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Inferring the effect of interventions on COVID-19 transmission networks
Countries around the world implement nonpharmaceutical interventions (NPIs) to mitigate the spread of COVID-19. Design of efficient NPIs requires identification of the structure of the disease transmission network. We here identify the key parameters of the COVID-19 transmission network for time per...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8578219/ https://www.ncbi.nlm.nih.gov/pubmed/34754025 http://dx.doi.org/10.1038/s41598-021-01407-y |
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author | Syga, Simon David-Rus, Diana Schälte, Yannik Hatzikirou, Haralampos Deutsch, Andreas |
author_facet | Syga, Simon David-Rus, Diana Schälte, Yannik Hatzikirou, Haralampos Deutsch, Andreas |
author_sort | Syga, Simon |
collection | PubMed |
description | Countries around the world implement nonpharmaceutical interventions (NPIs) to mitigate the spread of COVID-19. Design of efficient NPIs requires identification of the structure of the disease transmission network. We here identify the key parameters of the COVID-19 transmission network for time periods before, during, and after the application of strict NPIs for the first wave of COVID-19 infections in Germany combining Bayesian parameter inference with an agent-based epidemiological model. We assume a Watts–Strogatz small-world network which allows to distinguish contacts within clustered cliques and unclustered, random contacts in the population, which have been shown to be crucial in sustaining the epidemic. In contrast to other works, which use coarse-grained network structures from anonymized data, like cell phone data, we consider the contacts of individual agents explicitly. We show that NPIs drastically reduced random contacts in the transmission network, increased network clustering, and resulted in a previously unappreciated transition from an exponential to a constant regime of new cases. In this regime, the disease spreads like a wave with a finite wave speed that depends on the number of contacts in a nonlinear fashion, which we can predict by mean field theory. |
format | Online Article Text |
id | pubmed-8578219 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-85782192021-11-10 Inferring the effect of interventions on COVID-19 transmission networks Syga, Simon David-Rus, Diana Schälte, Yannik Hatzikirou, Haralampos Deutsch, Andreas Sci Rep Article Countries around the world implement nonpharmaceutical interventions (NPIs) to mitigate the spread of COVID-19. Design of efficient NPIs requires identification of the structure of the disease transmission network. We here identify the key parameters of the COVID-19 transmission network for time periods before, during, and after the application of strict NPIs for the first wave of COVID-19 infections in Germany combining Bayesian parameter inference with an agent-based epidemiological model. We assume a Watts–Strogatz small-world network which allows to distinguish contacts within clustered cliques and unclustered, random contacts in the population, which have been shown to be crucial in sustaining the epidemic. In contrast to other works, which use coarse-grained network structures from anonymized data, like cell phone data, we consider the contacts of individual agents explicitly. We show that NPIs drastically reduced random contacts in the transmission network, increased network clustering, and resulted in a previously unappreciated transition from an exponential to a constant regime of new cases. In this regime, the disease spreads like a wave with a finite wave speed that depends on the number of contacts in a nonlinear fashion, which we can predict by mean field theory. Nature Publishing Group UK 2021-11-09 /pmc/articles/PMC8578219/ /pubmed/34754025 http://dx.doi.org/10.1038/s41598-021-01407-y Text en © The Author(s) 2021 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 | Article Syga, Simon David-Rus, Diana Schälte, Yannik Hatzikirou, Haralampos Deutsch, Andreas Inferring the effect of interventions on COVID-19 transmission networks |
title | Inferring the effect of interventions on COVID-19 transmission networks |
title_full | Inferring the effect of interventions on COVID-19 transmission networks |
title_fullStr | Inferring the effect of interventions on COVID-19 transmission networks |
title_full_unstemmed | Inferring the effect of interventions on COVID-19 transmission networks |
title_short | Inferring the effect of interventions on COVID-19 transmission networks |
title_sort | inferring the effect of interventions on covid-19 transmission networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8578219/ https://www.ncbi.nlm.nih.gov/pubmed/34754025 http://dx.doi.org/10.1038/s41598-021-01407-y |
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