Game theoretic modelling of infectious disease dynamics and intervention methods: a review (original) (raw)
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Journal of Biological Dynamics, 2020
We review research studies which use game theory to model the decision-making of individuals during an epidemic, attempting to classify the literature and identify the emerging trends in this field. The literature is classified based on (i) type of population modelling (classical or network-based), (ii) frequency of the game (non-repeated or repeated), and (iii) type of strategy adoption (self-learning or imitation). The choice of model is shown to depend on many factors such as the immunity to the disease, the strength of immunity conferred by the vaccine, the size of population and the level of mixing therein. We highlight that while early studies used classical compartmental modelling with self-learning games, in recent years, there is a substantial growth of network-based modelling with imitation games. The review indicates that game theory continues to be an effective tool to model decision-making by individuals with respect to intervention (vaccination or social distancing).
Game Theoretic Modeling of Infectious Disease Transmission with Delayed Emergence of Symptoms
Games, 2020
Modeling the spread of infectious diseases and social responses is one method that can help public health policy makers improve the control of epidemic outbreaks and make better decisions about vaccination costs, the number of mandatory vaccinations, or investment in media efferts to inform the public of disease threats. Incubation period—the period when an individual has been exposed to a disease and could be infectious but is not yet aware of it—is one factor that can affect an epidemic outbreak, and considering it when modeling outbreaks can improve model accuracy. A change in outbreak activity can occur from the time a person becomes infected until they become aware of infection when they can transmit the disease but their social group considers them a susceptible individual and not an infectious one. This study evaluates the effect of this delay between the time of infection of an individual and the time of diagnosis of the infection (incubation period) in an epidemic outbreak. This study investigates the social dynamics of vaccination and transmission in such epidemic outbreaks, using a model of the public goods game.
“Wait and see” vaccinating behaviour during a pandemic: A game theoretic analysis
Vaccine, 2011
During the 2009 H1N1 pandemic, many individuals did not seek vaccination immediately but rather decided to "wait and see" until further information was available on vaccination costs. This behaviour implies two sources of strategic interaction: as more individuals become vaccinated, both the perceived vaccination cost and the probability that susceptible individuals become infected decline. Here we analyze the outcome of these two strategic interactions by combining game theory with a disease transmission model during an outbreak of a novel influenza strain. The model exhibits a "wait and see" Nash equilibrium strategy, with vaccine delayers relying on herd immunity and vaccine safety information generated by early vaccinators. This strategic behaviour causes the timing of the epidemic peak to be strongly conserved across a broad range of plausible transmission rates, in contrast to models without such adaptive behaviour. The model exhibits not only feedback mechanisms but also a feed-forward mechanism: a high initial perceived vaccination cost perpetuates high perceived vaccine costs (and lower vaccine coverage) throughout the remainder of the outbreak. This suggests that any effect of risk communication at the start of a pandemic outbreak will be amplified compared to the same amount of risk communication effort distributed throughout the outbreak.
Game Theory : A Case of Infectious Diseases
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2020
Game theory is a mathematical model which deals with interactions between various entities by analyzing the strategies and choices. In today’s world, Game Theory is being extensively used in fields like computer science, economics, sociology, political science, and so on, due to its versatile nature and applications in numerous conflicts and problems. The application of game theory has been extended to real life problems also due to its versatility and robustness. In this research, various game theory methodologies applied during pandemic was reviewed. Various aspects of these methodologies were highlighted such as methods applied, description, expected result and limitation. This research will act as a reliable and efficient way of understanding the concept of game theory and its application in combating infectious diseases, analyze and eventually understand different strategic scenarios. The main importance of game theory is to formulate the alternative strategy to compete with one another and in the same sense it is an essential tool for decision making process according to fluctuations in relevant contents. These reviewed methodologies would be further categorized into prevent, control or both based on the application they favour most.
Identification and Control of Game-Based Epidemic Models
Games, 2022
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 ...
