Individual costs and societal benefits of interventions during the COVID-19 pandemic.

Individual and societal reactions to an ongoing pandemic can lead to social dilemmas: In some cases, each individual is tempted to not follow an intervention, but for the whole society, it would be best if they did. Now that in most countries, the extent of regulations to reduce SARS-CoV-2 transmission is very small, interventions are driven by individual decision-making. Assuming that individuals act in their best own interest, we propose a framework in which this situation can be quantified, depending on the protection the intervention provides to a user and to others, the risk of getting infected, and the costs of the intervention. We discuss when a tension between individual and societal benefits arises and which parameter comparisons are important to distinguish between different regimes of intervention use.

To obey or not to obey? Can game theory explain human behavior in the context of coronavirus disease?

The objective of the current study is to explain noncompliance to social distancing rules in Western societies in the absence of a stringent law enforcement mechanism and vaccines. In the first part of the analysis, an evolutionary game theory mechanism of two players is developed. The theoretical model assumes the existence of the prisoner's dilemma due to personal inconveniences associated with mask wearing, hand washing, and lockdowns. The model demonstrates that in the absence of sufficient law enforcement mechanism, and regardless of the initial strategy undertaken, one of the three potential equilibria solutions is the convergence of the system to defection of both players. In the second part of the analysis, based on the freedom-house measures, we provide empirical evidence supporting the notion that law enforcement efficiency is higher in autocratic countries. We show the perseverance of higher projected infection rates per 100,000 persons in democratic countries even 8 months after the outbreak of the COVID-19 pandemic. Given the well-known inclination to cooperate more often than expected by game theory, this real-life outcome of noncompliance is remarkable. Moreover, the recent protests against lockdowns in China might reflect a shift from one equilibrium point (cooperation) to another (noncompliance). © 2023 John Wiley & Sons Ltd.

Coupled disease-vaccination behavior dynamic analysis and its application in COVID-19 pandemic.

Predicting the evolutionary dynamics of the COVID-19 pandemic is a complex challenge. The complexity increases when the vaccination process dynamic is also considered. In addition, when applying a voluntary vaccination policy, the simultaneous behavioral evolution of individuals who decide whether and when to vaccinate must be included. In this paper, a coupled disease-vaccination behavior dynamic model is introduced to study the coevolution of individual vaccination strategies and infection spreading. We study disease transmission by a mean-field compartment model and introduce a non-linear infection rate that takes into account the simultaneity of interactions. Besides, the evolutionary game theory is used to investigate the contemporary evolution of vaccination strategies. Our findings suggest that sharing information with the entire population about the negative and positive consequences of infection and vaccination is beneficial as it boosts behaviors that can reduce the final epidemic size. Finally, we validate our transmission mechanism on real data from the COVID-19 pandemic in France.

Mass Testing and Proactiveness Affect Epidemic Spreading.

The detection and management of diseases become quite complicated when pathogens contain asymptomatic phenotypes amongst their ranks, as evident during the recent COVID-19 pandemic. Spreading of diseases has been studied extensively under the paradigm of susceptible-infected-recovered-deceased (SIRD) dynamics. Various game-theoretic approaches have also addressed disease spread, many of which consider S , I , R , and D as strategies rather than as states. Remarkably, most studies from the above approaches do not account for the distinction between the symptomatic or asymptomatic aspect of the disease. It is well-known that precautionary measures like washing hands, wearing masks and social distancing significantly mitigate the spread of many contagious diseases. Herein, we consider the adoption of such precautions as strategies and treat S , I , R , and D as states. We also attempt to capture the differences in epidemic spreading arising from symptomatic and asymptomatic diseases on various network topologies. Through extensive computer simulations, we examine that the cost of maintaining precautionary measures as well as the extent of mass testing in a population affects the final fraction of socially responsible individuals. We observe that the lack of mass testing could potentially lead to a pandemic in case of asymptomatic diseases. Network topology also seems to play an important role. We further observe that the final fraction of proactive individuals depends on the initial fraction of both infected as well as proactive individuals. Additionally, edge density can significantly influence the overall outcome. Our findings are in broad agreement with the lessons learnt from the ongoing COVID-19 pandemic.

