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Stochastic simulation of successive waves of COVID-19 in the province of Barcelona

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dc.contributor.author Bosman, Martine
dc.contributor.author Esteve, Albert
dc.contributor.author Gabbanelli, Luciano
dc.contributor.author Jordán Parra, Javier
dc.contributor.author López-Gay, Antonio
dc.contributor.author Manera, Marc
dc.contributor.author Martínez, Manel
dc.contributor.author Masjuan, Pere
dc.contributor.author Mir, Lluïsa-Maria
dc.contributor.author Paradells, Josep
dc.contributor.author Pignatelli, Alessio
dc.contributor.author Riu, Imma
dc.contributor.author Vitagliano, Vincenzo
dc.date.accessioned 2023-03-14T07:09:09Z
dc.date.available 2023-03-14T07:09:09Z
dc.date.issued 2023
dc.identifier.citation Bosman M, Esteve A, Gabbanelli L, Jordan X, López-Gay A, Manera M, et al. Stochastic simulation of successive waves of COVID-19 in the province of Barcelona. Infectious Disease Modelling. 2023 Mar;8(1):145-58. DOI: 10.1016/j.idm.2022.12.005
dc.identifier.issn 2468-0427
dc.identifier.uri http://hdl.handle.net/10230/56198
dc.description Includes supplementary materials for the online appendix.
dc.description.abstract Analytic compartmental models are currently used in mathematical epidemiology to forecast the COVID-19 pandemic evolution and explore the impact of mitigation strategies. In general, such models treat the population as a single entity, losing the social, cultural and economical specificities. We present a network model that uses socio-demographic datasets with the highest available granularity to predict the spread of COVID-19 in the province of Barcelona. The model is flexible enough to incorporate the effect of containment policies, such as lockdowns or the use of protective masks, and can be easily adapted to future epidemics. We follow a stochastic approach that combines a compartmental model with detailed individual microdata from the population census, including social determinants and age-dependent strata, and time-dependent mobility information. We show that our model reproduces the dynamical features of the disease across two waves and demonstrates its capability to become a powerful tool for simulating epidemic events.
dc.description.sponsorship The authors affiliated to CED, IFAE and i2CAT acknowledge the support of the CERCA institution, Centres de Recerca de Catalunya. MB, XJ, LLM, MMar, PM, JP, IR acknowledge support from the grant 2020PANDE0180 of the programme PANDEMIES 2020, “Replegar-se per créixer: l'impacte de les pandèmies en un món sense fronteres visibles” of the Agència de Gestió d’Ajuts Universitaris i de Recerca of the Generalitat de Catalunya. LG thanks the funding from the European Union's Horizon 2020 research and innovation programme under grant agreement ID 758145. AL acknowledges the support from the Talent Research Program (Universitat Autònoma de Barcelona). PM has received funding from the Spanish Ministry of Science and Innovation (PID2020-112965 GB-I00/AEI/10.13039/501100011033), from the Agency for Management of University and Research Grants of the Government of Catalonia (project SGR 1069), and also received support from Ajuntament de Barcelona. MMan acknowledges support from Marie Sklodowska-Curie grant agreement ID 6655919. VV has been partially supported by the H2020 programme and by the Secretary of Universities and Research of the Government of Catalonia through a Marie Skłodowska-Curie COFUND fellowship – Beatriu de Pinós programme ID 801370. The work of VV has also been carried out in the framework of activities of the Italian National Group of Mathematical Physics (GNFM, INdAM) and of the INFN Research Project QGSKY.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher KeAi Communications
dc.relation.ispartof Infectious Disease Modelling. 2023 Mar;8(1):145-58
dc.rights © 2022 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
dc.rights.uri http://creativecommons.org/licenses/by/4.0/
dc.title Stochastic simulation of successive waves of COVID-19 in the province of Barcelona
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.1016/j.idm.2022.12.005
dc.subject.keyword COVID-19 modelling
dc.subject.keyword Parameter estimation
dc.subject.keyword Socio-demographic data
dc.subject.keyword Intervention
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/758145
dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/PID2020-112965GB-I00
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/801370
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion


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