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Network topological determinants of pathogen spread
How do we best constrain social interactions to decrease transmission of communicable diseases? Indiscriminate suppression is unsustainable long term and presupposes that all interactions carry equal importance. Instead, transmission within a social network has been shown to be determined by its top...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9095677/ https://www.ncbi.nlm.nih.gov/pubmed/35545647 http://dx.doi.org/10.1038/s41598-022-11786-5 |
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author | Pérez-Ortiz, María Manescu, Petru Caccioli, Fabio Fernández-Reyes, Delmiro Nachev, Parashkev Shawe-Taylor, John |
author_facet | Pérez-Ortiz, María Manescu, Petru Caccioli, Fabio Fernández-Reyes, Delmiro Nachev, Parashkev Shawe-Taylor, John |
author_sort | Pérez-Ortiz, María |
collection | PubMed |
description | How do we best constrain social interactions to decrease transmission of communicable diseases? Indiscriminate suppression is unsustainable long term and presupposes that all interactions carry equal importance. Instead, transmission within a social network has been shown to be determined by its topology. In this paper, we deploy simulations to understand and quantify the impact on disease transmission of a set of topological network features, building a dataset of 9000 interaction graphs using generators of different types of synthetic social networks. Independently of the topology of the network, we maintain constant the total volume of social interactions in our simulations, to show how even with the same social contact some network structures are more or less resilient to the spread. We find a suitable intervention to be specific suppression of unfamiliar and casual interactions that contribute to the network’s global efficiency. This is, pathogen spread is significantly reduced by limiting specific kinds of contact rather than their global number. Our numerical studies might inspire further investigation in connection to public health, as an integrative framework to craft and evaluate social interventions in communicable diseases with different social graphs or as a highlight of network metrics that should be captured in social studies. |
format | Online Article Text |
id | pubmed-9095677 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-90956772022-05-13 Network topological determinants of pathogen spread Pérez-Ortiz, María Manescu, Petru Caccioli, Fabio Fernández-Reyes, Delmiro Nachev, Parashkev Shawe-Taylor, John Sci Rep Article How do we best constrain social interactions to decrease transmission of communicable diseases? Indiscriminate suppression is unsustainable long term and presupposes that all interactions carry equal importance. Instead, transmission within a social network has been shown to be determined by its topology. In this paper, we deploy simulations to understand and quantify the impact on disease transmission of a set of topological network features, building a dataset of 9000 interaction graphs using generators of different types of synthetic social networks. Independently of the topology of the network, we maintain constant the total volume of social interactions in our simulations, to show how even with the same social contact some network structures are more or less resilient to the spread. We find a suitable intervention to be specific suppression of unfamiliar and casual interactions that contribute to the network’s global efficiency. This is, pathogen spread is significantly reduced by limiting specific kinds of contact rather than their global number. Our numerical studies might inspire further investigation in connection to public health, as an integrative framework to craft and evaluate social interventions in communicable diseases with different social graphs or as a highlight of network metrics that should be captured in social studies. Nature Publishing Group UK 2022-05-11 /pmc/articles/PMC9095677/ /pubmed/35545647 http://dx.doi.org/10.1038/s41598-022-11786-5 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 | Article Pérez-Ortiz, María Manescu, Petru Caccioli, Fabio Fernández-Reyes, Delmiro Nachev, Parashkev Shawe-Taylor, John Network topological determinants of pathogen spread |
title | Network topological determinants of pathogen spread |
title_full | Network topological determinants of pathogen spread |
title_fullStr | Network topological determinants of pathogen spread |
title_full_unstemmed | Network topological determinants of pathogen spread |
title_short | Network topological determinants of pathogen spread |
title_sort | network topological determinants of pathogen spread |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9095677/ https://www.ncbi.nlm.nih.gov/pubmed/35545647 http://dx.doi.org/10.1038/s41598-022-11786-5 |
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