Epidemiological Model of COVID-19 based on Evolutionary Game Theory: Considering the Viral Mutations

With the prevalence of COVID-19 infection, the use of mathematical models for infectious diseases has attracted considerable attention. In a previous study, human behavioral strategies are represented using evolutionary game theory and integrated with the SIR model of the COVID-19 epidemic. However, actual COVID-19 infection has an incubation period. In addition, due to viral mutations, the number of infected people is higher in the second and subsequent epidemics than in the first one. In this study, the previous study that uses evolutionary game theory to represent human behavioral selection in the SIR model is extended to the SEIR model. Then, considering the viral mutations, the relationship between the number of infected people and the risk of infection is formulated. The simulation results indicate that, by increasing the infection rate as the infection spread, the maximum number of infected people at each infection peak continued to increase until the maximum number of simultaneously infected people is reached. This finding indicates that the number of infected people is affected by the higher infection rate caused by the virus mutation. © 2022 IEEE.

On the adoption of nonpharmaceutical interventions during the pandemic: An evolutionary game model.

The adoption of behavioral nonpharmaceutical interventions (NPIs) among the public is essential for tackling the COVID-19 pandemic, yet presents challenges due to the complexity of human behaviors. A large body of literature has utilized classic game theory to investigate the population's decisions regarding the adoption of interventions, where the static solution concept such as the Nash equilibrium is studied. However, individual adoption behavior is not static, instead it is a dynamic process that involves the strategic interactions with other counterparts over time. The study of quantitatively analyzing the dynamics on precautionary behavior during an outbreak is rather scarce. This article fills the research gap by developing an evolutionary game-theoretic framework to model the dynamics of population behavior on the adoption of NPI. We construct the two-group asymmetric game, where behavioral change for each group is characterized by replicator equations. Sensitivity analyses are performed to examine the long-term stability of equilibrium points with respect to perturbation of model parameters. We found that the limiting behavior of intervention adoption in the population consists of only pure strategies in a game setting, indicating that the evolutionary outcome is that everyone either takes up the preventive measure or not. We also applied the framework to examine the mask-wearing behavior, and validated with actual data. Overall, this article provides insights into population dynamics on the adoption of intervention strategy during the outbreak, which can be beneficial for policy makers to better understand the evolutionary trajectory of population behavior.

Promotion strategy for online healthcare platform during the COVID-19 pandemic: Evidence from Spring Rain Doctor in China.

Introduction: Online healthcare platform (OHP) is a new form of medical treatment that solves the problems of an unbalanced distribution of medical resources in China. Especially during the COVID-19 pandemic, OHP has greatly reduced the medical pressure of the hospital and the risk of cross-infection. Methods: Based on self-determination theory (SDT) (Ryan and Deci, 2000), privacy calculus theory (PCT) (Culnan, 1999) and perceived value theory (PVT) (Choi, 2004), this study uses evolutionary game theory to analyze behavioral strategies and their dynamic evolution in the promotion of OHP. Moreover, we conduct numerical simulations with the help of program compilation. Results: The results demonstrate that (1) both the qualification inspection of doctors and the investment in information protection influence doctors' participation in and patients' usage of OHP; (2) both the initial probabilities of doctor participation and patient usage influence the multi-game results; (3) the trend of doctors joining OHP is affected by registration cost, time cost, and reputation loss; and (4) the trend of patients using online healthcare is mainly decided by the cost. Conclusion: This study takes the Spring Rain Doctor as an example to verify the game results. To further popularize online medical treatment among patients, the platform should attach importance to the inspection of doctors and the protection of privacy information and strengthen its publicity in remote places.

Social distancing as a public-good dilemma for socio-economic cost: An evolutionary game approach.

Partaking in social distancing can contribute to a public good affected by the perceived risk of infection and socioeconomic cost. Although social distancing can save lives by slowing down the disease transmission before introducing any effective medical intervention, the economic fallout of social distancing can be brutal for the poorest, vulnerable, and marginalized members of society. We combined the epidemiological and evolutionary game theoretical (EGT) framework through the consolidations of the SEIR (Susceptible-Exposed-Infected-Removed) disease model to analyze behavior enticements in a social distancing dilemma situation with the complex behavioral decision-making aspect. Extensive theoretical and numerical analyses reveal that socioeconomic cost and infected individuals' compliance behavior are critical factors in reining disease spread in the community. Lower cost for maintaining relative safety distance encourages maximum avoidance of public interactions by a detected infected individual. The benefitted fraction due to compliance is parted from the naturally immunized population. People get insignificant benefits from social distancing when the disease transmission rate is too low or crosses critical higher values. Average Social Payoff (ASP) analysis suggests the correspondence of significant safety distance with lowest cost setting as the best strategy to derive the maximum goods. But mounting inherent cost converts social distancing obedience to a public good dilemma.

