2025W

• Modeling and Simulation / 194.076 / VU
• Modelling and Simulation in Health Technology Assessment / 194.094 / VU

2025

• Managing equitable contagious disease testing: A mathematical model for resource optimization / Ghasemi, P., Ehmke, J. F., & Bicher, M. (2025). Managing equitable contagious disease testing: A mathematical model for resource optimization. OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE, 135, Article 103305. https://doi.org/10.1016/j.omega.2025.103305
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  Project: DynOptTestControl (2022–2026)
• Ideas towards Model Families for Multi-Criteria Decision Support: A COVID-19 Case Study / Bicher, M., Rippinger, C., Urach, C., Brunmeir, D., Zechmeister, M., & Popper, N. (2025). Ideas towards Model Families for Multi-Criteria Decision Support: A COVID-19 Case Study. ACM Transactions on Modeling and Computer Simulation, 35(3), Article 26. https://doi.org/10.1145/3722217
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• Zero Stability in Hierarchical Co-Simulation / Hafner, I., Bicher, M., & Popper, N. (2025). Zero Stability in Hierarchical Co-Simulation. In 2024 Winter Simulation Conference (WSC) (pp. 359–370). IEEE. https://doi.org/10.1109/WSC63780.2024.10838842
• Disaggregating Train Delays into Primary and Secondary Components using Gated Graph Convolutional Networks / Viehauser, M., Bicher, M., Rössler, M., & Popper, N. (2025). Disaggregating Train Delays into Primary and Secondary Components using Gated Graph Convolutional Networks. IFAC-PapersOnLine, 59(1), 439–444. https://doi.org/10.1016/j.ifacol.2025.03.075
  Project: Green-TrAIn-Plan (2022–2025)

2024

• Four Years of Not-Using a Simulator: The Agent-Based Template / Brunmeir, D., Bicher, M., Popper, N., Rößler, M., Urach, C., Rippinger, C., & Wastian, M. (2024). Four Years of Not-Using a Simulator: The Agent-Based Template. In C. G. Corlu, S. R. Hunter, H. Lam, B. S. Onggo, J. Shortle, & B. Biller (Eds.), 2023 Winter Simulation Conference (WSC) (pp. 255–266). IEEE. https://doi.org/10.1109/WSC60868.2023.10408482
• Modeling of Agent Decisions Using Conditional Generative Adversarial Networks / Bicher, M., Brunmeir, D., & Popper, N. (2024). Modeling of Agent Decisions Using Conditional Generative Adversarial Networks. In 2024 Winter Simulation Conference (WSC) (pp. 2643–2654). IEEE. https://doi.org/10.1109/WSC63780.2024.10838996
  Project: DynOptTestControl (2022–2026)

2023

• Model Families for Multi-Criteria Decision Support: A COVID-19 Case Study / Bicher, M., Rippinger, C., Urach, C., Brunmeir, D., Zechmeister, M., & Popper, N. (2023). Model Families for Multi-Criteria Decision Support: A COVID-19 Case Study. arXiv. https://doi.org/10.34726/5480
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  Project: KLIPHA-COVID19 (2020–2021)

