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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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-04-01
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| Series: | Vaccines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-393X/9/5/434 |
| _version_ | 1797536026537754624 |
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| author | Beate Jahn Gaby Sroczynski Martin Bicher Claire Rippinger Nikolai Mühlberger Júlia Santamaria Christoph Urach Michael Schomaker Igor Stojkov Daniela Schmid Günter Weiss Ursula Wiedermann Monika Redlberger-Fritz Christiane Druml Mirjam Kretzschmar Maria Paulke-Korinek Herwig Ostermann Caroline Czasch Gottfried Endel Wolfgang Bock Nikolas Popper Uwe Siebert |
| author_facet | Beate Jahn Gaby Sroczynski Martin Bicher Claire Rippinger Nikolai Mühlberger Júlia Santamaria Christoph Urach Michael Schomaker Igor Stojkov Daniela Schmid Günter Weiss Ursula Wiedermann Monika Redlberger-Fritz Christiane Druml Mirjam Kretzschmar Maria Paulke-Korinek Herwig Ostermann Caroline Czasch Gottfried Endel Wolfgang Bock Nikolas Popper Uwe Siebert |
| author_sort | Beate Jahn |
| collection | DOAJ |
| description | (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. |
| first_indexed | 2024-03-10T11:53:44Z |
| format | Article |
| id | doaj.art-13cfa03c9a0c46368f4c0bb50deb6068 |
| institution | Directory Open Access Journal |
| issn | 2076-393X |
| language | English |
| last_indexed | 2024-03-10T11:53:44Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Vaccines |
| spelling | doaj.art-13cfa03c9a0c46368f4c0bb50deb60682023-11-21T17:25:45ZengMDPI AGVaccines2076-393X2021-04-019543410.3390/vaccines9050434Targeted COVID-19 Vaccination (TAV-COVID) Considering Limited Vaccination Capacities—An Agent-Based Modeling EvaluationBeate Jahn0Gaby Sroczynski1Martin Bicher2Claire Rippinger3Nikolai Mühlberger4Júlia Santamaria5Christoph Urach6Michael Schomaker7Igor Stojkov8Daniela Schmid9Günter Weiss10Ursula Wiedermann11Monika Redlberger-Fritz12Christiane Druml13Mirjam Kretzschmar14Maria Paulke-Korinek15Herwig Ostermann16Caroline Czasch17Gottfried Endel18Wolfgang Bock19Nikolas Popper20Uwe Siebert21Department 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, AustriaDepartment 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, Austriadwh GmbH, dwh Simulation Services, Neustiftgasse 57–59, A-1070 Vienna, Austriadwh GmbH, dwh Simulation Services, Neustiftgasse 57–59, A-1070 Vienna, AustriaDepartment 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, AustriaDepartment 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, Austriadwh GmbH, dwh Simulation Services, Neustiftgasse 57–59, A-1070 Vienna, AustriaDepartment 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, AustriaDepartment 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, AustriaDivision 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, AustriaDepartment of Internal Medicine II, Medical University of Innsbruck, Anichstraße 35, 6020 Innsbruck, AustriaCenter of Pathophysiology, Infectiology & Immunology (OEL), Institute of Specific Prophylaxis and Tropical Medicine, Medical University of Vienna, Kinderspitalgasse 15, 1090 Vienna, AustriaCenter of Virology, Medical University of Vienna, Kinderspitalgasse 15, 1090 Vienna, AustriaUNESCO Chair on Bioethics, Medical University of Vienna, Waehringerstrasse 25, 1090 Vienna, AustriaJulius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Heidelberglaan 100, 3584CX Utrecht, The NetherlandsMinistry of Social Affairs, Health, Care and Consumer Protection, Stubenring 1, 1010 Vienna, AustriaAustrian National Public Health Institute/Gesundheit Österreich GmbH, Stubenring 6, 1010 Vienna, AustriaAustrian National Public Health Institute/Gesundheit Österreich GmbH, Stubenring 6, 1010 Vienna, AustriaAustrian Federation of Social Insurances, Kundmanngasse 21, 1030 Vienna, AustriaDepartment of Mathematics, TU Kaiserslautern, Gottlieb-Daimler-Straße 48, 67663 Kaiserslautern, Germanydwh GmbH, dwh Simulation Services, Neustiftgasse 57–59, A-1070 Vienna, AustriaDepartment 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(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.https://www.mdpi.com/2076-393X/9/5/434SARS-CoV-2COVID-19vaccinationprioritizationvaccination strategyoptimization |
| spellingShingle | Beate Jahn Gaby Sroczynski Martin Bicher Claire Rippinger Nikolai Mühlberger Júlia Santamaria Christoph Urach Michael Schomaker Igor Stojkov Daniela Schmid Günter Weiss Ursula Wiedermann Monika Redlberger-Fritz Christiane Druml Mirjam Kretzschmar Maria Paulke-Korinek Herwig Ostermann Caroline Czasch Gottfried Endel Wolfgang Bock Nikolas Popper Uwe Siebert Targeted COVID-19 Vaccination (TAV-COVID) Considering Limited Vaccination Capacities—An Agent-Based Modeling Evaluation Vaccines SARS-CoV-2 COVID-19 vaccination prioritization vaccination strategy optimization |
| title | Targeted COVID-19 Vaccination (TAV-COVID) Considering Limited Vaccination Capacities—An Agent-Based Modeling Evaluation |
| title_full | Targeted COVID-19 Vaccination (TAV-COVID) Considering Limited Vaccination Capacities—An Agent-Based Modeling Evaluation |
| title_fullStr | Targeted COVID-19 Vaccination (TAV-COVID) Considering Limited Vaccination Capacities—An Agent-Based Modeling Evaluation |
| title_full_unstemmed | Targeted COVID-19 Vaccination (TAV-COVID) Considering Limited Vaccination Capacities—An Agent-Based Modeling Evaluation |
| title_short | Targeted COVID-19 Vaccination (TAV-COVID) Considering Limited Vaccination Capacities—An Agent-Based Modeling Evaluation |
| title_sort | targeted covid 19 vaccination tav covid considering limited vaccination capacities an agent based modeling evaluation |
| topic | SARS-CoV-2 COVID-19 vaccination prioritization vaccination strategy optimization |
| url | https://www.mdpi.com/2076-393X/9/5/434 |
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