Predicting and containing epidemic risk using on-line friendship networks
© 2019 Coviello et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. To what extent can online social networks...
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2021
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| Online Access: | https://hdl.handle.net/1721.1/135183 |
| _version_ | 1826196929309048832 |
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| author | Coviello, Lorenzo Franceschetti, Massimo García-Herranz, Manuel Rahwan, Iyad |
| author2 | Massachusetts Institute of Technology. Media Laboratory |
| author_facet | Massachusetts Institute of Technology. Media Laboratory Coviello, Lorenzo Franceschetti, Massimo García-Herranz, Manuel Rahwan, Iyad |
| author_sort | Coviello, Lorenzo |
| collection | MIT |
| description | © 2019 Coviello et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. To what extent can online social networks predict who is at risk of an infection? Many infections are transmitted through physical encounter between humans, but collecting detailed information about it can be expensive, might invade privacy, or might not even be possible. In this paper, we ask whether online social networks help predict and contain epidemic risk. Using a dataset from a popular online review service which includes over 100 thousand users and spans 4 years of activity, we build a time-varying network that is a proxy of physical encounter between its users (the encounter network) and a static network based on their reported online friendship (the friendship With computer simulations, we compare stochastic infection processes on the two networks, considering infections on the encounter network as the benchmark. First, we show that the friendship network is useful to identify the individuals at risk of infection, despite providing lower accuracy than the ideal case in which the encounters are known. This limited prediction accuracy is not only due to the static nature of the friendship network because a static version of the encounter network provides more accurate prediction of risk than the friendship network. Then, we show that periodical monitoring of the infection spreading on the encounter network allows to correct the infection predicted by a process spreading on the friendly staff ndship network, and achieves high prediction accuracy. Finally, we show that the friendship network contains valuable information to effectively contain epidemic outbreaks even when a limited budget is available for immunization. In particular, a strategy that immunizes random friends of random individuals achieves the same performance as knowing individuals’ encounters at a small additional cost, even if the infection spreads on the encounter network. |
| first_indexed | 2024-09-23T10:39:55Z |
| format | Article |
| id | mit-1721.1/135183 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T10:39:55Z |
| publishDate | 2021 |
| publisher | Public Library of Science (PLoS) |
| record_format | dspace |
| spelling | mit-1721.1/1351832023-09-28T19:58:03Z Predicting and containing epidemic risk using on-line friendship networks Coviello, Lorenzo Franceschetti, Massimo García-Herranz, Manuel Rahwan, Iyad Massachusetts Institute of Technology. Media Laboratory © 2019 Coviello et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. To what extent can online social networks predict who is at risk of an infection? Many infections are transmitted through physical encounter between humans, but collecting detailed information about it can be expensive, might invade privacy, or might not even be possible. In this paper, we ask whether online social networks help predict and contain epidemic risk. Using a dataset from a popular online review service which includes over 100 thousand users and spans 4 years of activity, we build a time-varying network that is a proxy of physical encounter between its users (the encounter network) and a static network based on their reported online friendship (the friendship With computer simulations, we compare stochastic infection processes on the two networks, considering infections on the encounter network as the benchmark. First, we show that the friendship network is useful to identify the individuals at risk of infection, despite providing lower accuracy than the ideal case in which the encounters are known. This limited prediction accuracy is not only due to the static nature of the friendship network because a static version of the encounter network provides more accurate prediction of risk than the friendship network. Then, we show that periodical monitoring of the infection spreading on the encounter network allows to correct the infection predicted by a process spreading on the friendly staff ndship network, and achieves high prediction accuracy. Finally, we show that the friendship network contains valuable information to effectively contain epidemic outbreaks even when a limited budget is available for immunization. In particular, a strategy that immunizes random friends of random individuals achieves the same performance as knowing individuals’ encounters at a small additional cost, even if the infection spreads on the encounter network. 2021-10-27T20:11:08Z 2021-10-27T20:11:08Z 2019 2019-07-25T16:01:42Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/135183 en 10.1371/journal.pone.0211765 PLoS ONE Creative Commons Attribution 4.0 International license https://creativecommons.org/licenses/by/4.0/ application/pdf Public Library of Science (PLoS) PLoS |
| spellingShingle | Coviello, Lorenzo Franceschetti, Massimo García-Herranz, Manuel Rahwan, Iyad Predicting and containing epidemic risk using on-line friendship networks |
| title | Predicting and containing epidemic risk using on-line friendship networks |
| title_full | Predicting and containing epidemic risk using on-line friendship networks |
| title_fullStr | Predicting and containing epidemic risk using on-line friendship networks |
| title_full_unstemmed | Predicting and containing epidemic risk using on-line friendship networks |
| title_short | Predicting and containing epidemic risk using on-line friendship networks |
| title_sort | predicting and containing epidemic risk using on line friendship networks |
| url | https://hdl.handle.net/1721.1/135183 |
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