2022

• Supporting COVID-19 policy-making with a predictive epidemiological multi-model warning system / Bicher, M., Zuba, M., Rainer, L., Bachner, F., Rippinger, C., Ostermann, H., Popper, N., Thurner, S., & Klimek, P. (2022). Supporting COVID-19 policy-making with a predictive epidemiological multi-model warning system. Communications Medicine, 2(1), Article 157. https://doi.org/10.1038/s43856-022-00219-z
• An iterative algorithm for optimizing COVID-19 vaccination strategies considering unknown supply / Bicher, M., Rippinger, C., Zechmeister, M., Jahn, B., Sroczynski, G., Mühlberger, N., Santamaria-Navarro, J., Urach, C., Brunmeir, D., Siebert, U., & Popper, N. (2022). An iterative algorithm for optimizing COVID-19 vaccination strategies considering unknown supply. PLoS ONE, 17(5), Article e0265957. https://doi.org/10.1371/journal.pone.0265957
• Meteorological factors and non-pharmaceutical interventions explain local differences in the spread of SARS-CoV-2 in Austria / Ledebur, K., Kaleta, M., Chen, J., Lindner, S. D., Matzhold, C., Weidle, F., Wittmann, C., Habimana, K., Kerschbaumer, L., Stumpfl, S., Heiler, G., Bicher, M., Popper, N., Bachner, F., & Klimek, P. (2022). Meteorological factors and non-pharmaceutical interventions explain local differences in the spread of SARS-CoV-2 in Austria. PLoS Computational Biology, 18(4), Article e1009973. https://doi.org/10.1371/journal.pcbi.1009973
• Model based estimation of the SARS-CoV-2 immunization level in austria and consequences for herd immunity effects / Bicher, M., Rippinger, C., Schneckenreither, G., Weibrecht, N., Urach, C., Zechmeister, M., Brunmeir, D., Huf, W., & Popper, N. (2022). Model based estimation of the SARS-CoV-2 immunization level in austria and consequences for herd immunity effects. Scientific Reports, 12(1), Article 2872. https://doi.org/10.1038/s41598-022-06771-x
• POSA172 How to Optimize COVID-19 Vaccination Considering Limited Vaccination Capacities: A Decision-Analytic Framework and Modeling Study / Jahn, B., Sroczynski, G., Bicher, M., Rippinger, C., Mühlberger, N., Santamaria, J., Urach, C., Popper, N., & Siebert, U. (2022). POSA172 How to Optimize COVID-19 Vaccination Considering Limited Vaccination Capacities: A Decision-Analytic Framework and Modeling Study. Value in Health, 25(1), S121–S121. https://doi.org/10.1016/j.jval.2021.11.573
• HPR171 COVID 19 Vaccination - How to Support Decisions on Vaccination Prioritization for New Variants of Concern? / Jahn, B., Sroczynski, G., Bicher, M., Rippinger, C., Mühlberger, N., Santamaria, J., Urach, C., Popper, N., & Siebert, U. (2022). HPR171 COVID 19 Vaccination - How to Support Decisions on Vaccination Prioritization for New Variants of Concern? In ISPOR Europe 2022 Abstracts (p. S263). https://doi.org/10.1016/j.jval.2022.09.1300
• P16 COVID-19 Vaccination Strategies Considering a Vaccine Adapted to a New Variant of Concern: A Decision-Analytic Modeling Study / Jahn, B., Sroczynski, G., Bicher, M., Rippinger, C., Mühlberger, N., Santamaria, J., Urach, C., Popper, N., & Siebert, U. (2022). P16 COVID-19 Vaccination Strategies Considering a Vaccine Adapted to a New Variant of Concern: A Decision-Analytic Modeling Study [Conference Presentation]. ISPOR Europe 2022, Vienna, Austria. https://doi.org/10.1016/j.jval.2022.04.022
• Time Dynamics of the Spread of Virus Mutants with Increased Infectiousness in Austria / Bicher, M., Rippinger, C., & Popper, N. (2022). Time Dynamics of the Spread of Virus Mutants with Increased Infectiousness in Austria. In 10th Vienna International Conference on Mathematical Modelling MATHMOD 2022: Vienna Austria, 27–29 July 2022 (pp. 445–450). https://doi.org/10.1016/j.ifacol.2022.09.135
• Methods for Integrated Simulation - 10 Concepts to Integrate / Popper, N., Bicher, M., Breitenecker, F., Glock, B., Hafner, I., Mujica Mota, M., Mušic, G., Rippinger, C., Rössler, M., Schneckenreither, G., Urach, C., Wastian, M., Zauner, G., & Zechmeister, M. (2022). Methods for Integrated Simulation - 10 Concepts to Integrate. Simulation Notes Europe, 32(4), 225–236. https://doi.org/10.11128/sne.32.on.10627

2021

• Evaluation of Contact-Tracing Policies against the Spread of SARS-CoV-2 in Austria: An Agent-Based Simulation / Bicher, M., Rippinger, C., Urach, C., Brunmeir, D., Siebert, U., & Popper, N. (2021). Evaluation of Contact-Tracing Policies against the Spread of SARS-CoV-2 in Austria: An Agent-Based Simulation. Medical Decision Making, 41(8), 1017–1032. https://doi.org/10.1177/0272989x211013306
• Evaluation of a targeted COVID-19 vaccination strategy for Austria–a decision-analytic modeling study / Jahn, B., Sroczynski, G., Bicher, M., Claire Rippinger, Mühlberger, N., Santamaria-Navarro, J., Urach, C., Ostermann, H., Popper, N., & Siebert, U. (2021). Evaluation of a targeted COVID-19 vaccination strategy for Austria–a decision-analytic modeling study. European Journal of Public Health, 31(Supplement_3). https://doi.org/10.1093/eurpub/ckab165.078
• Targeted COVID-19 Vaccination (TAV-COVID) Considering Limited Vaccination Capacities-An Agent-Based Modeling Evaluation / Jahn, B., Sroczynski, G., Bicher, M., Rippinger, C., Mühlberger, N., Santamaria, J., Urach, C., Schomaker, M., Stojkov, I., Schmid, D., Weiss, G., Wiedermann, U., Redlberger-Fritz, M., Druml, C., Kretschmar, M., Paulke-Korinek, M., Ostermann, H., Czasch, C., Endel, G., … Siebert, U. (2021). Targeted COVID-19 Vaccination (TAV-COVID) Considering Limited Vaccination Capacities-An Agent-Based Modeling Evaluation. Vaccines, 9(5), 434. https://doi.org/10.3390/vaccines9050434
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  Project: KLIPHA-COVID19 (2020–2021)
• Logistikentscheidungsunterstützung in der Pandemie / Alarcon Ortega, E. J., Bicher, M., Bomze, I. M., Bot, R. I., Doerner, K. F., Gansterer, M., Kahr, M., Kritzinger, S., Popper, N., Sedlmayer, M., Vigo, D., & Wolfinger, D. (2021). Logistikentscheidungsunterstützung in der Pandemie. In Jahrbuch der Logistikforschung: Innovative Anwendungen, Konzepte & Technologien (Vol. 3, pp. 185–195). Trauner Verlag.
• How an election can be safely planned and conducted during a pandemic: Decision support based on a discrete event model / Weibrecht, N., Rößler, M., Bicher, M., Emrich, Š., Zauner, G., & Popper, N. (2021). How an election can be safely planned and conducted during a pandemic: Decision support based on a discrete event model. PLoS ONE, 16(12), Article e0261016. https://doi.org/10.1371/journal.pone.0261016
• Evaluation of undetected cases during the COVID-19 epidemic in Austria / Rippinger, C., Bicher, M., Urach, C., Brunmeir, D., Weibrecht, N., Zauner, G., Sroczynski, G., Jahn, B., Mühlberger, N., Siebert, U., & Popper, N. (2021). Evaluation of undetected cases during the COVID-19 epidemic in Austria. BMC Infectious Diseases, 21(70). https://doi.org/10.1186/s12879-020-05737-6
• Synthetic Reproduction and Augmentation of COVID-19 Case Reporting Data by Agent-Based Simulation / Popper, N., Zechmeister, M., Brunmeir, D., Rippinger, C., Weibrecht, N., Urach, C., Bicher, M., Schneckenreither, G., & Rauber, A. (2021). Synthetic Reproduction and Augmentation of COVID-19 Case Reporting Data by Agent-Based Simulation. Data Science Journal, 20. https://doi.org/10.5334/dsj-2021-016
• Woher kommen die Covid-19-Zahlen? / Popper, N., & Bicher, M. (2021). Woher kommen die Covid-19-Zahlen? TU Wien Alumnitag 2021, Wien, Austria. http://hdl.handle.net/20.500.12708/87254

