Vaccination Game Research Articles

Risk assessment for infectious disease and its impact on voluntary vaccination behavior in social networks

Achievement of the herd immunity is essential for preventing the periodic spreading of an infectious disease such as the flu. If vaccination is voluntary, as vaccination coverage approaches the critical level required for herd immunity, there is less incentive for individuals to be vaccinated; this results in an increase in the number of so-called “free-riders” who craftily avoid infection via the herd immunity and avoid paying any cost. We use a framework originating in evolutionary game theory to investigate this type of social dilemma with respect to epidemiology and the decision of whether to be vaccinated. For each individual in a population, the decision on vaccination is associated with how one assesses the risk of infection. In this study, we propose a new risk-assessment model in a vaccination game when an individual updates her strategy, she compares her own payoff to a net payoff obtained by averaging a collective payoff over individuals who adopt the same strategy as that of a randomly selected neighbor. In previous studies of vaccination games, when an individual updates her strategy, she typically compares her payoff to the payoff of a randomly selected neighbor, indicating that the risk for changing her strategy is largely based on the behavior of one other individual, i.e., this is an individual-based risk assessment. However, in our proposed model, risk assessment by any individual is based on the collective success of a strategy and not on the behavior of any one other individual. For strategy adaptation, each individual always takes a survey of the degree of success of a certain strategy that one of her neighbors has adopted, i.e., this is a strategy-based risk assessment. Using computer simulations, we determine how these two different risk-assessment methods affect the spread of an infectious disease over a social network. The proposed model is found to benefit the population, depending on the structure of the social network and cost of vaccination. Our results suggest that individuals (or governments) should understand the structure of their social networks at the regional level, and accordingly, they should adopt an appropriate risk-assessment methodology as per the demands of the situation.

The impact of other-regarding tendencies on the spatial vaccination game

Using the evolutionary game framework to describe the vaccination behaviors of individuals, this work focuses on the impact of other-regarding tendencies on collective decision-making of vaccination. With extensive computational simulations, we find that when the vaccination cost is small or moderate, the variance of vaccination coverage as well as epidemic size is monotonically depending on the other-regarding effect. However, with a high price of vaccine, a moderate deviation from being entirely self-centered results in a poor communal vaccination benefit. In addition, we also find that the other-regarding effect may lead to an optimal total social cost.

Dynamic vaccination games and hybrid dynamical systems

This paper relates variational inequalities and hybrid dynamical systems to describe the evolution of a class of noncooperative games. To describe such evolution, we define a hybrid system based on a continuous dynamics described by a projected system, and on a finite set of exogenous event times. This hybrid system is then used to track evolution strategies. We apply our results to the evolution of population groups’ strategies playing a noncooperative vaccinating game over a finite time interval.

A vaccination game based on public health actions and personal decisions

Susceptible-infective-removed (SIR) models are commonly used for representing the spread of contagious diseases. A SIR model can be described in terms of a probabilistic cellular automaton (PCA), where each individual (corresponding to a cell of the PCA lattice) is connected to others by a random network favoring local contacts. Here, this framework is employed for investigating the consequences of applying vaccine against the propagation of a contagious infection, by considering vaccination as a game, in the sense of game theory. In this game, the players are the government and the susceptible newborns. In order to maximize their own payoffs, the government attempts to reduce the costs for combating the epidemic, and the newborns may be vaccinated only when infective individuals are found in their neighborhoods and/or the government promotes an immunization program. As a consequence of these strategies supported by cost-benefit analysis and perceived risk, numerical simulations show that the disease is not fully eliminated and the government implements quasi-periodic vaccination campaigns.

A general approach for population games with application to vaccination

Reconciling the interests of individuals with the interests of communities is a major challenge in designing and implementing health policies. In this paper, we present a technique based on a combination of mechanistic population-scale models, Markov decision process theory and game theory that facilitates the evaluation of game theoretic decisions at both individual and community scales. To illustrate our technique, we provide solutions to several variants of the simple vaccination game including imperfect vaccine efficacy and differential waning of natural and vaccine immunity. In addition, we show how path-integral approaches can be applied to the study of models in which strategies are fixed waiting times rather than exponential random variables. These methods can be applied to a wide variety of decision problems with population-dynamic feedbacks.

Dynamic vaccination games and variational inequalities on time-dependent sets

This paper presents a model of a dynamic vaccination game in a population consisting of a collection of groups, each of which holds distinct perceptions of vaccinating versus non-vaccinating risks. Vaccination is regarded here as a game due to the fact that the payoff to each population group depends on the so-called perceived probability of getting infected given a certain level of the vaccine coverage in the population, a level that is generally obtained by the vaccinating decisions of other members of a population. The novelty of this model resides in the fact that it describes a repeated vaccination game (over a finite time horizon) of population groups whose sizes vary with time. In particular, the dynamic game is proven to have solutions using a parametric variational inequality approach often employed in optimization and network equilibrium problems. Moreover, the model does not make any assumptions upon the level of the vaccine coverage in the population, but rather computes this level as a final result. This model could then be used to compute possible vaccine coverage scenarios in a population, given information about its heterogeneity with respect to perceived vaccine risks. In support of the model, some theoretical results were advanced (presented in the appendix) to ensure that computation of optimal vaccination strategies can take place; this means, the theory states the existence, uniqueness and regularity (in our case piecewise continuity) of the solution curves representing the evolution of optimal vaccination strategies of each population group.

