1. Predicted Impacts of Booster, Immunity Decline, Vaccination Strategies, and Non-Pharmaceutical Interventions on COVID-19 Outcomes in France
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Simon, Pageaud, Anne, Eyraud-Loisel, Jean-Pierre, Bertoglio, Alexis, Bienvenüe, Nicolas, Leboisne, Catherine, Pothier, Christophe, Rigotti, Nicolas, Ponthus, Romain, Gauchon, François, Gueyffier, Philippe, Vanhems, Jean, Iwaz, Stéphane, Loisel, Pascal, Roy, On Behalf Of The CovDyn Group Covid Dynamics, Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS), Hospices Civils de Lyon (HCL), Laboratoire de Sciences Actuarielles et Financières [Lyon] (LSAF), Institut de Science Financière et d'Assurances (ISFA), Université de Lyon, Laboratoire de Mecanique des Fluides et d'Acoustique (LMFA), École Centrale de Lyon (ECL), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS), Extraction de Caractéristiques et Identification (imagine), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Data Mining and Machine Learning (DM2L), Artificial Evolution and Computational Biology (BEAGLE), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Inria Lyon, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Laboratoire de Tribologie et Dynamique des Systèmes (LTDS), Université de Lyon-Université de Lyon-École Nationale des Travaux Publics de l'État (ENTPE)-Ecole Nationale d'Ingénieurs de Saint Etienne (ENISE)-Centre National de la Recherche Scientifique (CNRS), Centre International de Recherche en Infectiologie (CIRI), École normale supérieure de Lyon (ENS de Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), F-CRIN, Innovative clinical research network in vaccinology (I-REIVAC), Service de Biostatistiques [Lyon], Institut Louis Bachelier, École Nationale des Travaux Publics de l'État (ENTPE), Centre International de Recherche en Infectiologie - UMR (CIRI), École normale supérieure - Lyon (ENS Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), and Rigotti, Christophe
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[SDV.MHEP.ME] Life Sciences [q-bio]/Human health and pathology/Emerging diseases ,Pharmacology ,[SDV.MHEP.ME]Life Sciences [q-bio]/Human health and pathology/Emerging diseases ,decision support techniques ,Immunology ,COVID-19 ,vaccination ,agent-based model ,[INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation ,[SDV.IMM.VAC] Life Sciences [q-bio]/Immunology/Vaccinology ,Infectious Diseases ,booster ,[SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie ,[SDV.MHEP.MI]Life Sciences [q-bio]/Human health and pathology/Infectious diseases ,Drug Discovery ,[SDV.MHEP.MI] Life Sciences [q-bio]/Human health and pathology/Infectious diseases ,[SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie ,Pharmacology (medical) ,[INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation ,[SDV.IMM.VAC]Life Sciences [q-bio]/Immunology/Vaccinology - Abstract
International audience; The major economic and health consequences of COVID-19 called for various protective measures and mass vaccination campaigns. A previsional model was used to predict the future impacts of various measure combinations on COVID-19 mortality over a 400-day period in France. Calibrated on previous national hospitalization and mortality data, an agent-based epidemiological model was used to predict individual and combined effects of booster doses, vaccination of refractory adults, and vaccination of children, according to infection severity, immunity waning, and graded non-pharmaceutical interventions (NPIs). Assuming a 1.5 hospitalization hazard ratio and rapid immunity waning, booster doses would reduce COVID-19-related deaths by 50–70% with intensive NPIs and 93% with moderate NPIs. Vaccination of initially-refractory adults or children ≥5 years would half the number of deaths whatever the infection severity or degree of immunity waning. Assuming a 1.5 hospitalization hazard ratio, rapid immunity waning, moderate NPIs and booster doses, vaccinating children ≥12 years, ≥5 years, and ≥6 months would result in 6212, 3084, and 3018 deaths, respectively (vs. 87,552, 64,002, and 48,954 deaths without booster, respectively). In the same conditions, deaths would be 2696 if all adults and children ≥12 years were vaccinated and 2606 if all adults and children ≥6 months were vaccinated (vs. 11,404 and 3624 without booster, respectively). The model dealt successfully with single measures or complex combinations. It can help choosing them according to future epidemic features, vaccination extensions, and population immune status.
- Published
- 2022
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