Outbreak detection for temporal contact data

dc.contributor.authorSterchi, Martin
dc.contributor.authorSarasua, Cristina
dc.contributor.authorGrütter, Rolf
dc.contributor.authorBernstein, Abraham
dc.date.accessioned2024-05-24T12:09:05Z
dc.date.available2024-05-24T12:09:05Z
dc.date.issued2021
dc.description.abstractEpidemic spreading is a widely studied process due to its importance and possibly grave consequences for society. While the classical context of epidemic spreading refers to pathogens transmitted among humans or animals, it is straightforward to apply similar ideas to the spread of information (e.g., a rumor) or the spread of computer viruses. This paper addresses the question of how to optimally select nodes for monitoring in a network of timestamped contact events between individuals. We consider three optimization objectives: the detection likelihood, the time until detection, and the population that is affected by an outbreak. The optimization approach we use is based on a simple greedy approach and has been proposed in a seminal paper focusing on information spreading and water contamination. We extend this work to the setting of disease spreading and present its application with two example networks: a timestamped network of sexual contacts and a network of animal transports between farms. We apply the optimization procedure to a large set of outbreak scenarios that we generate with a susceptible-infectious-recovered model. We find that simple heuristic methods that select nodes with high degree or many contacts compare well in terms of outbreak detection performance with the (greedily) optimal set of nodes. Furthermore, we observe that nodes optimized on past periods may not be optimal for outbreak detection in future periods. However, seasonal effects may help in determining which past period generalizes well to some future period. Finally, we demonstrate that the detection performance depends on the simulation settings. In general, if we force the simulator to generate larger outbreaks, the detection performance will improve, as larger outbreaks tend to occur in the more connected part of the network where the top monitoring nodes are typically located. A natural progression of this work is to analyze how a representative set of outbreak scenarios can be generated, possibly taking into account more realistic propagation models.
dc.identifier.doi10.1007/s41109-021-00360-z
dc.identifier.issn2364-8228
dc.identifier.urihttps://irf.fhnw.ch/handle/11654/42995
dc.identifier.urihttps://doi.org/10.26041/fhnw-6960
dc.issue17
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofApplied Network Science
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subject.ddc330 - Wirtschaft
dc.titleOutbreak detection for temporal contact data
dc.type01A - Beitrag in wissenschaftlicher Zeitschrift
dc.volume6
dspace.entity.typePublication
fhnw.InventedHereYes
fhnw.ReviewTypeAnonymous ex ante peer review of a complete publication
fhnw.affiliation.hochschuleHochschule für Wirtschaftde_CH
fhnw.affiliation.institutInstitut für Unternehmensführungde_CH
fhnw.openAccessCategoryGold
fhnw.publicationStatePublished
relation.isAuthorOfPublication8fd97bed-9fae-445e-bf5b-6d2e87c0eab4
relation.isAuthorOfPublication.latestForDiscovery8fd97bed-9fae-445e-bf5b-6d2e87c0eab4
Dateien
Originalbündel
Gerade angezeigt 1 - 1 von 1
Lade...
Vorschaubild
Name:
Sterchi_Sarasua_Gruetter_Bernstein_2021_Outbreak_detection_for_temporal_contact_data.pdf
Größe:
2.02 MB
Format:
Adobe Portable Document Format
Beschreibung:
Lizenzbündel
Gerade angezeigt 1 - 1 von 1
Lade...
Vorschaubild
Name:
license.txt
Größe:
1.36 KB
Format:
Item-specific license agreed upon to submission
Beschreibung: