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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

Authors: Steven Abrams; James Wambua; Eva Santermans; Lander Willem; Elise Kuylen; Pietro Coletti; Pieter Libin; +5 Authors

Modeling the early phase of the Belgian COVID-19 epidemic using a stochastic compartmental model and studying its implied future trajectories

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.

ispartof: location:Netherlands

ispartof: EPIDEMICS vol:35

status: published

Country
Belgium
Subjects by Vocabulary

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

Keywords

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

41 references, page 1 of 5

Bailey, N.T.J., 1975. The Mathematical Theory of Infectious Diseases and Its Applications. Griffin, London.

Belgian Government: Federal Public Service - Health, Food Chain Safety and Environment, 2020. Coronavirus COVID-19 - current measures. https://www.infocoronavirus.be/en/faq/.

den Boon, S., Jit, M., Brisson, M., Medley, G., Beutels, P., White, R., Flasche, S., Hollingsworth, T.D., Garske, T., Pitzer, V.E., Hoogendoorn, M., Geffen, O., Clark, A., Kim, J., Hutubessy, R., 2019. Guidelines for multi-model comparisons of the impact of infectious disease interventions. BMC Med. (163), http://dx.doi.org/10.1186/ s12916-019-1403-9.

Centers for Disease Control and Prevention, 2020. Interim Clinical Guidance for Management of Patients with Confirmed Coronavirus Disease (COVID-19). CDC, https://www.cdc.gov/coronavirus/2019-ncov/hcp/clinical-guidance-managementpatients.html.

Cereda, D., Tirani, M., Rovida, F., Demicheli, V., Ajelli, M., Poletti, P., Trentini, F., Guzzetta, G., Marziano, V., Barone, A., Magoni, M., Deandrea, S., Diurno, G., Lombardo, M., Faccini, M., Pan, A., Bruno, R., Pariani, E., Grasselli, G., Piatti, A., Gramegna, M., Baldanti, F., Melegaro, A., Merler, S., 2020. The early phase of the COVID-19 outbreak in Lombardy, Italy. arXiv. arXiv:2003.09320. [OpenAIRE]

Coletti, P., Libin, P., Petrof, O., Willem, L., Abrams, S., Herzog, S., Faes, C., Wambua, J., Kuylen, E., SIMID COVID-19 team, Beutels, P., Hens, N., 2020a. A data-driven metapopulation model for the Belgian COVID-19 epidemic: assessing the impact of lockdown and exit strategies. medRxiv.

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).

Di Domenico, L., Pullano, G., Sabbatini, C.E., Boëlle, P.-Y., Colizza, V., 2020. Expected impact of lockdown in Île-de-France and possible exit strategies. BMC Med. 18 (240).

Diekmann, O., Heesterbeek, J.A.P., Metz, J.A.J., 1990. On the definition and the computation of the basic reproduction ratio 0 in models for infectious diseases in heterogeneous populations. J. Math. Biol. 28 (4), 365-382. [OpenAIRE]

Faes, C., Abrams, S., Van Beckhoven, D., Meyfroidt, G., Vlieghe, E., Hens, N., 2020. Time between symptom onset, hospitalisation and recovery or death: a statistical analysis of different time-delay distributions in Belgian COVID-19 patients. Int. J. Environ. Res. Public Health 17 (20), 7560. http://dx.doi.org/10.3390/ ijerph17207560.

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    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
56
Top 1%
Top 10%
Top 1%
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