Agent-based modeling and simulation in pandemic management
DOI:
https://doi.org/10.14512/tatup.32.1.30Keywords:
agent-based modeling, simulation, pandemic managementAbstract
Mathematical models and computer simulations play a crucial role in the context of the COVID-19 crisis for knowledge about the possible course of the pandemic and for appropriate policy decisions. The paper presents results from an ethnographic study of a government-funded R & D project dealing with agent-based modeling and simulation (ABMS) in the context of pandemic management. Based on the assumption that the use of computer simulations in pandemic management is not only a means to an end for political or epidemiological goals but also plays a significant role in determining which goals and strategies appear politically legitimate, the paper reconstructs how insights into the pandemic are generated in ABMS and specifically in the researched project and made accessible for decision-making.
References
Adam, David (2020): Special report: The simulations driving the world’s response to COVID-19. In: Nature 580 (7803), S. 316–318. https://doi.org/10.1038/d41586-020-01003-6 DOI: https://doi.org/10.1038/d41586-020-01003-6
Deutsche Gesellschaft für Epidemiologie (2020): 2. Stellungnahme der Deutschen Gesellschaft für Epidemiologie (DGEpi) zur Verbreitung des neuen Coronavirus (SARS-CoV-2). Online verfügbar unter https://www.awmf.org/fileadmin/user_upload/dateien/covid_19_leitlinien/6.2.pdf, zuletzt geprüft am 03. 02. 2023
Epstein, Joshua (2009): Modelling to contain pandemics. In: Nature 460 (7256), S. 687. https://doi.org/10.1038/460687a DOI: https://doi.org/10.1038/460687a
Epstein, Joshua; Parker, Jon; Cummings, Derek; Hammond, Ross (2008): Coupled contagion dynamics of fear and disease. Mathematical and computational explorations. In: PloS one 3 (12), S. e3955. https://doi.org/10.1371/journal.pone.0003955 DOI: https://doi.org/10.1371/journal.pone.0003955
Frias-Martinez, Enrique; Williamson, Graham; Frias-Martinez, Vanessa (2011): An agent-based model of epidemic spread using human mobility and social network information. In: 2011 IEEE International Conference on Privacy, Security, Risk, and Trust, and IEEE International Conference on Social Computing, S. 57–64. https://doi.org/10.1109/PASSAT/SocialCom.2011.142 DOI: https://doi.org/10.1109/PASSAT/SocialCom.2011.142
Hälterlein, Jens (2020): Die Simulation der Pandemie: Ein Beitrag zur Reihe „Sicherheit in der Krise“. Online verfügbar unter https://www.soziopolis.de/die-simulation-der-pandemie.html, zuletzt geprüft am 03. 02. 2023
Ioannidis, John; Cripps, Sally; Tanner, Martin (2022): Forecasting for COVID-19 has failed. In: International Journal of Forecasting 38 (2), S. 423–438. https://doi.org/10.1016/j.ijforecast.2020.08.004 DOI: https://doi.org/10.1016/j.ijforecast.2020.08.004
Littoz-Monnet, Annabelle (2020): Depoliticising through expertise. The politics of modelling in the governance of COVID-19. Online verfügbar unter https://globalchallenges.ch/issue/special_1/depoliticising-through-expertise-thepolitics-of-modelling-in-the-governance-of-covid-19, zuletzt geprüft am 03. 02. 2023.
Lorig, Fabian; Johansson, Emil; Davidsson, Paul (2021): Agent-based social simulation of the COVID-19 pandemic. A systematic review. In: Journal of Artificial Societies and Social Simulation 24 (3), 26 S. https://doi.org/10.18564/jasss.4601 DOI: https://doi.org/10.18564/jasss.4601
Opitz, Sven (2017): Simulating the world. The digital enactment of pandemics as a mode of global self-observation. In: European Journal of Social Theory 20 (3), S. 392–416. https://doi.org/10.1177/1368431016671141 DOI: https://doi.org/10.1177/1368431016671141
Saltelli, Andrea et al. (2020): Five ways to ensure that models serve society. A manifesto. In: Nature 582 (7813), S. 482–484. https://doi.org/10.1038/d41586-020-01812-9 DOI: https://doi.org/10.1038/d41586-020-01812-9
Skitka, Linda; Mosier, Kathleen; Burdick, Mark (1999): Does automation bias decision-making? In: International Journal of Human-Computer Studies 51 (5), S. 991–1006. https://doi.org/10.1006/ijhc.1999.0252 DOI: https://doi.org/10.1006/ijhc.1999.0252
Weyer, Johannes; Roos, Michael (2017): Agentenbasierte Modellierung und Simulation. In: TATuP – Zeitschrift für Technikfolgenabschätzung in Theorie und Praxis 26 (3), S.11–16. https://doi.org/10.14512/tatup.26.3.11 DOI: https://doi.org/10.14512/tatup.26.3.11
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