Artikel
COVID-19: Data-driven mean-field-type game perspective
In this article, a class of mean-field-type games with discrete-continuous state spaces is considered. We establish Bellman systems which provide sufficiency conditions for mean-field-type equilibria in state-and-mean-field-type feedback form. We then derive unnormalized master adjoint systems (MASS). The methodology is shown to be flexible enough to capture multi-class interaction in epidemic propagation in which multiple authorities are risk-aware atomic decision-makers and individuals are risk-aware non-atomic decision-makers. Based on MASS, we present a data-driven modelling and analytics for mitigating Coronavirus Disease 2019 (COVID-19). The model integrates untested cases, age-structure, decision-making, gender, pre-existing health conditions, location, testing capacity, hospital capacity, and a mobility map of local areas, including in-cities, inter-cities, and internationally. It is shown that the data-driven model can capture most of the reported data on COVID-19 on confirmed cases, deaths, recovered, number of testing and number of active cases in 66+ countries. The model also reports non-Gaussian and non-exponential properties in 15+ countries.
- Language
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Englisch
- Bibliographic citation
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Journal: Games ; ISSN: 2073-4336 ; Volume: 11 ; Year: 2020 ; Issue: 4 ; Pages: 1-107 ; Basel: MDPI
- Classification
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Wirtschaft
- Subject
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data-driven
dynamics
game theory
- Event
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Geistige Schöpfung
- (who)
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Tembine, Hamidou
- Event
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Veröffentlichung
- (who)
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MDPI
- (where)
-
Basel
- (when)
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2020
- DOI
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doi:10.3390/g11040051
- Handle
- Last update
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10.03.2025, 11:44 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Artikel
Associated
- Tembine, Hamidou
- MDPI
Time of origin
- 2020
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