Efficient sentinel surveillance strategies for preventing epidemics on networks.
Surveillance plays a crucial role in preventing emerging infectious diseases from becoming epidemic. In circumstances where it is possible to monitor the infection status of certain people, transport hubs, or hospitals, early detection of the disease allows interventions to be implemented before mos...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2019-11-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1007517 |
| _version_ | 1819020265315631104 |
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| author | Ewan Colman Petter Holme Hiroki Sayama Carlos Gershenson |
| author_facet | Ewan Colman Petter Holme Hiroki Sayama Carlos Gershenson |
| author_sort | Ewan Colman |
| collection | DOAJ |
| description | Surveillance plays a crucial role in preventing emerging infectious diseases from becoming epidemic. In circumstances where it is possible to monitor the infection status of certain people, transport hubs, or hospitals, early detection of the disease allows interventions to be implemented before most of the damage can occur, or at least its impact can be mitigated. This paper addresses the question of which nodes we should select in a network of individuals susceptible to some infectious disease in order to minimize the number of casualties. By simulating disease outbreaks on a collection of empirical and synthetic networks we show that the best strategy depends on topological characteristics of the network. For highly modular or spatially embedded networks it is better to place the sentinels on nodes distributed across different regions. However, if the degree heterogeneity is high, then a strategy that targets network hubs is preferred. We further consider the consequences of having an incomplete sample of the network and demonstrate that the value of new information diminishes as more data is collected. Finally we find further marginal improvements using two heuristics informed by known results in graph theory that exploit the fragmented structure of sparse network data. |
| first_indexed | 2024-12-21T03:48:28Z |
| format | Article |
| id | doaj.art-8a096e6a4de64875b9d295b15554b56c |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-12-21T03:48:28Z |
| publishDate | 2019-11-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-8a096e6a4de64875b9d295b15554b56c2022-12-21T19:17:01ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582019-11-011511e100751710.1371/journal.pcbi.1007517Efficient sentinel surveillance strategies for preventing epidemics on networks.Ewan ColmanPetter HolmeHiroki SayamaCarlos GershensonSurveillance plays a crucial role in preventing emerging infectious diseases from becoming epidemic. In circumstances where it is possible to monitor the infection status of certain people, transport hubs, or hospitals, early detection of the disease allows interventions to be implemented before most of the damage can occur, or at least its impact can be mitigated. This paper addresses the question of which nodes we should select in a network of individuals susceptible to some infectious disease in order to minimize the number of casualties. By simulating disease outbreaks on a collection of empirical and synthetic networks we show that the best strategy depends on topological characteristics of the network. For highly modular or spatially embedded networks it is better to place the sentinels on nodes distributed across different regions. However, if the degree heterogeneity is high, then a strategy that targets network hubs is preferred. We further consider the consequences of having an incomplete sample of the network and demonstrate that the value of new information diminishes as more data is collected. Finally we find further marginal improvements using two heuristics informed by known results in graph theory that exploit the fragmented structure of sparse network data.https://doi.org/10.1371/journal.pcbi.1007517 |
| spellingShingle | Ewan Colman Petter Holme Hiroki Sayama Carlos Gershenson Efficient sentinel surveillance strategies for preventing epidemics on networks. PLoS Computational Biology |
| title | Efficient sentinel surveillance strategies for preventing epidemics on networks. |
| title_full | Efficient sentinel surveillance strategies for preventing epidemics on networks. |
| title_fullStr | Efficient sentinel surveillance strategies for preventing epidemics on networks. |
| title_full_unstemmed | Efficient sentinel surveillance strategies for preventing epidemics on networks. |
| title_short | Efficient sentinel surveillance strategies for preventing epidemics on networks. |
| title_sort | efficient sentinel surveillance strategies for preventing epidemics on networks |
| url | https://doi.org/10.1371/journal.pcbi.1007517 |
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