EPROACH: A Population Vaccination Game for Strategic Information Design to Enable Responsible COVID Reopening

The COVID-19 lockdowns have created a significant socioeconomic impact on our society. In this paper, we propose a population vaccination game framework, called EPROACH, to design policies for reopenings that guarantee post-opening public health safety. In our framework, a population of players decides whether to vaccinate based on the public and private information they receive. The reopening is captured by the switching of the game state. The insights obtained from our framework include the appropriate vaccination coverage threshold for safe-reopening and information-based methods to incentivize individual vaccination decisions. In particular, our framework bridges the modeling of the strategic behaviors of the populations and the spreading of infectious diseases. This integration enables finding the threshold which guarantees a disease-free epidemic steady state under the population's Nash equilibrium vaccination decisions. The equilibrium vaccination decisions depend on the information received by the agents. It makes the steady-state epidemic severity controllable through information. We find that the externalities created by reopening lead to the coordination of the players in the population and result in a unique Nash equilibrium. We use numerical experiments to corroborate the results and illustrate the design of public information for responsible reopening. © 2022 American Automatic Control Council.

Control of COVID-19 transmission dynamics, a game theoretical approach

We analyze a mathematical model of COVID-19 transmission control, which includes the interactions among different groups of the population: vaccinated, susceptible, exposed, infectious, super-spreaders, hospitalized and fatality, based on a system of ordinary differential equations, which describes compartment model of a disease and its treatment. The aim of the model is to predict the development disease under different types of treatment during some fixed time period. We develop a game theoretic approach and a dual dynamic programming method to formulate optimal conditions of the treatment for an administration of a vaccine. Next, we calculate numerically an optimal treatment. © 2022, The Author(s), under exclusive licence to Springer Nature B.V.

Two-stage game model of code sharing agreement choosing and tickets' pricing

The code sharing is the popular and effective cooperation of the airlines. First, this paper applies the spokes model to describe the different preferences of passengers for three airlines, and proposes the twostage game model and the simple method for the code sharing agreement choosing stage and the tickets' pricing stage under the free-sale model. Second, we use the numerical example to show the feasibility of the proposed models and method. By comparison and analysis, the Nash equilibrium situations of the two-stage game can be obtained. This study demonstrates: The code sharing agreement saves the total operating cost, but it raises the average airfare of the aviation market, so that the airlines' profits increase and the passengers' surpluses decrease;the government should promote the realization of better Nash equilibrium situation in order to maximize the total social welfare;to further improve the total social welfare during the period of Corona Virus Disease-19, the government could give the airlines some cost subsidies or adjust the passengers' psychological costs. Therefore, this paper provides significant theoretical and methodical supports to optimize the choices of the code sharing agreements and to improve the total social welfare in the aviation market. © 2022, Editorial Board of Journal of Systems Engineering Society of China. All right reserved.

A heterogeneous study on online education outcomes during the quarantine period

This paper examines the impact of learning environments, educators’ attention, and students’ personal qualities on the outcomes of online learning. To approach conclusions, this paper describes the heterogeneous effect of these three types of factors on online learning outcomes and employs a fixed-effect regression to examine what exact factors constitute a good learning environment, appropriate educators’ attention and personal qualities that benefit students’ learning efficiency. The fixed-effect regression is applied on data of students’ answers to questionnaires and their final exam scores collected anonymously at three high schools in northern China during COVID-19 quarantine periods when Chinese high schools all conduct lessons and mid-term exams online but the final exam off-line, which is a major differentiation factor of this study. This paper contributes to three major findings: students who are more self-disciplined benefit more from the improvement of learning environments, practicing sports regularly improves online learning outcomes, and educators should find a balance between ensuring students are focused and not giving students too much pressure. © 2021 Association for Computing Machinery.