Modelling the early phase of the Belgian COVID-19 epidemic using a stochastic compartmental model and studying its implied future trajectories
| Autor: | Elise Kuylen, Steven Abrams, Niel Hens, Pietro Coletti, Eva Santermans, Oana Petrof, James Wambua, Sereina A. Herzog, Lander Willem, Pieter Libin, Philippe Beutels, Christel Faes |
|---|---|
| Přispěvatelé: | Informatics and Applied Informatics, ABRAMS, Steven, WAMBUA, James, SANTERMANS, Eva, WILLEM, Lander, KUYLEN, Elise, COLETTI, Pietro, LIBIN, Pieter, FAES, Christel, PETROF, Oana, HERZOG, Sereina, Beutels, Philippe, HENS, Niel |
| Rok vydání: | 2021 |
| Předmět: |
DYNAMICS
IMPACT Epidemiology Stochastic modelling Computer science Psychological intervention Serial serological survey Infectious and parasitic diseases RC109-216 Bayes' theorem 0302 clinical medicine Belgium Seroepidemiologic Studies Pandemic Econometrics INFECTIOUS-DISEASES 030212 general & internal medicine 0303 health sciences education.field_of_study Lift (data mining) 3. Good health Hospitalization Infectious Diseases Age-structured compartmental SEIR model SPREAD Life Sciences & Biomedicine Markov Chain Monte Carlo (MCMC) TRANSMISSION Distancing 030231 tropical medicine Population Stochastic chain-binomial model Microbiology Article 03 medical and health sciences Virology Humans Scenario analysis education Hospitalization and mortality data 030304 developmental biology Models Statistical Science & Technology SARS-CoV-2 Public Health Environmental and Occupational Health COVID-19 Bayes Theorem Communicable Disease Control Survey data collection Parasitology Human medicine Forecasting |
| Zdroj: | Epidemics Epidemics, Vol 35, Iss, Pp 100449-(2021) |
| ISSN: | 1755-4365 |
| Popis: | 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. We thank several researchers from the SIMID COVID-19 consortium from the University of Antwerp and Hasselt University for numerous constructive discussions and meetings. SA and NH gratefully acknowledge support from the Research Foundation Flanders (FWO), Belgium (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). The authors are also very grateful for access to the data from the Belgian Scientific Institute for Public Health, Sciensano, and from the Vaccine & Infectious Disease Institute (VaxInfectio), University of Antwerp. LW and PL gratefully acknowledge funding from the Research Foundation Flanders, Belgium (postdoctoral fellowships 1234620N and 1242021N). We acknowledge support from the Antwerp Study Centre for Infectious Diseases (ASCID). The resources and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by the FWO and the Flemish Government. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. All authors contributed to the final version of the paper and approved the final version of the manuscript. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|