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Game Theory and the Success of Voluntary Vaccination Campaigns

This article discusses how game theory can be used to model human decision-making in voluntary vaccination programs. The article first asserts that human decision-making is critical to voluntary vaccination programs. Vaccines have led to the complete eradication of some diseases in the past, such as smallpox. Many more diseases, including measles, could have also been eradicated in a similar fashion if there had not been a recent increase in vaccine denialism, in which some people choose not to receive a vaccine that is readily available. This trend is partly due to the emergence of studies on the potential risks of getting vaccinated and the spread of vaccine myths. Researchers wanted to determine what exactly causes people to join or stay out of voluntary vaccination programs, and they used game theory to derive an explanation. 


In this particular model, there are many factors that can affect the decisions made by people, represented by agents. For example, some people may want to minimize the risk of getting vaccinated by free-riding on herd immunity, which is when a population has a high vaccination rate, so that without getting the vaccine themselves they still have a low risk of getting the disease. The agents were assumed to be rational and making choices based on increasing the benefits, or maximizing their payoffs in game theory terminology. Each agent in the model can be one of three states: infected by the disease, susceptible to the disease, or recovered from the disease. At each round, game theory was used to assess whether each agent would decide whether or not to get vaccinated based on available information. The benefits of getting vaccinated largely depend on how many people are currently infected, as if a lot of people are infected, the risk of the agent contracting the disease is high, and the best strategy would be to get vaccinated. The benefits of not getting vaccinated, however, depend on how many people have already recovered from the disease. If the number of people who have already recovered from the disease is high, then an agent is not likely to contract the disease, so the best strategy would be to not get vaccinated and avoid the costs of getting a vaccine. The payoffs of each agent, and consequently the number of agents who choose to get the vaccine, change as the numbers of people who are currently infected and who have already recovered change.


Using this model, researchers simulated the virtual spread of disease and vaccination campaigns in South Indian villages. The applied the model of decision-making above, and found that some factors influencing the choices people make on whether to get the vaccine or not include the infectiousness of the disease and whether the information people receive about the prevalence of the disease is global or local. 


I found this article interesting because it relates a topic discussed in lecture, game theory, to another topic that at first seemed completely unrelated, public health. Using game theory helped explain why certain people choose not to get vaccinated, and in general how the spread of disease not only depends on the nature of the disease itself but also the social network through which it spreads. The use of game theory also has future implications, as if the goal of a certain vaccination program is to increase vaccination rates, the model of decision-making can help elucidate factors that contribute to the choices that people make on whether to get vaccinated or not. For example, for the simulations of the spread of disease in South Indian villages, one of the factors that influenced people’s choices was whether the information people received about the prevalence of the disease is global or local, so in order to increase vaccination rates one thing that can be done is present information about the spread of the disease at a local level. 


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