Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/257469 
Year of Publication: 
2020
Citation: 
[Journal:] Games [ISSN:] 2073-4336 [Volume:] 11 [Issue:] 4 [Article No.:] 51 [Publisher:] MDPI [Place:] Basel [Year:] 2020 [Pages:] 1-107
Publisher: 
MDPI, Basel
Abstract: 
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.
Subjects: 
data-driven
dynamics
game theory
Persistent Identifier of the first edition: 
Creative Commons License: 
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Document Type: 
Article
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