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Modeling the early phase of the Belgian COVID-19 epidemic using a stochastic compartmental model and studying its implied future trajectories
pmc: PMC7986325
pmid: 33799289
Modeling the early phase of the Belgian COVID-19 epidemic using a stochastic compartmental model and studying its implied future trajectories
Following the onset of the ongoing COVID-19 pandemic throughout the world, a large fraction of the global population is or has been under strict measures of physical distancing and quarantine, with many countries being in partial or full lockdown. These measures are imposed in order to reduce the spread of the disease and to lift the pressure on healthcare systems. Estimating the impact of such interventions as well as monitoring the gradual relaxing of these stringent measures is quintessential to understand how resurgence of the COVID-19 epidemic can be controlled for in the future. In this paper we use a stochastic age-structured discrete time compartmental model to describe the transmission of COVID-19 in Belgium. Our model explicitly accounts for age-structure by integrating data on social contacts to (i) assess the impact of the lockdown as implemented on March 13, 2020 on the number of new hospitalizations in Belgium; (ii) conduct a scenario analysis estimating the impact of possible exit strategies on potential future COVID-19 waves. More specifically, the aforementioned model is fitted to hospital admission data, data on the daily number of COVID-19 deaths and serial serological survey data informing the (sero)prevalence of the disease in the population while relying on a Bayesian MCMC approach. Our age-structured stochastic model describes the observed outbreak data well, both in terms of hospitalizations as well as COVID-19 related deaths in the Belgian population. Despite an extensive exploration of various projections for the future course of the epidemic, based on the impact of adherence to measures of physical distancing and a potential increase in contacts as a result of the relaxation of the stringent lockdown measures, a lot of uncertainty remains about the evolution of the epidemic in the next months.
ispartof: location:Netherlands
ispartof: EPIDEMICS vol:35
status: published
- Hasselt University Belgium
- Vrije Universiteit Brussel Belgium
- KU Leuven Belgium
- Rega Institute for Medical Research Belgium
- University of Antwerp Belgium
Microsoft Academic Graph classification: Stochastic modelling Computer science Psychological intervention Bayes' theorem Pandemic Econometrics education.field_of_study Lift (data mining) Distancing Population Scenario analysis education Survey data collection
DYNAMICS, IMPACT, Epidemiology, Serial serological survey, Infectious and parasitic diseases, RC109-216, Belgium, Seroepidemiologic Studies, INFECTIOUS-DISEASES, Hospitalization, Infectious Diseases, Age-structured compartmental SEIR model, SPREAD, Life Sciences & Biomedicine, Markov Chain Monte Carlo (MCMC), TRANSMISSION, Stochastic chain-binomial model, Microbiology, Article, Virology, Humans, Hospitalization and mortality data, Science & Technology, Models, Statistical, SARS-CoV-2, Public Health, Environmental and Occupational Health, COVID-19, Bayes Theorem, Communicable Disease Control, Parasitology, Human medicine, Forecasting
DYNAMICS, IMPACT, Epidemiology, Serial serological survey, Infectious and parasitic diseases, RC109-216, Belgium, Seroepidemiologic Studies, INFECTIOUS-DISEASES, Hospitalization, Infectious Diseases, Age-structured compartmental SEIR model, SPREAD, Life Sciences & Biomedicine, Markov Chain Monte Carlo (MCMC), TRANSMISSION, Stochastic chain-binomial model, Microbiology, Article, Virology, Humans, Hospitalization and mortality data, Science & Technology, Models, Statistical, SARS-CoV-2, Public Health, Environmental and Occupational Health, COVID-19, Bayes Theorem, Communicable Disease Control, Parasitology, Human medicine, Forecasting
Microsoft Academic Graph classification: Stochastic modelling Computer science Psychological intervention Bayes' theorem Pandemic Econometrics education.field_of_study Lift (data mining) Distancing Population Scenario analysis education Survey data collection
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Coletti, P., Wambua, J., Gimma, A., Willem, L., Vercruysse, S., Vanhoutte, B., Jarvis, C.I., Van Zandvoort, K., Edmunds, J., Beutels, P., Hens, N., 2020b. CoMix: comparing mixing patterns in the Belgian population during lockdown. Sci. Rep. 10 (21885).
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