2020

• Simulation Based Decision Support -the COVID19 Crisis from a Modeller's Perspective / Popper, N., & Bicher, M. (2020). Simulation Based Decision Support -the COVID19 Crisis from a Modeller’s Perspective. 25. Symposium Simulationstechnik - ASIM 2020, online, Unknown. http://hdl.handle.net/20.500.12708/87172
• Simulation and Optimization of Traction Unit Circulations / Rößler, M., Wastian, M., Jellen, A., Frisch, S., Weinberger, D., Hungerländer, P., Bicher, M., & Popper, N. (2020). Simulation and Optimization of Traction Unit Circulations. In Proceedings of the 2020 Winter Simulation Conference (pp. 90–101). IEEE. http://hdl.handle.net/20.500.12708/55605

2019

• Direct Implementation of ARGESIM Benchmark C7 'Constrained Pendulum' in MATLAB and EXCEL / Stockinger, A. E., Gütl, E., Rath, S. A., Strasser, D., Bicher, M., Körner, A., & Ecker, H. (2019). Direct Implementation of ARGESIM Benchmark C7 “Constrained Pendulum” in MATLAB and EXCEL. SNE Simulation Notes Europe, 29(2), 105–110. https://doi.org/10.11128/sne.29.bne07.10478
• Microscopic modelling of international (re-)hospitalisation effects in the CEPHOS-LINK setting / Zauner, G., Urach, C., Bicher, M., Popper, N., & Endel, F. (2019). Microscopic modelling of international (re-)hospitalisation effects in the CEPHOS-LINK setting. International Journal of Simulation and Process Modelling, 14(3), 261. https://doi.org/10.1504/ijspm.2019.101012
• ARGESIM Benchmark C11 'SCARA Robot': Comparison of Basic Implementations in EXCEL and MATLAB / Rekova, O., Pelzmann, N., Mandl, P., Hoffmann, M., Ecker, H., Körner, A., Bicher, M., & Breitenecker, F. (2019). ARGESIM Benchmark C11 “SCARA Robot”: Comparison of Basic Implementations in EXCEL and MATLAB. SNE Simulation Notes Europe, 29(3), 149–158. https://doi.org/10.11128/sne.29.bne11.10488

2017

• Planning future health: developing big data and system modelling pipelines for health system research / Popper, N., Endel, F., Mayer, R., Bicher, M., & Glock, B. (2017). Planning future health: developing big data and system modelling pipelines for health system research. Simulation Notes Europe, 27(4), 203–208. https://doi.org/10.11128/sne.27.tn.10396
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  Project: CSTAT: DEXHELPP (2014–2019)
• Microscopic Pendulum Modelling: Absurd Idea or Innovative View on Old Problems? / Bicher, M., & Popper, N. (2017). Microscopic Pendulum Modelling: Absurd Idea or Innovative View on Old Problems? Simulation Notes Europe, 27(4), 177–182. https://doi.org/10.11128/sne.27.tn.10392
  Project: CSTAT: DEXHELPP (2014–2019)

2016

• Agent-based Modelling and Simulation for Population Dynamics under Agricultural Constraints in Prehistoric Hallstatt: Hints for a Second Settlement / Tanzler, J., Wurzer, G., Kowarik, K., Popper, N., Reschreiter, H., Bicher, M., & Breitenecker, F. (2016). Agent-based Modelling and Simulation for Population Dynamics under Agricultural Constraints in Prehistoric Hallstatt: Hints for a Second Settlement. Simulation Notes Europe, 26(1), 41–46. https://doi.org/10.11128/sne.26.tn.10327

• Enhancing Train Network Simulation Model with a Moving Block System / Hartmann, F. (2025). Enhancing Train Network Simulation Model with a Moving Block System [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2025.126741
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• Primary delay injection models: A data-driven approach to robust stochastic railway simulation / Peter, V. (2025). Primary delay injection models: A data-driven approach to robust stochastic railway simulation [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2025.126740
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• Simulation-based disaggregation of train delay data using graph neural networks / Viehauser, M. (2024). Simulation-based disaggregation of train delay data using graph neural networks [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2024.115824
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