Pietro Coletti1, Pieter Libin2,3,4, Oana Petrof2, Lander Willem5, Steven Abrams2,6, Sereina A Herzog5,7, Christel Faes2, Elise Kuylen2,5, James Wambua2, Philippe Beutels5,8, Niel Hens2,5. 1. Data Science Institute, I-Biostat, Hasselt University, Agoralaan Gebouw D, Diepenbeek, 3590, Belgium. pietro.coletti@uhasselt.be. 2. Data Science Institute, I-Biostat, Hasselt University, Agoralaan Gebouw D, Diepenbeek, 3590, Belgium. 3. Vrije Universiteit Brussel, Pleinlaan 2, Brussels, 1050, Belgium. 4. Rega Institute for Medical Research, Katholieke Universiteit Leuven, Herestraat 49, Leuven, 3000, Belgium. 5. Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Universiteitsplein 1, Wilrijk, 2610, Belgium. 6. Global Health Institute, Family Medicine and Population Health, University of Antwerp, Wilrijk, Belgium. 7. Institute for Medical Informatics, Statistics and Documentation, Auenbruggerplatz 2, Graz, 8036, Austria. 8. School of Public Health and Community Medicine, The University of New South Wales, Sydney, Australia.
Abstract
BACKGROUND: In response to the ongoing COVID-19 pandemic, several countries adopted measures of social distancing to a different degree. For many countries, after successfully curbing the initial wave, lockdown measures were gradually lifted. In Belgium, such relief started on May 4th with phase 1, followed by several subsequent phases over the next few weeks. METHODS: We analysed the expected impact of relaxing stringent lockdown measures taken according to the phased Belgian exit strategy. We developed a stochastic, data-informed, meta-population model that accounts for mixing and mobility of the age-structured population of Belgium. The model is calibrated to daily hospitalization data and is able to reproduce the outbreak at the national level. We consider different scenarios for relieving the lockdown, quantified in terms of relative reductions in pre-pandemic social mixing and mobility. We validate our assumptions by making comparisons with social contact data collected during and after the lockdown. RESULTS: Our model is able to successfully describe the initial wave of COVID-19 in Belgium and identifies interactions during leisure/other activities as pivotal in the exit strategy. Indeed, we find a smaller impact of school re-openings as compared to restarting leisure activities and re-openings of work places. We also assess the impact of case isolation of new (suspected) infections, and find that it allows re-establishing relatively more social interactions while still ensuring epidemic control. Scenarios predicting a second wave of hospitalizations were not observed, suggesting that the per-contact probability of infection has changed with respect to the pre-lockdown period. CONCLUSIONS: Contacts during leisure activities are found to be most influential, followed by professional contacts and school contacts, respectively, for an impending second wave of COVID-19. Regular re-assessment of social contacts in the population is therefore crucial to adjust to evolving behavioral changes that can affect epidemic diffusion.
BACKGROUND: In response to the ongoing COVID-19 pandemic, several countries adopted measures of social distancing to a different degree. For many countries, after successfully curbing the initial wave, lockdown measures were gradually lifted. In Belgium, such relief started on May 4th with phase 1, followed by several subsequent phases over the next few weeks. METHODS: We analysed the expected impact of relaxing stringent lockdown measures taken according to the phased Belgian exit strategy. We developed a stochastic, data-informed, meta-population model that accounts for mixing and mobility of the age-structured population of Belgium. The model is calibrated to daily hospitalization data and is able to reproduce the outbreak at the national level. We consider different scenarios for relieving the lockdown, quantified in terms of relative reductions in pre-pandemic social mixing and mobility. We validate our assumptions by making comparisons with social contact data collected during and after the lockdown. RESULTS: Our model is able to successfully describe the initial wave of COVID-19 in Belgium and identifies interactions during leisure/other activities as pivotal in the exit strategy. Indeed, we find a smaller impact of school re-openings as compared to restarting leisure activities and re-openings of work places. We also assess the impact of case isolation of new (suspected) infections, and find that it allows re-establishing relatively more social interactions while still ensuring epidemic control. Scenarios predicting a second wave of hospitalizations were not observed, suggesting that the per-contact probability of infection has changed with respect to the pre-lockdown period. CONCLUSIONS: Contacts during leisure activities are found to be most influential, followed by professional contacts and school contacts, respectively, for an impending second wave of COVID-19. Regular re-assessment of social contacts in the population is therefore crucial to adjust to evolving behavioral changes that can affect epidemic diffusion.
Authors: Shari Krishnaratne; Hannah Littlecott; Kerstin Sell; Jacob Burns; Julia E Rabe; Jan M Stratil; Tim Litwin; Clemens Kreutz; Michaela Coenen; Karin Geffert; Anna Helen Boger; Ani Movsisyan; Suzie Kratzer; Carmen Klinger; Katharina Wabnitz; Brigitte Strahwald; Ben Verboom; Eva Rehfuess; Renke L Biallas; Caroline Jung-Sievers; Stephan Voss; Lisa M Pfadenhauer Journal: Cochrane Database Syst Rev Date: 2022-01-17
Authors: Shari Krishnaratne; Lisa M Pfadenhauer; Michaela Coenen; Karin Geffert; Caroline Jung-Sievers; Carmen Klinger; Suzie Kratzer; Hannah Littlecott; Ani Movsisyan; Julia E Rabe; Eva Rehfuess; Kerstin Sell; Brigitte Strahwald; Jan M Stratil; Stephan Voss; Katharina Wabnitz; Jacob Burns Journal: Cochrane Database Syst Rev Date: 2020-12-17
Authors: Mafalda N S Miranda; Marta Pingarilho; Victor Pimentel; Andrea Torneri; Sofia G Seabra; Pieter J K Libin; Ana B Abecasis Journal: Front Microbiol Date: 2022-06-02 Impact factor: 6.064