Game-theoretic modeling of collective decision making during epidemics

The application of innovative ecosystems to build resilient communities in response to major public health events

In recent years, major public health events have had a significant and far-reaching impact on communities. As a response, there has been an increasing interest in enhancing community resilience through innovative ecosystems that involve diverse stakeholders with varying needs and demands. This study investigates the application of innovative ecosystems to improve community resilience in the face of major public health events by utilizing a sequential game approach to balance the interests of government, community, and residents. Subsequently, a comprehensive questionnaire survey was conducted among key stakeholders to ascertain their objectives, requirements and concerns for the innovation ecosystem based on the analysis results of the game model. The reliability and effectiveness of the proposed research method were verified through the analysis and verification of the sequence game model and questionnaire survey results. Finally, according to our analysis results, we propose countermeasures for promoting innovative ecosystems to improve community resilience. The research results indicate that the successful implementation of innovative ecosystems requires consideration of the different needs of stakeholders such as government officials, community members, and residents. Combining these perspectives can effectively promote such systems while enhancing the community's resilience to major public health events.

Heterogeneous risk tolerance, in-groups, and epidemic waves

There is a growing interest in the joint modeling of the dynamics of disease and health-related beliefs and attitudes, but coupling mechanisms are yet to be understood. We introduce a model where risk information, which can be delayed, comes in two flavors, including historical risk derived from perceived incidence data and predicted risk information. Our model also includes an interpretation domain where the behavioral response to risk information is subject to in-group pressure. We then simulate how the strength of behavioral reaction impacts epidemic severity as measured by epidemic peak size, number of waves, and final size. Simulated behavioral response is not effective when the level of protection that prophylactic behavior provides is as small as 50% or lower. At a higher level of 75% or more, we see the emergence of multiple epidemic waves. In addition, simulations show that different behavioral response profiles can lead to various epidemic outcomes that are non-monotonic with the strength of reaction to risk information. We also modeled heterogeneity in the response profile of a population and find they can lead to less severe epidemic outcome in terms of peak size.

Epidemic spreading under game-based self-quarantine behaviors: The different effects of local and global information

During the outbreak of an epidemic, individuals may modify their behaviors in response to external (including local and global) infection-related information. However, the difference between local and global information in influencing the spread of diseases remains inadequately explored. Here, we study a simple epidemic model that incorporates the game-based self-quarantine behavior of individuals, taking into account the influence of local infection status, global disease prevalence, and node heterogeneity (non-identical degree distribution). Our findings reveal that local information can effectively contain an epidemic, even with only a small proportion of individuals opting for self-quarantine. On the other hand, global information can cause infection evolution curves shaking during the declining phase of an epidemic, owing to the synchronous release of nodes with the same degree from the quarantined state. In contrast, the releasing pattern under the local information appears to be more random. This shaking phenomenon can be observed in various types of networks associated with different characteristics. Moreover, it is found that under the proposed game-epidemic framework, a disease is more difficult to spread in heterogeneous networks than in homogeneous networks, which differs from conventional epidemic models.

Policy choices and compliance behavior in pandemic times

In this paper, we model an evolutionary noncooperative game between politicians and citizens that, given the level of infection, describes the observed variety of mitigation policies and citizens' compliance during the COVID-19 pandemic period. Our results show that different stable equilibria exist and that different ways/paths exist to reach these equilibria may be present, depending on the choice of parameters. When the parameters are chosen opportunistically, in the short run, our model generates transitions between hard and soft policy measures to deal with the pandemic. In the long-run, convergence is achieved toward one of the possible stable steady states (obey or not obey lockdown rules) as functions of politicians' and citizens' incentives.

Multilayer networks with higher-order interaction reveal the impact of collective behavior on epidemic dynamics

The ongoing COVID-19 pandemic has inflicted tremendous economic and societal losses. In the absence of pharmaceutical interventions, the population behavioral response, including situational awareness and adherence to non-pharmaceutical intervention policies, has a significant impact on contagion dynamics. Game-theoretic models have been used to reproduce the concurrent evolution of behavioral responses and disease contagion, and social networks are critical platforms on which behavior imitation between social contacts, even dispersed in distant communities, takes place. Such joint contagion dynamics has not been sufficiently explored, which poses a challenge for policies aimed at containing the infection. In this study, we present a multi-layer network model to study contagion dynamics and behavioral adaptation. It comprises two physical layers that mimic the two solitary communities, and one social layer that encapsulates the social influence of agents from these two communities. Moreover, we adopt high-order interactions in the form of simplicial complexes on the social influence layer to delineate the behavior imitation of individual agents. This model offers a novel platform to articulate the interaction between physically isolated communities and the ensuing coevolution of behavioral change and spreading dynamics. The analytical insights harnessed therefrom provide compelling guidelines on coordinated policy design to enhance the preparedness for future pandemics.