COVID-19 vaccine incentive scheduling using an optimally controlled reinforcement learning model.

We model Covid-19 vaccine uptake as a reinforcement learning dynamic between two populations: the vaccine adopters, and the vaccine hesitant. Using data available from the Center for Disease Control (CDC), we estimate the payoff matrix governing the interaction between these two groups over time and show they are playing a Hawk-Dove evolutionary game with an internal evolutionarily stable Nash equilibrium (the asymptotic percentage of vaccinated in the population). We then ask whether vaccine adoption can be improved by implementing dynamic incentive schedules that reward/punish the vaccine hesitant, and if so, what schedules are optimal and how effective are they likely to be? When is the optimal time to start an incentive program, how large should the incentives be, and is there a point of diminishing returns? By using a tailored replicator dynamic reinforcement learning model together with optimal control theory, we show that well designed and timed incentive programs can improve vaccine uptake by shifting the Nash equilibrium upward in large populations, but only so much, and incentive sizes above a certain threshold show diminishing returns.

An Evolutionary Game Model of the Supply Decisions between GNPOs and Hospitals during a Public Health Emergency

The distribution of medical supplies tied to the government-owned nonprofit organizations (GNPOs) is crucial to the sustainable and high-quality development of emergency response to public health emergencies. This paper constructs a two-sided GNPO–hospital game model in a Chinese context, and explores the strategies and influencing factors of medical supply distribution in public health emergencies based on evolutionary game theory. The results show that: (1) GNPOs, as the distributor of medical supplies, should choose strategies that balance efficiency and equity as much as possible. (2) Hospitals, as the recipient of medical supplies, should actively choose strategies that maximize the total benefit to society and strengthen trust in GNPOs. Meanwhile, hospital managers need to pay attention to reducing the impact of communication and coordination costs and strive for the reduction of conflicts between different values. (3) The government should strengthen supervision to avoid conflicts between medical distributors and receivers during a public health emergency and ensure the rescue efficiency. This study provides some reference for the sustainable development of emergency relief in public health emergencies.

Research on Emergency Supply Chain Collaboration Based on Tripartite Evolutionary Game

To explore the optimal mechanism of emergency supply chain collaboration when an epidemic crisis occurs, we construct a tripartite evolutionary game model of emergency collaboration among the government, the retailer, and the supplier to explore the interaction of primary strategies and the impact of critical parameters on the evolution and stability of the system. We conduct simulation analysis based on the actual situation of China’s emergency supply chain development. The research results show that in the recognition period of the crisis, the government participates in emergency supervision and the retailer participates in emergency collaboration, but the supplier does not participate in emergency collaboration. However, in the containment period of the crisis, the government increases penalties, provides appropriate emergency subsidies, and improves the efficiency of emergency supervision, which are conducive to promoting the supplier’s participation in emergency collaboration. In the recovery period of the crisis, as the benefits of both the retailer and the supplier participation in emergency collaboration increase, the government will withdraw from emergency supervision gradually. Moreover, the weaker the risk of supply chain enterprises participating in emergency collaboration, the more reasonable the distribution of the collaboration benefits, and the more conducive to the spontaneous emergency collaboration of the retailer and the supplier.

Authority or Autonomy? Exploring Interactions between Central and Peer Punishments in Risk-Resistant Scenarios.

Game theory provides a powerful means to study human cooperation and better understand cooperation-facilitating mechanisms in general. In classical game-theoretic models, an increase in group cooperation constantly increases people's gains, implying that individual gains are a continuously varying function of the cooperation rate. However, this is inconsistent with the increasing number of risk-resistant scenarios in reality. A risk-resistant scenario means once a group does not successfully resist the risk, all individuals lose their resources, such as a community coping with COVID-19 and a village resisting a flood. In other words, individuals' gains are segmented about the collaboration rate. This paper builds a risk-resistant model to explore whether punishment still promotes collaboration when people resist risk. The results show that central and peer punishments can both encourage collaboration but with different characteristics under different risk-resistant scenarios. Specifically, central punishment constrains the collaboration motivated by peer punishment regardless of risk, while peer punishment limits the collaboration induced by central punishment only when the risk is high. Our findings provide insights into the balance between peer punishment from public autonomy and central punishment from central governance, and the proposed model paves the way for the development of richer risk-resistant models.

Experts vs. policymakers in the COVID-19 policy response.

