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

M. Bosman, A. Esteve, L. Gabbanelli, X. Jordan, A. López-Gay, M. Manera, M. Martínez, P. Masjuan, LlM. Mir, J. Paradells, A. Pignatelli, I. Riu, V. Vitagliano

Research output: Contribution to journalArticleResearchpeer-review


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.

Original languageEnglish
Pages (from-to)145-158
Number of pages14
JournalInfectious Disease Modelling
Issue number1
Publication statusPublished - Mar 2023


  • COVID-19 modelling
  • Intervention
  • Parameter estimation
  • Socio-demographic data


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