Modelling the early phase of the Belgian COVID-19 epidemic using a stochastic compartmental model and studying its implied future trajectories
Steven Abrams,
James Wambua,
Eva Santermans,
Lander Willem,
Elise Kuylen,
Pietro Coletti,
Pieter Libin,
Christel Faes,
Oana Petrof,
Sereina A. Herzog,
Philippe Beutels,
Niel Hens
Affiliations
Steven Abrams
Data Science Institute, Interuniversity Institute of Biostatistics and statistical Bioinformatics, UHasselt, Hasselt, Belgium; Global Health Institute, Family Medicine and Population Health, University of Antwerp, Antwerp, Belgium; Corresponding author at: Data Science Institute, Interuniversity Institute of Biostatistics and statistical Bioinformatics, UHasselt, Hasselt, Belgium.
James Wambua
Data Science Institute, Interuniversity Institute of Biostatistics and statistical Bioinformatics, UHasselt, Hasselt, Belgium
Eva Santermans
Data Science Institute, Interuniversity Institute of Biostatistics and statistical Bioinformatics, UHasselt, Hasselt, Belgium
Lander Willem
Centre for Health Economic Research and Modelling Infectious Diseases, Vaccine & Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
Elise Kuylen
Centre for Health Economic Research and Modelling Infectious Diseases, Vaccine & Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
Pietro Coletti
Data Science Institute, Interuniversity Institute of Biostatistics and statistical Bioinformatics, UHasselt, Hasselt, Belgium
Pieter Libin
Data Science Institute, Interuniversity Institute of Biostatistics and statistical Bioinformatics, UHasselt, Hasselt, Belgium; Artificial Intelligence Lab, Department of Computer Science, Vrije Universiteit Brussel, Brussels, Belgium; Department of Microbiology and Immunology, Rega Institute for Medical Research, Clinical and Epidemiological Virology, University of Leuven, Leuven, Belgium
Christel Faes
Data Science Institute, Interuniversity Institute of Biostatistics and statistical Bioinformatics, UHasselt, Hasselt, Belgium
Oana Petrof
Data Science Institute, Interuniversity Institute of Biostatistics and statistical Bioinformatics, UHasselt, Hasselt, Belgium
Sereina A. Herzog
Centre for Health Economic Research and Modelling Infectious Diseases, Vaccine & Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
Philippe Beutels
Centre for Health Economic Research and Modelling Infectious Diseases, Vaccine & Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
Niel Hens
Data Science Institute, Interuniversity Institute of Biostatistics and statistical Bioinformatics, UHasselt, Hasselt, Belgium; Centre for Health Economic Research and Modelling Infectious Diseases, Vaccine & Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
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.