We build an evolutionary game-theoretic model of the interaction between policymakers and experts in shaping the policy response to the COVID-19 pandemic. Players' decisions concern two alternative strategies of pandemic management: a "hard" approach, enforcing potentially unpopular measures such as strict confinement orders, and a "soft" approach, based upon voluntary and short-lived social distancing. Policymakers' decisions may also rely upon expert advice. Unlike experts, policymakers are sensitive to a public consensus incentive that makes lifting restrictions as soon as possible especially desirable. This incentive may conflict with the overall goal of mitigating the effects of the pandemic, leading to a typical policy dilemma. We show that the selection of strategies may be path-dependent, as their initial distribution is a crucial driver of players' choices. Contingent on cultural factors and the epidemiological conditions, steady states in which both types of players unanimously endorse the strict strategy can coexist with others where experts and policymakers agree on the soft strategy, depending on the initial conditions. The model can also lead to attractive asymmetric equilibria where experts and policymakers endorse different strategies, or to cyclical dynamics where the shares of adoption of strategies oscillate indefinitely around a mixed strategy equilibrium. This multiplicity of equilibria can explain the coexistence of contrasting pandemic countermeasures observed across countries in the first wave of the outbreak. Our results suggest that cross-country differences in the COVID-19 policy response need not be the effect of poor decision making. Instead, they can endogenously result from the interplay between policymakers and experts incentives under the local social, cultural and epidemiological conditions.

To Adopt CCU Technology or Not? An Evolutionary Game between Local Governments and Coal-Fired Power Plants

Carbon dioxide capture and utilization (CCU) technology is a significant means by which China can achieve its ambitious carbon neutrality goal. It is necessary to explore the behavioral strategies of relevant companies in adopting CCU technology. In this paper, an evolutionary game model is established in order to analyze the interaction process and evolution direction of local governments and coal-fired power plants. We develop a replicator dynamic system and analyze the stability of the system under different conditions. Based on numerical simulation, we analyze the impact of key parameters on the strategies of stakeholders. The simulation results show that the unit prices of hydrogen and carbon dioxide derivatives have the most significant impact: when the unit price of hydrogen decreases to 15.9 RMB/kg or the unit price of carbon dioxide derivatives increases to 3.4 RMB/kg, the evolutionary stabilization strategy of the system changes and power plants shift to adopt CCU technology. The results of this paper suggest that local governments should provide relevant support policies and incentives for CCU technology deployment, as well as focusing on the synergistic development of CCU technology and renewable energy hydrogen production technology.

Epidemic proximity and imitation dynamics drive infodemic waves during the COVID-19 pandemic

An infodemic - an outpouring of information, including misleading and also fake news - is accompanying the current pandemic caused by SARS-CoV-2. In the absence of valid therapeutic approaches, behavioral responses may seriously affect the social dynamics of contagion, so the infodemic may cause confusion and disorientation in the public, leading to possible individually and socially harmful choices. This new phenomenon requires specific modeling efforts to better understand the complex intertwining of the epidemic and infodemic components of a pandemic crisis, with a view to building an integrative public health approach. We propose three models, from epidemiology to game theory, as potential candidates for the onset of the infodemics and statistically assess their accuracy in reproducing real infodemic waves observed in a data set of 390 million tweets collected worldwide. Our results show that evolutionary game-theory models are the most suitable ones to reproduce the observed infodemic modulations around the onset of the local epidemic wave. Furthermore, we find that the number of confirmed COVID-19 reported cases in each country and worldwide are driving the modeling dynamics with opposite effects. © 2022 authors. Published by the American Physical Society.

Research on dynamic coalition stability of mask production under COVID-19 epidemic

Based on evolutionary game and catastrophe theory, the stability of dynamic coalition of mask production is explored. This research introduces the Gaussian White noise and a Itô stochastic differential equation to develop dynamical equation. Then, probability density function is introduced to build the catastrophe model. Finally, some numerical simulations are given to explore the influence of excess return, default cost and initial cooperation probability. The results show: 1) Catastrophic change occurs suddenly when parameters cross the borderline of bifurcation aggregation;2) The catastrophic change occurs due to external disturbance when parameters are inside the bifurcation aggregation which is easy to recover;3) The excess return affects negatively, and the default cost and the initial cooperation probability affect positively on the stability of dynamic coalition. This research integrates evolutionary game and catastrophe theory and provide a new idea for dynamic coalition research;supports the establishment of mask production dynamic coalition and implementation for unconventional control measures under the COVID-19 epidemic. © 2021, Editorial Board of Journal of Systems Engineering Society of China. All right reserved.

