Inferring the effect of interventions on COVID-19 transmission networks

Abstract 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...

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Autores principales:	Simon Syga, Diana David-Rus, Yannik Schälte, Haralampos Hatzikirou, Andreas Deutsch
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/6d792d32015346399c30d729af32156b
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spelling	oai:doaj.org-article:6d792d32015346399c30d729af32156b2021-11-14T12:17:58ZInferring the effect of interventions on COVID-19 transmission networks10.1038/s41598-021-01407-y2045-2322https://doaj.org/article/6d792d32015346399c30d729af32156b2021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-01407-yhttps://doaj.org/toc/2045-2322Abstract 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.Simon SygaDiana David-RusYannik SchälteHaralampos HatzikirouAndreas DeutschNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Simon Syga Diana David-Rus Yannik Schälte Haralampos Hatzikirou Andreas Deutsch Inferring the effect of interventions on COVID-19 transmission networks
description	Abstract 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	article
author	Simon Syga Diana David-Rus Yannik Schälte Haralampos Hatzikirou Andreas Deutsch
author_facet	Simon Syga Diana David-Rus Yannik Schälte Haralampos Hatzikirou Andreas Deutsch
author_sort	Simon Syga
title	Inferring the effect of interventions on COVID-19 transmission networks
title_short	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_sort	inferring the effect of interventions on covid-19 transmission networks
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/6d792d32015346399c30d729af32156b
work_keys_str_mv	AT simonsyga inferringtheeffectofinterventionsoncovid19transmissionnetworks AT dianadavidrus inferringtheeffectofinterventionsoncovid19transmissionnetworks AT yannikschalte inferringtheeffectofinterventionsoncovid19transmissionnetworks AT haralamposhatzikirou inferringtheeffectofinterventionsoncovid19transmissionnetworks AT andreasdeutsch inferringtheeffectofinterventionsoncovid19transmissionnetworks
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Inferring the effect of interventions on COVID-19 transmission networks

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