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Modeling the Interplay Between Human Behavior and the Spread of Infectious Diseases pp 203–227Cite as

Modeling Influenza Vaccination Behavior via Inductive Reasoning Games

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Abstract

Past experiences with seasonal influenza and immunization may affect individual decisions about whether to obtain vaccinations. Individuals continually adapt to recent influenza-related experiences, using inductive thought to reevaluate their options to obtain vaccinations. We explore this concept by constructing an individual-level model of adaptive decision-making. We couple this model with a population-level model of influenza that includes vaccination dynamics. The coupled models allow us to explore how individual-level decisions may change influenza epidemiology and, conversely, how influenza epidemiology might change individual-level decisions. By including the effects of adaptive decision-making within an epidemic model, we show that the behavioral dynamics of vaccination uptake could lead to severe influenza epidemics even without the presence of a pandemic strain. We further show that these severe epidemics might be prevented if vaccination programs provided commitment-based incentives or if mass media released epidemiological information that individuals can use to evaluate the prudence of vaccination. Finally we discuss and present some preliminary results of the model when social networks offer preferential paths for transmission.

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Notes

  1. 1.

    For s = 1, the pro-vaccination experience of an individual simply represents the total number of the years that he/she would have benefited from vaccinating.

  2. 2.

    If no epidemic occurred, we assume that an individual who chose to get vaccinated is not attending to the fact that mass vaccination could have prevented the epidemic. He/she is purely self-interested and believes that in the next flu season he/she can choose not to vaccinate and free-ride on the protection provided by those that do get vaccinated. This assumption is relaxed in Sect. 5.

  3. 3.

    We used an R 0 value of 2. 5 and obtained q(p) by integrating a deterministic SIR model with daily transmissibility rate of 5 ∕ 6 and an average infectious duration with flu of 3 days. Similar results can be obtained for the case where q(p) is a linear function with q(0) = 0. 8 that monotonically decreases to q(p) = 0 for p ≥ π c .

  4. 4.

    When simulating the basic model, the achieved vaccination coverage p n in year n represents only one realization as given by Eq. (3). However, using the same set of vaccination probabilities w n (i), the Bernoulli process describing the vaccination decisions could have resulted in a different set of vaccination decisions and thus in a different vaccination coverage realization.

  5. 5.

    Here, \(\Pi _{0} =\{ s + 1/{[(1 - s)q(0)]\}}^{-1}\) and is found by setting \(\pi _{n} = s\pi _{c}\) and \(\pi _{n+1} = \pi _{c}\) in the iterative map Eq. (14).

  6. 6.

    Here, incidence is defined as the number of new cases per susceptible (i.e., non-vaccinated) individual. Since we assume a perfect vaccine, the incidence is equivalent to the risk of infection q(π n ) that a vaccinated individual would have had if he/she had not been vaccinated at the beginning of the season.

  7. 7.

    Here, R 0 is given by \(\langle k\rangle T\). For the Erdös–Rènyi graph, the epidemic threshold condition is still expressed in terms of R 0, although this is not the case for a general network [19]. Therefore, Eq. (1) relating π c to R 0 is still valid. It is however important to note that π c gives the critical vaccination coverage needed if individuals vaccinate irrespective of their location on the network.

  8. 8.

    This seemingly simple construct provides an example of what is known as a complex-adaptive system [30] since the emergence of the vaccination coverage (i.e., a macroscopic quantity) provides different types of feedback to the individuals (i.e., the microscopic agents) depending on both its achieved value and the actions taken by each individual.

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Acknowledgments

We thank Drs. Romulus Breban, Sarah Nowak, Kayla de la Haye, Courtney Gidengil, Andrew Parker, and Sydne Jennifer Newberry for discussions in the preparation of this chapter. We also gratefully acknowledge the financial support from US National Institute of Health/NCI (5R21CA157571-02). Contents are solely the responsibility of the authors and do not necessarily represent the official views of the National Institute of Health/NCI.

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Authors and Affiliations

  1. RAND Corporation, 1776 Main Street, Santa Monica, CA, 90401-3208, USA

    Raffaele Vardavas

  2. RAND Corporation, 1776 Main Street, Santa Monica, CA, 90401-2138, USA

    Christopher Steven Marcum

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  1. Raffaele Vardavas
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Correspondence to Raffaele Vardavas .

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Editors and Affiliations

  1. , Department of Economics and Management, University of Pisa, Via Ridolfi 10, Pisa, 56124, Italy

    Piero Manfredi

  2. , Department of Experimental Oncology, European Institute of Oncology, Via Ripamonti 435, Milan, 20141, Italy

    Alberto D'Onofrio

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Vardavas, R., Marcum, C.S. (2013). Modeling Influenza Vaccination Behavior via Inductive Reasoning Games. In: Manfredi, P., D'Onofrio, A. (eds) Modeling the Interplay Between Human Behavior and the Spread of Infectious Diseases. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-5474-8_13

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