1. Agent-based modelling of reactive vaccination of workplaces and schools against COVID-19
- Author
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Benjamin Faucher, Rania Assab, Jonathan Roux, Daniel Levy-Bruhl, Cécile Tran Kiem, Simon Cauchemez, Laura Zanetti, Vittoria Colizza, Pierre-Yves Boëlle, Chiara Poletto, Institut Pierre Louis d'Epidémiologie et de Santé Publique (iPLESP), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU), Centre de Recherches sur l'Action Politique en Europe (ARENES), Université de Rennes (UR)-Institut d'Études Politiques [IEP] - Rennes-École des Hautes Études en Santé Publique [EHESP] (EHESP)-Centre National de la Recherche Scientifique (CNRS), École des Hautes Études en Santé Publique [EHESP] (EHESP), Santé publique France - French National Public Health Agency [Saint-Maurice, France], Modélisation mathématique des maladies infectieuses - Mathematical modelling of Infectious Diseases, Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Collège Doctoral, Sorbonne Université (SU), Haute Autorité de Santé [Saint-Denis La Plaine] (HAS), Tokyo Institute of Technology [Tokyo] (TITECH), We acknowledge financial support from Haute Autorité de Santé, the ANR and Fondation de France through the project NoCOV (00105995), the Municipality of Paris (https://www.paris.fr/) through the programme Emergence(s), EU H2020 grants MOOD(H2020-874850, paper MOOD 035) and RECOVER (H2020-101003589) (the contents of this publication do not necessarily reflect the views of the European Commission), the ANRS through the project EMERGEN (ANRS0151), and the Institut des Sciences duCalcul et de la Donnée., ANR-20-COVI-0070,NoCOV,Prévisions au court et moyen terme de la diffusion de COVID-19 dans la population générale française(2020), European Project: 874850,H2020-SC1-2019-Single-Stage-RTD,MOOD(2020), European Project: 101003589, H2020-SC1-PHE-CORONAVIRUS-2020,RECOVER(2020), EHESP, SCD, Prévisions au court et moyen terme de la diffusion de COVID-19 dans la population générale française - - NoCOV2020 - ANR-20-COVI-0070 - COVID-19 - VALID, MOnitoring Outbreak events for Disease surveillance in a data science context - MOOD - 874850 - INCOMING, Rapid European COVID-19 Emergency Response research - RECOVER - - H2020-SC1-PHE-CORONAVIRUS-20202020-02-14 - 2022-02-13 - 101003589 - VALID, and European Project: 874850,MOOD
- Subjects
Multidisciplinary ,Schools ,Systems Analysis ,Vaccination ,General Physics and Astronomy ,COVID-19 ,General Chemistry ,General Biochemistry, Genetics and Molecular Biology ,[SDV.IMM.VAC] Life Sciences [q-bio]/Immunology/Vaccinology ,[SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie ,Humans ,[SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie ,[SDV.IMM.VAC]Life Sciences [q-bio]/Immunology/Vaccinology ,Workplace ,COVID 19 - Abstract
With vaccination against COVID-19 stalled in some countries, increasing vaccine accessibility and distribution could help keep transmission under control. Here, we study the impact of reactive vaccination targeting schools and workplaces where cases are detected, with an agent-based model accounting for COVID-19 natural history, vaccine characteristics, demographics, behavioural changes and social distancing. In most scenarios, reactive vaccination leads to a higher reduction in cases compared with non-reactive strategies using the same number of doses. The reactive strategy could however be less effective than a moderate/high pace mass vaccination program if initial vaccination coverage is high or disease incidence is low, because few people would be vaccinated around each case. In case of flare-ups, reactive vaccination could better mitigate spread if it is implemented quickly, is supported by enhanced test-trace-isolate and triggers an increased vaccine uptake. These results provide key information to plan an adaptive vaccination rollout.
- Published
- 2021
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