Abstract
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.
- age-structured compartmental SEIR model
- stochastic chain-binomial model
- hospitalization and mortality data
- serial serological survey
- Markov Chain Monte Carlo (MCMC)
Competing Interest Statement
The authors have declared no competing interest.
Funding Statement
SA and NH gratefully acknowledge support from the Research Foundation Flanders (FWO) (RESTORE project - G0G2920N). This work also received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (PC and NH, grant number 682540 - TransMID project, NH, PB grant number 101003688 - EpiPose project). LW and PL gratefully acknowledge funding from the Research Foundation Flanders (postdoctoral fellowships 1234620N and 1242021N). We acknowledge support from the Antwerp Study Centre for Infectious Diseases (ASCID).
Author Declarations
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Data Availability
Data on hospitalizations and deaths are available on the website of the Belgian Scientific Institute for Public Health, Sciensano.