Preface to Special Issue on Dynamic Games for Modeling and Control of Epidemics
Dynamic Games and Applications
This preface introduces the special issue on Dynamic Games for Modeling and Control of Epidemics. It showcases 12 papers with timely contributions to dynamic games and their applications to the modeling, analysis, and control of epidemics. The papers in this collection connect dynamic games and epidemic models to address the recent challenges related to screening, containment, and mitigation strategies for epidemics. This collection covers broad application areas in networks, human behaviors, and epidemiology as well as a diverse range of dynamic game methods, including evolutionary games, differential games, and mean-field games. This article is part of the topical collection "Modeling and Control of Epidemics" edited by Quanyan Zhu, Elena Gubar and Eitan Altman.
Applied Mathematics and Computation, 2019
We build a new analytic scheme that competently reproduces the decision-making process of choosing an imperfect provision based on the evolutionary game theory dovetailed with the SIR model for epidemic spreading dynamics. Aside from considering the two extreme options whether or not taking vaccination, we consider an 'intermediate defense measure' (IDM) that emulates hand-washing, masking, gargling, and taking energy drinks, defined as the third strategy while taking vaccination as well as IDM at the same time as the fourth strategy. In the present study, each of the proposed three imperfect provisions is able to oppress infectious diseases like Flu, Influenza, Ebola, and SARS during an epidemic season with certain extent. Considering an infinite and well-mixed population, a new analytic framework is built to take care of those three cases instead of perfect vaccination. Unlike MAS (multi-agent simulation) approach we conduct our study throughout using the socalled theoretical approach. Besides that, three different strategy updating rules based on evolutionary game theory have also been considered in our proposed model. We successfully obtain phase diagrams showing the final epidemic size, social average payoff and the respective fractions of the different strategy holders using various values of effectiveness and efficiency coefficients. Finally, a comprehensive discussion is made with comparison among the two-, three-and four-strategy models to get a holistic idea justifying how imperfect provisions work during an epidemic spreading.
Modeling Behavioral Response to Vaccination Using Public Goods Game
IEEE Transactions on Computational Social Systems, 2019
Epidemics of infectious disease can be traced back to the early days of mankind. Only in the last two centuries vaccination has become a viable strategy to prevent such epidemics. In addition to the clinical efficacy of this strategy, the behavior and public attitudes affect the success of vaccines. This paper describes modeling the efficacy of vaccination considering the cost and benefit of vaccination to individual players. The model is based on the public goods game and is presented as a spatial game on a lattice. Using this model, individuals can contribute to the public health by paying the cost of vaccination or choose to be protected by the public who is vaccinated rather than pay the cost and share the risk of vaccination. Thus, in this model individuals can choose to stay susceptible, can become infected, or choose to vaccinate once in each episode. This paper presents the behavioral changes of the population and the cost to the society as a function of the cost of vaccines, cost of being infected, and the "fear factor" created by the public media.
Optimal vaccine roll-out strategies including social distancing for pandemics
Non-pharmacological interventions (NPIs), principally social distancing, in combination with effective vaccines, aspire to develop a protective immunity shield against pandemics and particularly against the COVID-19 pandemic. In this study, an agent-based network model with small-world topology is employed to find optimal policies against pandemics, including social distancing and vaccination strategies. The agents' states are characterized by a variation of the SEIR model (susceptible, exposed, infected, recovered). To explore optimal policies, an equation-free method is proposed to solve the inverse problem of calibrating an agent's infection rate with respect to the vaccination efficacy. The results show that prioritizing the first vaccine dose in combination with mild social restrictions, is sufficient to control the pandemic, with respect to the number of deaths. Moreover, for the same mild number of social contacts, we find an optimal vaccination ratio of 0.85 between older people of ages > 65 compared to younger ones.
Modeling infection spread and behavioral change using spatial games
This paper presents a methodology that combines information transmission, contact networks, and changes of human behaviors in modeling the dynamics of infectious diseases. The methodology presented is based on a spatial evolutionary game with additional information representing human behavior. This approach is used to model the transmission process of infectious disease, which emphasizes the human response and information transmission in a social context. It combines the advantages of evolutionary game theory with modeling the spontaneous changes of human behaviors based on the balance of benefits and costs. The model assumes rational participants who use information acquired to make individual decisions. This novel modeling approach shows the global spread of infection considering an individual human behavior.