Targeted COVID-19 Vaccination (TAV-COVID) Considering Limited Vaccination Capacities—An Agent-Based Modeling Evaluation

(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 vacc...

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Published in:	Vaccines (Basel) Vol. 9; no. 5; p. 434
Main Authors:	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
Format:	Journal Article
Language:	English
Published:	Switzerland MDPI AG 27.04.2021 MDPI
Subjects:	Age Agent-based models Asymptomatic Coronaviruses COVID-19 COVID-19 vaccines Decision making Disease transmission Epidemics Fatalities Hospitalization Infections Influenza Medical personnel Mortality Occupational safety Older people Optimization Patients Pharmaceutical industry prioritization Public health Reduction SARS-CoV-2 Severe acute respiratory syndrome coronavirus 2 vaccination vaccination strategy Vaccines Viruses United States > US Austria COVID-19 SARS-CoV-2 health policy decision making optimization vaccination strategy decision-analytic modeling agent-based simulation policy guidance prioritization vaccination
ISSN:	2076-393X, 2076-393X
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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 ≥ 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.
AbstractList	(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 ≥ 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. (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 ≥ 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.(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 ≥ 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.
Author	Jahn, Beate Siebert, Uwe Schmid, Daniela Stojkov, Igor Bock, Wolfgang Paulke-Korinek, Maria Weiss, Günter Rippinger, Claire Kretzschmar, Mirjam Santamaria, Júlia Endel, Gottfried Bicher, Martin Urach, Christoph Mühlberger, Nikolai Sroczynski, Gaby Ostermann, Herwig Popper, Nikolas Schomaker, Michael Wiedermann, Ursula Druml, Christiane Redlberger-Fritz, Monika Czasch, Caroline
AuthorAffiliation	6 Department of Internal Medicine II, Medical University of Innsbruck, Anichstraße 35, 6020 Innsbruck, Austria; guenter.weiss@i-med.ac.at 4 Center for Infectious Disease Epidemiology and Research, University of Cape Town, Barnard Fuller Building, Anzio Rd, Observatory, Cape Town 7935, South Africa 9 UNESCO Chair on Bioethics, Medical University of Vienna, Waehringerstrasse 25, 1090 Vienna, Austria; christiane.druml@meduniwien.ac.at 12 Austrian National Public Health Institute/Gesundheit Österreich GmbH, Stubenring 6, 1010 Vienna, Austria; herwig.ostermann@goeg.at (H.O.); caroline.czasch@goeg.at (C.C.) 13 Austrian Federation of Social Insurances, Kundmanngasse 21, 1030 Vienna, Austria; Gottfried.Endel@sozialversicherung.at 15 Association for Decision Support for Health Policy and Planning, DEXHELPP, Neustiftgasse 57–59, A-1070 Vienna, Austria 17 Center for Health Decision Science, Departments of Epidemiology and Health Policy & Management, Harvard T.H. Chan School of Public Health, 718 Huntin
AuthorAffiliation_xml	– name: 10 Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Heidelberglaan 100, 3584CX Utrecht, The Netherlands; m.e.e.kretzschmar@umcutrecht.nl – name: 9 UNESCO Chair on Bioethics, Medical University of Vienna, Waehringerstrasse 25, 1090 Vienna, Austria; christiane.druml@meduniwien.ac.at – name: 15 Association for Decision Support for Health Policy and Planning, DEXHELPP, Neustiftgasse 57–59, A-1070 Vienna, Austria – name: 17 Center for Health Decision Science, Departments of Epidemiology and Health Policy & Management, Harvard T.H. Chan School of Public Health, 718 Huntington Avenue, Boston, MA 02115, USA – name: 4 Center for Infectious Disease Epidemiology and Research, University of Cape Town, Barnard Fuller Building, Anzio Rd, Observatory, Cape Town 7935, South Africa – name: 2 dwh GmbH, dwh Simulation Services, Neustiftgasse 57–59, A-1070 Vienna, Austria; martin.bicher@tuwien.ac.at (M.B.); claire.rippinger@dwh.at (C.R.); christoph.urach@dwh.at (C.U.); nikolas.popper@tuwien.ac.at (N.P.) – name: 11 Ministry of Social Affairs, Health, Care and Consumer Protection, Stubenring 1, 1010 Vienna, Austria; Maria.Paulke-Korinek@gesundheitsministerium.gv.at – name: 14 Department of Mathematics, TU Kaiserslautern, Gottlieb-Daimler-Straße 48, 67663 Kaiserslautern, Germany; bock@mathematik.uni-kl.de – name: 8 Center of Virology, Medical University of Vienna, Kinderspitalgasse 15, 1090 Vienna, Austria; monika.redlberger@meduniwien.ac.at – name: 6 Department of Internal Medicine II, Medical University of Innsbruck, Anichstraße 35, 6020 Innsbruck, Austria; guenter.weiss@i-med.ac.at – name: 5 Division for Quantitative Methods in Public Health and Health Services Research, Department of Public Health, Health Services Research and Health Technology Assessment, UMIT—University for Health Sciences, Medical Informatics and Technology, Eduard-Wallnoefer-Zentrum 1, A-6060 Hall in Tirol, Austria; daniela.schmid@umit.at – name: 16 Institute for Technology Assessment and Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 101 Merrimac St., Boston, MA 02114, USA – name: 3 Institute of Information Systems Engineering, TU Wien, Favoritenstraße 11, A-1050 Vienna, Austria – name: 13 Austrian Federation of Social Insurances, Kundmanngasse 21, 1030 Vienna, Austria; Gottfried.Endel@sozialversicherung.at – name: 12 Austrian National Public Health Institute/Gesundheit Österreich GmbH, Stubenring 6, 1010 Vienna, Austria; herwig.ostermann@goeg.at (H.O.); caroline.czasch@goeg.at (C.C.) – name: 7 Center of Pathophysiology, Infectiology & Immunology (OEL), Institute of Specific Prophylaxis and Tropical Medicine, Medical University of Vienna, Kinderspitalgasse 15, 1090 Vienna, Austria; ursula.wiedermann-schmidt@meduniwein.ac.at – name: 1 Department of Public Health, Health Services Research and Health Technology Assessment, Institute of Public Health, Medical Decision Making and Health Technology Assessment, UMIT—University for Health Sciences, Medical Informatics and Technology, Eduard-Wallnoefer-Zentrum 1, A-6060 Hall in Tirol, Austria; beate.jahn@umit.at (B.J.); gaby.sroczynski@umit.at (G.S.); nikolai.muehlberger@umit.at (N.M.); julia.santamaria-navarro@umit.at (J.S.); michael.schomaker@umit.at (M.S.); igor.stojkov@umit.at (I.S.)
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Copyright	2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2021 by the authors. 2021
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Snippet	(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...
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SubjectTerms	Age Agent-based models Asymptomatic Coronaviruses COVID-19 COVID-19 vaccines Decision making Disease transmission Epidemics Fatalities Hospitalization Infections Influenza Medical personnel Mortality Occupational safety Older people Optimization Patients Pharmaceutical industry prioritization Public health Reduction SARS-CoV-2 Severe acute respiratory syndrome coronavirus 2 vaccination vaccination strategy Vaccines Viruses
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Title	Targeted COVID-19 Vaccination (TAV-COVID) Considering Limited Vaccination Capacities—An Agent-Based Modeling Evaluation
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