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Contextual contact tracing based on stochastic compartment modeling and spatial risk assessment

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Contextual contact tracing based on stochastic compartment modeling and spatial risk assessment

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dc.contributor.author Mahmood, Mateen es_ES
dc.contributor.author Mateu, Jorge es_ES
dc.contributor.author Hernández-Orallo, Enrique es_ES
dc.date.accessioned 2023-10-19T18:01:48Z
dc.date.available 2023-10-19T18:01:48Z
dc.date.issued 2022-03 es_ES
dc.identifier.issn 1436-3240 es_ES
dc.identifier.uri http://hdl.handle.net/10251/198416
dc.description.abstract [EN] The current situation of COVID-19 highlights the paramount importance of infectious disease surveillance, which necessitates early monitoring for effective response. Policymakers are interested in data insights identifying high-risk areas as well as individuals to be quarantined, especially as the public gets back to their normal routine. We investigate both requirements by the implementation of disease outbreak modeling and exploring its induced dynamic spatial risk in form of risk assessment, along with its real-time integration back into the disease model. This paper implements a contact tracing-based stochastic compartment model as a baseline, to further modify the existing setup to include the spatial risk. This modification of each individual-level contact¿s intensity to be dependent on its spatial location has been termed as Contextual Contact Tracing. The results highlight that the inclusion of spatial context tends to send more individuals into quarantine which reduces the overall spread of infection. With a simulated example of an induced spatial high-risk, it is highlighted that the new spatio-SIR model can act as a tool to empower the analyst with a capability to explore disease dynamics from a spatial perspective. We conclude that the proposed spatio-SIR tool can be of great help for policymakers to know the consequences of their decision prior to their implementation. es_ES
dc.language Inglés es_ES
dc.publisher Springer-Verlag es_ES
dc.relation.ispartof Stochastic Environmental Research and Risk Assessment es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Compartment modeling es_ES
dc.subject Contact tracing es_ES
dc.subject Digital epidemiology es_ES
dc.subject Human mobility es_ES
dc.subject Self organizing maps es_ES
dc.subject Trajectories es_ES
dc.subject.classification ARQUITECTURA Y TECNOLOGIA DE COMPUTADORES es_ES
dc.title Contextual contact tracing based on stochastic compartment modeling and spatial risk assessment es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1007/s00477-021-02065-2 es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escola Tècnica Superior d'Enginyeria Informàtica es_ES
dc.description.bibliographicCitation Mahmood, M.; Mateu, J.; Hernández-Orallo, E. (2022). Contextual contact tracing based on stochastic compartment modeling and spatial risk assessment. Stochastic Environmental Research and Risk Assessment. 36(3):893-917. https://doi.org/10.1007/s00477-021-02065-2 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1007/s00477-021-02065-2 es_ES
dc.description.upvformatpinicio 893 es_ES
dc.description.upvformatpfin 917 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 36 es_ES
dc.description.issue 3 es_ES
dc.identifier.pmid 34720737 es_ES
dc.identifier.pmcid PMC8547309 es_ES
dc.relation.pasarela S\448219 es_ES
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