Activity-driven network modeling and control of the spread of two concurrent epidemic strains

The emergency generated by the current COVID-19 pandemic has claimed millions of lives worldwide. There have been multiple waves across the globe that emerged as a result of new variants, due to arising from unavoidable mutations. The existing network toolbox to study epidemic spreading cannot be readily adapted to the study of multiple, coexisting strains. In this context, particularly lacking are models that could elucidate re-infection with the same strain or a different strain-phenomena that we are seeing experiencing more and more with COVID-19. Here, we establish a novel mathematical model to study the simultaneous spreading of two strains over a class of temporal networks. We build on the classical susceptible-exposed-infectious-removed model, by incorporating additional states that account for infections and re-infections with multiple strains. The temporal network is based on the activity-driven network paradigm, which has emerged as a model of choice to study dynamic processes that unfold at a time scale comparable to the network evolution. We draw analytical insight from the dynamics of the stochastic network systems through a mean-field approach, which allows for characterizing the onset of different behavioral phenotypes (non-epidemic, epidemic, and endemic). To demonstrate the practical use of the model, we examine an intermittent stay-at-home containment strategy, in which a fraction of the population is randomly required to isolate for a fixed period of time.

Promotion Policies for Electric Vehicle Diffusion in China Considering Dynamic Consumer Preferences: A Network-Based Evolutionary Analysis

An improved understanding of how policies can promote the diffusion of electric vehicles (EVs) is critical to achieving sustainable development. Previous studies of EV diffusion dynamics have paid insufficient attention to consumer preferences. In this paper, a network-based evolutionary game model considering dynamic consumer preference is constructed to study EV diffusion. Through numerical experiments, the evolutionary processes and results of various promotion policies, including carbon taxes, production subsidies, purchase subsidies, and information policy on EV diffusion, are simulated. In particular, this paper explores the differentiated effects of supply-side policies and demand-side policies. The simulation results indicate that: (1) The effectiveness of promotion policies is sensitive to the size of the manufacturer network, and large networks can dampen periodical fluctuations in diffusion rates. (2) Supply-side carbon taxes and subsidies facilitate a steady diffusion of EVs. However, compared with the sustained effectiveness of subsidies, carbon taxes may inhibit the rapid penetration of EVs. (3) Implementing purchase subsidies in the early stages of diffusion is more effective than production subsidies, but the potential uncertainty of demand-side subsidies should be noted. (4) The impact of information policy on the evolutionary trend of EV diffusion is pronounced but is a longer-term impact, requiring a long enough implementation horizon.

Identification and Control of Game-Based Epidemic Models

The effectiveness of control measures against the diffusion of the COVID-19 pandemic is grounded on the assumption that people are prepared and disposed to cooperate. From a strategic decision point of view, cooperation is the unreachable strategy of the Prisoner’s Dilemma game, where the temptation to exploit the others and the fear of being betrayed by them drives the people’s behavior, which eventually results in a fully defective outcome. In this work, we integrate a standard epidemic model with the replicator equation of evolutionary games in order to study the interplay between the infection spreading and the propensity of people to be cooperative under the pressure of the epidemic. The developed model shows high performance in fitting real measurements of infected, recovered and dead people during the whole period of COVID-19 epidemic spread, from March 2020 to September 2021 in Italy. The estimated parameters related to cooperation result to be significantly correlated with vaccination and screening data, thus validating the model. The stability analysis of the multiple steady states present in the proposed model highlights the possibility to tune fundamental control parameters to dramatically reduce the number of potential dead people with respect to the non-controlled case.

Evolutionary Game Theoretic Insights on the SIRS Model of the COVID-19 Pandemic

The effectiveness of control measures against the diffusion of the COVID-19 pandemic is grounded on the assumption that people are prepared and disposed to cooperate. From a strategic decision point of view, cooperation is the unreachable strategy of the prisoner's dilemma game, where the temptation to exploit the others and the fear to be betrayed by them drives the people behavior, which eventually results fully defective. In this work, we integrate the SIRS epidemic model with the replicator equation of evolutionary games in order to study the interplay between the infection spreading and the propensity of people to become cooperative under the pressure of the epidemic. We find that the developed model possesses several steady states, including fully or partially cooperative ones and that the presence of such states allows to take the disease under control. Moreover, assuming a seasonal variation of the infection rate, the system presents rich dynamics, including chaotic behavior and epidemic extinction.