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Inferring Contagion Patterns in Social Contact Networks with Limited Infection Data
The spread of infectious disease is an inherently stochastic process. As such, real time control and prediction methods present a significant challenge. For diseases which spread through direct human interaction, (e.g., transferred from infected to susceptible individuals) the contagion process can...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7111645/ https://www.ncbi.nlm.nih.gov/pubmed/32288688 http://dx.doi.org/10.1007/s11067-013-9186-6 |
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author | Fajardo, David Gardner, Lauren M. |
author_facet | Fajardo, David Gardner, Lauren M. |
author_sort | Fajardo, David |
collection | PubMed |
description | The spread of infectious disease is an inherently stochastic process. As such, real time control and prediction methods present a significant challenge. For diseases which spread through direct human interaction, (e.g., transferred from infected to susceptible individuals) the contagion process can be modeled on a social-contact network where individuals are represented as nodes, and contacts between individuals are represented as links. The model presented in this paper seeks to identify the infection pattern which depicts the current state of an ongoing outbreak. This is accomplished by inferring the most likely paths of infection through a contact network under the assumption of partially available infection data. The problem is formulated as a bi-linear integer program, and heuristic solution methods are developed based on sub-problems which can be solved much more efficiently. The heuristic performance is presented for a range of randomly generated networks and different levels of information. The model results, which include the most likely set of infection spreading contacts, can be used to provide insight into future epidemic outbreak patterns, and aid in the development of intervention strategies. |
format | Online Article Text |
id | pubmed-7111645 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-71116452020-04-02 Inferring Contagion Patterns in Social Contact Networks with Limited Infection Data Fajardo, David Gardner, Lauren M. Netw Spat Econ Article The spread of infectious disease is an inherently stochastic process. As such, real time control and prediction methods present a significant challenge. For diseases which spread through direct human interaction, (e.g., transferred from infected to susceptible individuals) the contagion process can be modeled on a social-contact network where individuals are represented as nodes, and contacts between individuals are represented as links. The model presented in this paper seeks to identify the infection pattern which depicts the current state of an ongoing outbreak. This is accomplished by inferring the most likely paths of infection through a contact network under the assumption of partially available infection data. The problem is formulated as a bi-linear integer program, and heuristic solution methods are developed based on sub-problems which can be solved much more efficiently. The heuristic performance is presented for a range of randomly generated networks and different levels of information. The model results, which include the most likely set of infection spreading contacts, can be used to provide insight into future epidemic outbreak patterns, and aid in the development of intervention strategies. Springer US 2013-06-19 2013 /pmc/articles/PMC7111645/ /pubmed/32288688 http://dx.doi.org/10.1007/s11067-013-9186-6 Text en © Springer Science+Business Media New York 2013 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article Fajardo, David Gardner, Lauren M. Inferring Contagion Patterns in Social Contact Networks with Limited Infection Data |
title | Inferring Contagion Patterns in Social Contact Networks with Limited Infection Data |
title_full | Inferring Contagion Patterns in Social Contact Networks with Limited Infection Data |
title_fullStr | Inferring Contagion Patterns in Social Contact Networks with Limited Infection Data |
title_full_unstemmed | Inferring Contagion Patterns in Social Contact Networks with Limited Infection Data |
title_short | Inferring Contagion Patterns in Social Contact Networks with Limited Infection Data |
title_sort | inferring contagion patterns in social contact networks with limited infection data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7111645/ https://www.ncbi.nlm.nih.gov/pubmed/32288688 http://dx.doi.org/10.1007/s11067-013-9186-6 |
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