Evaluating the social influences that impact vaccination decisions

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DESCRIPTION (provided by applicant): A key to predicting medical decisions, such as vaccinations, is to understand individual behavior and its determinants, including self-interest and self-determined goals (desire to benefit oneself or enhance one's utility), prosociality (preferences for the well-being of others), imitation (imitating decisions of others), habitual behavior (tendency to repeat past actions), as well as time and budget constraints. As these behavioral factors influence vaccination decisions, they need to be incorporated into models of alternative vaccination strategies in order to provide policy decision-making guidance. Here, we propose to evaluate how behavioral factors shape influenza vaccination decisions, vary across nations, and can be used to inform vaccination strategies. To do so, we will use epidemiological-economic models including game theory and network models of influenza transmission, parameterized by psychological, economic and sociological data. Specifically, our transdisciplinary team will employ surveys, statistical analysis, and mathematical modeling to assess the interplay among decision-making processes of individuals, infectious disease transmission, and social influences in different countries. We will evaluate behavior, decision-making and epidemiological outcomes at multiple interrelated scales, including at the individual, household, age class, national, and population levels. Individual-level vaccination decisions can profoundly influence the fate of outbreaks, and thus have far reaching public health consequences for regional, national, and international populations. Understanding the factors underlying vaccination decisions and adherence to public health recommendations will enable the design of more effective vaccination programs. Therefore, we will also evaluate the capacity to influence behavioral factors, including prosociality, imitation, and habitual behavior, to promote vaccination policies that are optimal at both the individual and public health scales. Our results will increase our understanding of health decision-making and inform intervention strategies to improve vaccination rates.