دورية أكاديمية
Influenza spread on context-specific networks lifted from interaction-based diary data.
| العنوان: | Influenza spread on context-specific networks lifted from interaction-based diary data. |
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| المؤلفون: | Mallory K; Division of Applied Mathematics, Brown University, Providence, RI, USA., Rubin Abrams J; Department of Mathematics, The University of Arizona, Tucson, AZ, USA., Schwartz A; Amazon, Seattle, WA, USA., Ciocanel MV; Department of Mathematics and Biology, Duke University, Durham, NC, USA., Volkening A; NSF-Simons Center for Quantitative Biology, and Department of Engineering Sciences and Applied Mathematics, Northwestern University, Evanston, IL, USA., Sandstede B; Division of Applied Mathematics, Brown University, Providence, RI, USA. |
| المصدر: | Royal Society open science [R Soc Open Sci] 2021 Jan 27; Vol. 8 (1), pp. 191876. Date of Electronic Publication: 2021 Jan 27 (Print Publication: 2021). |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Royal Society Publishing Country of Publication: England NLM ID: 101647528 Publication Model: eCollection Cited Medium: Print ISSN: 2054-5703 (Print) Linking ISSN: 20545703 NLM ISO Abbreviation: R Soc Open Sci Subsets: PubMed not MEDLINE |
| أسماء مطبوعة: | Original Publication: London : Royal Society Publishing, 2014- |
| مستخلص: | Studying the spread of infections is an important tool in limiting or preventing future outbreaks. A first step in understanding disease dynamics is constructing networks that reproduce features of real-world interactions. In this paper, we generate networks that maintain some features of the partial interaction networks that were recorded in an existing diary-based survey at the University of Warwick. To preserve realistic structure in our artificial networks, we use a context-specific approach. In particular, we propose different algorithms for producing larger home, work and social networks. Our networks are able to maintain much of the interaction structure in the original diary-based survey and provide a means of accounting for the interactions of survey participants with non-participants. Simulating a discrete susceptible-infected-recovered model on the full network produces epidemic behaviour which shares characteristics with previous influenza seasons. Our approach allows us to explore how disease transmission and dynamic responses to infection differ depending on interaction context. We find that, while social interactions may be the first to be reduced after influenza infection, limiting work and school encounters may be significantly more effective in controlling the overall severity of the epidemic. Competing Interests: We declare we have no competing interests. (© 2021 The Authors.) |
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| فهرسة مساهمة: | Keywords: disease spread; dynamic network; influenza; social distance; susceptible–infected–recovered model |
| تواريخ الأحداث: | Date Created: 20210222 Latest Revision: 20210223 |
| رمز التحديث: | 20231215 |
| مُعرف محوري في PubMed: | PMC7890481 |
| DOI: | 10.1098/rsos.191876 |
| PMID: | 33614059 |
| قاعدة البيانات: | MEDLINE |
Full Text Finder | تدمد: | 2054-5703 |
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| DOI: | 10.1098/rsos.191876 |