Please use this identifier to cite or link to this item:
http://hdl.handle.net/1942/34339
Title: | Modelling the early phase of the Belgian COVID-19 epidemic using a stochastic compartmental model and studying its implied future trajectories | Authors: | ABRAMS, Steven WAMBUA, James SANTERMANS, Eva WILLEM, Lander KUYLEN, Elise COLETTI, Pietro LIBIN, Pieter FAES, Christel PETROF, Oana HERZOG, Sereina Beutels, Philippe HENS, Niel |
Issue Date: | 2021 | Publisher: | ELSEVIER | Source: | Epidemics, 35 (Art N° 100449) | 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. | Keywords: | Age-structured compartmental SEIR model;Hospitalization and mortality data;Markov Chain Monte Carlo (MCMC);Serial serological survey;Stochastic chain-binomial model | Document URI: | http://hdl.handle.net/1942/34339 | ISSN: | 1755-4365 | e-ISSN: | 1878-0067 | DOI: | 10.1016/j.epidem.2021.100449 | ISI #: | 000663758400013 | Rights: | 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license | Category: | A1 | Type: | Journal Contribution | Validations: | ecoom 2022 |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
1-s2.0-S1755436521000116-main.pdf | Published version | 4.78 MB | Adobe PDF | View/Open |
WEB OF SCIENCETM
Citations
48
checked on Oct 13, 2024
Page view(s)
62
checked on Aug 9, 2022
Download(s)
16
checked on Aug 9, 2022
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.