Targeted COVID-19 Vaccination (TAV-COVID) Considering Limited Vaccination Capacities-An Agent-Based Modeling Evaluation
Jahn, Beate; Sroczynski, Gaby; Bicher, Martin; Rippinger, Claire; Mühlberger, Nikolai; Santamaria, Júlia; Urach, Christoph; Schomaker, Michael; Stojkov, Igor; Schmid, Daniela; Weiss, Günter; Wiedermann, Ursula; Redlberger-Fritz, Monika; Druml, Christiane; Kretzschmar, Mirjam; Paulke-Korinek, Maria; Ostermann, Herwig; Czasch, Caroline; Endel, Gottfried; Bock, Wolfgang; Popper, Nikolas; Siebert, Uwe
(2021) Vaccines, volume 9, issue 5, pp. 1 - 17
(Article)
Abstract
(1) Background: The Austrian supply of COVID-19 vaccine is limited for now. We aim to provide evidence-based guidance to the authorities in order to minimize COVID-19-related hospitalizations and deaths in Austria. (2) Methods: We used a dynamic agent-based population model to compare different vaccination strategies targeted to the elderly (65
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≥ years), middle aged (45-64 years), younger (15-44 years), vulnerable (risk of severe disease due to comorbidities), and healthcare workers (HCW). First, outcomes were optimized for an initially available vaccine batch for 200,000 individuals. Second, stepwise optimization was performed deriving a prioritization sequence for 2.45 million individuals, maximizing the reduction in total hospitalizations and deaths compared to no vaccination. We considered sterilizing and non-sterilizing immunity, assuming a 70% effectiveness. (3) Results: Maximum reduction of hospitalizations and deaths was achieved by starting vaccination with the elderly and vulnerable followed by middle-aged, HCW, and younger individuals. Optimizations for vaccinating 2.45 million individuals yielded the same prioritization and avoided approximately one third of deaths and hospitalizations. Starting vaccination with HCW leads to slightly smaller reductions but maximizes occupational safety. (4) Conclusion: To minimize COVID-19-related hospitalizations and deaths, our study shows that elderly and vulnerable persons should be prioritized for vaccination until further vaccines are available.
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Keywords: Agent-based simulation, COVID-19, Decision-analytic modeling, Health policy decision making, Optimization, Policy guidance, Prioritization, SARS-CoV-2, Vaccination, Vaccination strategy, Immunology, Pharmacology, Drug Discovery, Infectious Diseases, Pharmacology (medical)
ISSN: 2076-393X
Publisher: Multidisciplinary Digital Publishing Institute
Note: Funding Information: Funding: This research was funded by the Gordon and Betty Moore Foundation through Grant (GBMF9634) to Johns Hopkins University to support the work of the Society for Medical Decision Making COVID-19 Decision Modeling Initiative and partially funded by the Austrian Federal Ministry for Digital and Economic Affairs BMDW and handled by the Austrian Research Promotion Agency (FFG) within the Emergency Call for research into COVID-19 in response to the Sars-CoV-2 outbreak (CIDS—Concurrent Infectious Disease Simulation) (881665), Vienna Science and Technology Fund WWTF within the Call COVID-19 Rapid Response Funding (COV20-035), and the Medical-Scientific Fund of the Mayor of Vienna (CoVid002). The authors had complete and independent control over study design, data collection and analysis, interpretation of data, report writing, decision to publish, and preparation of the manuscript. Funding Information: This research was funded by the Gordon and Betty Moore Foundation through Grant (GBMF9634) to Johns Hopkins University to support the work of the Society for Medical Decision Making COVID-19 Decision Modeling Initiative and partially funded by the Austrian Federal Ministry for Digital and Economic Affairs BMDW and handled by the Austrian Research Promotion Agency (FFG) within the Emergency Call for research into COVID-19 in response to the Sars-CoV-2 outbreak (CIDS?Concurrent Infectious Disease Simulation) (881665), Vienna Science and Technology Fund WWTF within the Call COVID-19 Rapid Response Funding (COV20-035), and the Medical-Scientific Fund of the Mayor of Vienna (CoVid002). The authors had complete and independent control over study design, data collection and analysis, interpretation of data, report writing, decision to publish, and preparation of the manuscript. The project team was advised by members of the Standing Policy and Expert Panel (SPEP TAV-COVID) and other national and international experts on the selection of methods and interpretation of results. We thank the following SPEP members for their support Klemens Fuchs (Austrian Agency for Health and Food Safety GmbH), Ingrid Zechmeister-Koss (Austrian Institute for Health Technology Assessment GmbH), Alan Brennan (School of Health and Related Research, University of Sheffield), and Tyll Kr?ger (Institute of Computer Engineering, Control and Robotics, Wroclaw University of Science and Technology). Additional SPEP members made further substantial contribution as coauthors. All research results represent the findings generated by the research team and do not necessarily reflect the opinions of the members of the SPEP. The views and opinions expressed during the expert workshops are solely those of the individuals involved and do not necessarily represent the official policy or position of any agency, organization, or employer. We thank Lyndon James (Harvard T.H. Chan School of Public Health) for proofreading and language editing. Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
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