Improvement of Contact Tracing with Citizen's Distributed Risk Maps.

COVID collaboration complex network consensus contact tracing risk map

Journal

Entropy (Basel, Switzerland)
ISSN: 1099-4300
Titre abrégé: Entropy (Basel)
Pays: Switzerland
ID NLM: 101243874

Informations de publication

Date de publication:
20 May 2021
Historique:
received: 01 03 2021
revised: 07 05 2021
accepted: 11 05 2021
entrez: 2 6 2021
pubmed: 3 6 2021
medline: 3 6 2021
Statut: epublish

Résumé

The rapid spread of COVID-19 has demonstrated the need for accurate information to contain its diffusion. Technological solutions are a complement that can help citizens to be informed about the risk in their environment. Although measures such as contact traceability have been successful in some countries, their use raises society's resistance. This paper proposes a variation of the consensus processes in directed networks to create a risk map of a determined area. The process shares information with trusted contacts: people we would notify in the case of being infected. When the process converges, each participant would have obtained the risk map for the selected zone. The results are compared with the pilot project's impact testing of the Spanish contact tracing app (RadarCOVID). The paper also depicts the results combining both strategies: contact tracing to detect potential infections and risk maps to avoid movements into conflictive areas. Although some works affirm that contact tracing apps need 60% of users to control the propagation, our results indicate that a 40% could be enough. On the other hand, the elaboration of risk maps could work with only 20% of active installations, but the effect is to delay the propagation instead of reducing the contagion. With both active strategies, this methodology is able to significantly reduce infected people with fewer participants.

Identifiants

pubmed: 34065581
pii: e23050638
doi: 10.3390/e23050638
pmc: PMC8160685
pii:
doi:

Types de publication

Journal Article

Langues

eng

Subventions

Organisme : Horizon 2020 Framework Programme
ID : 952215
Organisme : Spanish Ministry of Science, Innovation and Universities
ID : PGC2018-093854-B-I00b
Organisme : Spanish Ministry of Science, Innovation and Universities
ID : RTI2018-095390-B-C32

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Auteurs

Miguel Rebollo (M)

VRAIn-Valencian Research Institute for Artificial Intelligence, Universitat Politècnica de València, 46022 Valencia, Spain.
Complex Systems Group, Universidad Politécnica de Madrid, 28040 Madrid, Spain.

Rosa María Benito (RM)

Complex Systems Group, Universidad Politécnica de Madrid, 28040 Madrid, Spain.

Juan Carlos Losada (JC)

Complex Systems Group, Universidad Politécnica de Madrid, 28040 Madrid, Spain.

Javier Galeano (J)

Complex Systems Group, Universidad Politécnica de Madrid, 28040 Madrid, Spain.

Classifications MeSH