Optimal vaccination choice, vaccination games, and rational exemption: An appraisal

A threat for vaccination policies might be the onset of “rational” exemption, i.e. the family's decision not to vaccinate children after a seemingly rational comparison between the perceived risk of infection and the perceived risk of vaccine side effects. We study the implications of rational exemption by models of vaccination choice. By a simple model of individual choice we first prove the “elimination impossible” result in presence of informed families, i.e. aware of herd immunity, and suggest that limited information might explain patterns of universal vaccination. Next, we investigate vaccination choice in a game-theoretic framework for communities stratified into two groups, “pro” and “anti” vaccinators, having widely different perceived costs of infection and of vaccine side effects. We show that under informed families neither a Nash nor a Stackelberg behaviour (characterized, respectively, by players acting simultaneously and by an asymmetric situation with a “leader” and a “follower) allow elimination, unless “pro-vaccinators” assign no costs to vaccine side effects. Elimination turns out to be possible when cooperation is encouraged by a social planner, provided, however, he incorporates in the “social loss function” the preferences of anti-vaccinators only. This allows an interpretation of the current Italian vaccination policy.

Dynamic equilibria of group vaccination strategies in a heterogeneous population

In this paper we present an evolutionary variational inequality model of vaccination strategies games in a population with a known vaccine coverage profile over a certain time interval. The population is considered to be heterogeneous, namely its individuals are divided into a finite number of distinct population groups, where each group has different perceptions of vaccine and disease risks. Previous game theoretical analyses of vaccinating behaviour have studied the strategic interaction between individuals attempting to maximize their health states, in situations where an individual's health state depends upon the vaccination decisions of others due to the presence of herd immunity. Here we extend such analyses by applying the theory of evolutionary variational inequalities (EVI) to a (one parameter) family of generalized vaccination games. An EVI is used to provide conditions for existence of solutions (generalized Nash equilibria) for the family of vaccination games, while a projected dynamical system is used to compute approximate solutions of the EVI problem. In particular we study a population model with two groups, where the size of one group is strictly larger than the size of the other group (a majority/minority population). The smaller group is considered much less vaccination inclined than the larger group. Under these hypotheses, considering that the vaccine coverage of the entire population is measured during a vaccine scare period, we find that our model reproduces a feature of real populations: the vaccine averse minority will react immediately to a vaccine scare by dropping their strategy to a nonvaccinator one; the vaccine inclined majority does not follow a nonvaccinator strategy during the scare, although vaccination in this group decreases as well. Moreover we find that there is a delay in the majority's reaction to the scare. This is the first time EVI problems are used in the context of mathematical epidemiology. The results presented emphasize the important role played by social heterogeneity in vaccination behaviour, while also highlighting the valuable role that can be played by EVI in this area of research.

Dynamics of Vaccination Strategies via Projected Dynamical Systems

Previous game theoretical analyses of vaccinating behaviour have underscored the strategic interaction between individuals attempting to maximise their health states, in situations where an individual's health state depends upon the vaccination decisions of others due to the presence of herd immunity. Here, we extend such analyses by applying the theories of variational inequalities (VI) and projected dynamical systems (PDS) to vaccination games. A PDS provides a dynamics that gives the conditions for existence, uniqueness and stability properties of Nash equilibria. In this paper, it is used to analyse the dynamics of vaccinating behaviour in a population consisting of distinct social groups, where each group has different perceptions of vaccine and disease risks. In particular, we study populations with two groups, where the size of one group is strictly larger than the size of the other group (a majority/minority population). We find that a population with a vaccine-inclined majority group and a vaccine-averse minority group exhibits higher average vaccine coverage than the corresponding homogeneous population, when the vaccine is perceived as being risky relative to the disease. Our model also reproduces a feature of real populations: In certain parameter regimes, it is possible to have a majority group adopting high vaccination rates and simultaneously a vaccine-averse minority group adopting low vaccination rates. Moreover, we find that minority groups will tend to exhibit more extreme changes in vaccinating behaviour for a given change in risk perception, in comparison to majority groups. These results emphasise the important role played by social heterogeneity in vaccination behaviour, while also highlighting the valuable role that can be played by PDS and VI in mathematical epidemiology.

Transients and attractors in epidemics.

Historical records of childhood disease incidence reveal complex dynamics. For measles, a simple model has indicated that epidemic patterns represent attractors of a nonlinear dynamic system and that transitions between different attractors are driven by slow changes in birth rates and vaccination levels. The same analysis can explain the main features of chickenpox dynamics, but fails for rubella and whooping cough. We show that an additional (perturbative) analysis of the model, together with knowledge of the population size in question, can account for all the observed incidence patterns by predicting how stochastically sustained transient dynamics should be manifested in these systems.