Cooperation dynamics under pandemic risks and heterogeneous economic interdependence.

The spread of COVID-19 and ensuing containment measures have accentuated the profound interdependence among nations or regions. This has been particularly evident in tourism, one of the sectors most affected by uncoordinated mobility restrictions. The impact of this interdependence on the tendency to adopt less or more restrictive measures is hard to evaluate, more so if diversity in economic exposures to citizens' mobility are considered. Here, we address this problem by developing an analytical and computational game-theoretical model encompassing the conflicts arising from the need to control the economic effects of global risks, such as in the COVID-19 pandemic. The model includes the individual costs derived from severe restrictions imposed by governments, including the resulting economic interdependence among all the parties involved in the game. By using tourism-based data, the model is enriched with actual heterogeneous income losses, such that every player has a different economic cost when applying restrictions. We show that economic interdependence enhances cooperation because of the decline in the expected payoffs by free-riding parties (i.e., those neglecting the application of mobility restrictions). Furthermore, we show (analytically and through numerical simulations) that these cross-exposures can transform the nature of the cooperation dilemma each region or country faces, modifying the position of the fixed points and the size of the basins of attraction that characterize this class of games. Finally, our results suggest that heterogeneity among regions may be used to leverage the impact of intervention policies by ensuring an agreement among the most relevant initial set of cooperators.

Optimal governance and implementation of vaccination programmes to contain the COVID-19 pandemic.

Since the recent introduction of several viable vaccines for SARS-CoV-2, vaccination uptake has become the key factor that will determine our success in containing the COVID-19 pandemic. We argue that game theory and social network models should be used to guide decisions pertaining to vaccination programmes for the best possible results. In the months following the introduction of vaccines, their availability and the human resources needed to run the vaccination programmes have been scarce in many countries. Vaccine hesitancy is also being encountered from some sections of the general public. We emphasize that decision-making under uncertainty and imperfect information, and with only conditionally optimal outcomes, is a unique forte of established game-theoretic modelling. Therefore, we can use this approach to obtain the best framework for modelling and simulating vaccination prioritization and uptake that will be readily available to inform important policy decisions for the optimal control of the COVID-19 pandemic.

How do government subsidies promote new energy vehicle diffusion in the complex network context? A three-stage evolutionary game model.

In order to cope with the impact of current coronavirus disease 2019 (COVID-19), the continued extension of financial subsidy period for new energy vehicles at the national level is a strong measure to support the sustainable development of new energy vehicle (NEV) industry. This paper further explores the promotion impact of government subsidies on NEV diffusion, and establishes a three-stage evolutionary game model. Based on the actual application, the NEV diffusion process is simulated in four kinds of authoritative networks. Results show that: (1) in the scale-free network, the subsidy rate must be high enough to promote full NEV diffusion, and the larger the network scale, the higher the threshold of subsidy rate; (2) in the small-world network, the larger the network scale, the more beneficial it is for full NEV diffusion; (3) for the small-scale network, topological characteristics have little effect on NEV diffusion depth, and only affect the speed when NEV diffusion reaches the stable state; (4) for the large-scale network, NEV diffusion in the scale-free network is more sensitive to the subsidy rate than that in the small-world network; (5) network topologies influencing NEV diffusion can be divided into two priorities. Finally, relevant policy recommendations are presented.

Noncompliance With Safety Guidelines as a Free-Riding Strategy: An Evolutionary Game-Theoretic Approach to Cooperation During the COVID-19 Pandemic.

Evolutionary game theory and public goods games offer an important framework to understand cooperation during pandemics. From this perspective, the COVID-19 situation can be conceptualized as a dilemma where people who neglect safety precautions act as free riders, because they get to enjoy the benefits of decreased health risk from others' compliance with policies despite not contributing to or even undermining public safety themselves. At the same time, humans appear to carry a suite of evolved psychological mechanisms aimed at curbing free riding in order to ensure the continued provision of public goods, which can be leveraged to develop more effective measures to promote compliance with regulations. We also highlight factors beyond free riding that reduce compliance rates, such as the emergence of conspiratorial thinking, which seriously undermine the effectiveness of measures to suppress free riding. Together, the current paper outlines the social dynamics that occur in public goods dilemmas involving the spread of infectious disease, highlights the utility and limits of evolutionary game-theoretic approaches for COVID-19 management, and suggests novel directions based on emerging challenges to cooperation.