</volume><issue>7</issue><spage>e0287932</spage><epage>e0287932</epage><pages>e0287932-e0287932</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>We numerically simulated the spread of disease for a Susceptible-Infected-Recovered (SIR) model on contact networks drawn from a small-world ensemble. We investigated the impact of two types of vaccination strategies, namely random vaccination and high-degree heuristics, on the probability density function (pdf) of the cumulative number C of infected people over a large range of its support. To obtain the pdf even in the range of probabilities as small as 10-80, we applied a large-deviation approach, in particular the 1/t Wang-Landau algorithm. To study the size-dependence of the pdfs within the framework of large-deviation theory, we analyzed the empirical rate function. 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subjects | Algorithms Analysis Biology and Life Sciences Computer and Information Sciences Deviation Disease Disease susceptibility Disease Susceptibility - epidemiology Disease transmission Distribution (Probability theory) Empirical analysis Epidemics Health aspects Humans Likelihood Functions Medical research Medicine and Health Sciences Medicine, Experimental Models, Biological Physical Sciences Prevention Probability Probability density function Probability density functions Research and Analysis Methods Statistical physics Vaccination Vaccines |
title | Large-deviations of disease spreading dynamics with vaccination |
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