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Activity-driven network modeling and control of the spread of two concurrent epidemic strains

The emergency generated by the current COVID-19 pandemic has claimed millions of lives worldwide. There have been multiple waves across the globe that emerged as a result of new variants, due to arising from unavoidable mutations. The existing network toolbox to study epidemic spreading cannot be re...

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Autores principales: Burbano Lombana, Daniel Alberto, Zino, Lorenzo, Butail, Sachit, Caroppo, Emanuele, Jiang, Zhong-Ping, Rizzo, Alessandro, Porfiri, Maurizio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9514203/
https://www.ncbi.nlm.nih.gov/pubmed/36186912
http://dx.doi.org/10.1007/s41109-022-00507-6
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author Burbano Lombana, Daniel Alberto
Zino, Lorenzo
Butail, Sachit
Caroppo, Emanuele
Jiang, Zhong-Ping
Rizzo, Alessandro
Porfiri, Maurizio
author_facet Burbano Lombana, Daniel Alberto
Zino, Lorenzo
Butail, Sachit
Caroppo, Emanuele
Jiang, Zhong-Ping
Rizzo, Alessandro
Porfiri, Maurizio
author_sort Burbano Lombana, Daniel Alberto
collection PubMed
description The emergency generated by the current COVID-19 pandemic has claimed millions of lives worldwide. There have been multiple waves across the globe that emerged as a result of new variants, due to arising from unavoidable mutations. The existing network toolbox to study epidemic spreading cannot be readily adapted to the study of multiple, coexisting strains. In this context, particularly lacking are models that could elucidate re-infection with the same strain or a different strain—phenomena that we are seeing experiencing more and more with COVID-19. Here, we establish a novel mathematical model to study the simultaneous spreading of two strains over a class of temporal networks. We build on the classical susceptible–exposed–infectious–removed model, by incorporating additional states that account for infections and re-infections with multiple strains. The temporal network is based on the activity-driven network paradigm, which has emerged as a model of choice to study dynamic processes that unfold at a time scale comparable to the network evolution. We draw analytical insight from the dynamics of the stochastic network systems through a mean-field approach, which allows for characterizing the onset of different behavioral phenotypes (non-epidemic, epidemic, and endemic). To demonstrate the practical use of the model, we examine an intermittent stay-at-home containment strategy, in which a fraction of the population is randomly required to isolate for a fixed period of time.
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spelling pubmed-95142032022-09-28 Activity-driven network modeling and control of the spread of two concurrent epidemic strains Burbano Lombana, Daniel Alberto Zino, Lorenzo Butail, Sachit Caroppo, Emanuele Jiang, Zhong-Ping Rizzo, Alessandro Porfiri, Maurizio Appl Netw Sci Research The emergency generated by the current COVID-19 pandemic has claimed millions of lives worldwide. There have been multiple waves across the globe that emerged as a result of new variants, due to arising from unavoidable mutations. The existing network toolbox to study epidemic spreading cannot be readily adapted to the study of multiple, coexisting strains. In this context, particularly lacking are models that could elucidate re-infection with the same strain or a different strain—phenomena that we are seeing experiencing more and more with COVID-19. Here, we establish a novel mathematical model to study the simultaneous spreading of two strains over a class of temporal networks. We build on the classical susceptible–exposed–infectious–removed model, by incorporating additional states that account for infections and re-infections with multiple strains. The temporal network is based on the activity-driven network paradigm, which has emerged as a model of choice to study dynamic processes that unfold at a time scale comparable to the network evolution. We draw analytical insight from the dynamics of the stochastic network systems through a mean-field approach, which allows for characterizing the onset of different behavioral phenotypes (non-epidemic, epidemic, and endemic). To demonstrate the practical use of the model, we examine an intermittent stay-at-home containment strategy, in which a fraction of the population is randomly required to isolate for a fixed period of time. Springer International Publishing 2022-09-27 2022 /pmc/articles/PMC9514203/ /pubmed/36186912 http://dx.doi.org/10.1007/s41109-022-00507-6 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Research
Burbano Lombana, Daniel Alberto
Zino, Lorenzo
Butail, Sachit
Caroppo, Emanuele
Jiang, Zhong-Ping
Rizzo, Alessandro
Porfiri, Maurizio
Activity-driven network modeling and control of the spread of two concurrent epidemic strains
title Activity-driven network modeling and control of the spread of two concurrent epidemic strains
title_full Activity-driven network modeling and control of the spread of two concurrent epidemic strains
title_fullStr Activity-driven network modeling and control of the spread of two concurrent epidemic strains
title_full_unstemmed Activity-driven network modeling and control of the spread of two concurrent epidemic strains
title_short Activity-driven network modeling and control of the spread of two concurrent epidemic strains
title_sort activity-driven network modeling and control of the spread of two concurrent epidemic strains
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9514203/
https://www.ncbi.nlm.nih.gov/pubmed/36186912
http://dx.doi.org/10.1007/s41109-022-00507-6
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