scholarly journals COVID-19: A Data-Driven Mean-Field-Type Game Perspective

2020 ◽  
Author(s):  
Hamidou Tembine
Keyword(s):  
Mean Field ◽  
Decision Makers ◽  
Data Driven ◽  
Feedback Form ◽  
State Spaces ◽  
Hospital Capacity ◽  
Field Type ◽  
Adjoint Systems ◽  
Continuous State ◽  
Reported Data

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, mobility map on local areas, in-city, inter-cities, and international. It 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-Gaussianity and non-exponential properties in 15+ countries.

scholarly journals COVID-19: Data-Driven Mean-Field-Type Game Perspective

Games ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 51
Author(s):  
Hamidou Tembine
Keyword(s):  
Mean Field ◽  
Decision Makers ◽  
Data Driven ◽  
Feedback Form ◽  
State Spaces ◽  
Field Type ◽  
Adjoint Systems ◽  
Continuous State ◽  
Non Gaussian ◽  
Reported Data

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.

Data-Driven Mean-Field-Type Games

2021 ◽  
pp. 389-404
Author(s):  
Julian Barreiro-Gomez ◽  
Hamidou Tembine
Keyword(s):  
Mean Field ◽  
Data Driven ◽  
Field Type

scholarly journals Hierarchical Structures and Leadership Design in Mean-Field-Type Games with Polynomial Cost

Games ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 30
Author(s):  
Zahrate El Oula Frihi ◽  
Julian Barreiro-Gomez ◽  
Salah Eddine Choutri ◽  
Hamidou Tembine
Keyword(s):  
Control Strategies ◽  
Mean Field ◽  
Single Layer ◽  
Hierarchical Structures ◽  
Hierarchical Systems ◽  
Feedback Form ◽  
Field Type ◽  
The Hierarchical Structure ◽  
Linear State ◽  
Multiple Followers

This article presents a class of hierarchical mean-field-type games with multiple layers and non-quadratic polynomial costs. The decision-makers act in sequential order with informational differences. We first examine the single-layer case where each decision-maker does not have the information about the other control strategies. We derive the Nash mean-field-type equilibrium and cost in a linear state-and-mean-field feedback form by using a partial integro-differential system. Then, we examine the Stackelberg two-layer problem with multiple leaders and multiple followers. Numerical illustrations show that, in the symmetric case, having only one leader is not necessarily optimal for the total sum cost. Having too many leaders may also be suboptimal for the total sum cost. The methodology is extended to multi-level hierarchical systems. It is shown that the order of the play plays a key role in the total performance of the system. We also identify a specific range of parameters for which the Nash equilibrium coincides with the hierarchical solution independently of the number of layers and the order of play. In the heterogeneous case, it is shown that the total cost is significantly affected by the design of the hierarchical structure of the problem.

scholarly journals A Retrospective Analysis of the COVID-19 Pandemic Evolution in Italy

Biology ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 311
Author(s):  
Anna Fochesato ◽  
Giulia Simoni ◽  
Federico Reali ◽  
Giulia Giordano ◽  
Enrico Domenici ◽  
...  
Keyword(s):  
Retrospective Analysis ◽  
Policy Design ◽  
Vaccination Campaign ◽  
Optimization Procedure ◽  
Decision Makers ◽  
Original Strain ◽  
Epidemiological Modeling ◽  
To Come ◽  
Reported Data ◽  
Refined Version

Late 2019 saw the outbreak of COVID-19, a respiratory disease caused by the new coronavirus SARS-CoV-2, which rapidly turned into a pandemic, killing more than 2.77 million people and infecting more than 126 million as of late March 2021. Daily collected data on infection cases and hospitalizations informed decision makers on the ongoing pandemic emergency, enabling the design of diversified countermeasures, from behavioral policies to full lockdowns, to curb the virus spread. In this context, mechanistic models could represent valuable tools to optimize the timing and stringency of interventions, and to reveal non-trivial properties of the pandemic dynamics that could improve the design of suitable guidelines for future epidemics. We performed a retrospective analysis of the Italian epidemic evolution up to mid-December 2020 to gain insight into the main characteristics of the original strain of SARS-CoV-2, prior to the emergence of new mutations and the vaccination campaign. We defined a time-varying optimization procedure to calibrate a refined version of the SIDARTHE (Susceptible, Infected, Diagnosed, Ailing, Recognized, Threatened, Healed, Extinct) model and hence accurately reconstruct the epidemic trajectory. We then derived additional features of the COVID-19 pandemic in Italy not directly retrievable from reported data, such as the estimate of the day zero of infection in late November 2019 and the estimate of the spread of undetected infection. The present analysis contributes to a better understanding of the past pandemic waves, confirming the importance of epidemiological modeling to support an informed policy design against epidemics to come.

Mean-Field-Type Games for Engineers

2021 ◽  
Author(s):  
Julian Barreiro-Gomez ◽  
Hamidou Tembine
Keyword(s):  
Mean Field ◽  
Field Type
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