Stochastic simulation of successive waves of COVID-19 in the province of Barcelona
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 pre...
| Autores: | , , , , , , , , , , , , |
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| Formato: | artículo |
| Fecha de publicación: | 2023 |
| País: | España |
| Recursos: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/381332 |
| Acesso em linha: | https://hdl.handle.net/2117/381332 https://dx.doi.org/10.1016/j.idm.2022.12.005 |
| Access Level: | acceso abierto |
| Palavra-chave: | COVID-19 Pandemic, 2020- -- Barcelona -- Mathematical models COVID-19 modelling Parameter estimation Socio-demographic data Intervention Pandèmia de COVID-19, 2020- -- Barcelona -- Models matemàtics Àrees temàtiques de la UPC::Matemàtiques i estadística::Investigació operativa::Simulació |
| Resumo: | 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 |
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