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The Mechanism and Phenomena of Adaptive Human Behavior During an Epidemic and the Role of Information

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Modeling the Interplay Between Human Behavior and the Spread of Infectious Diseases

Abstract

Disease transmission can be described phenomenologically at the population level or mechanistically as the aggregate result of individual behaviors. To explain why epidemics evolve as they do in response to information, a mechanistic approach is required. However, taking a mechanistic approach reveals that information can be parsed in terms of forecasting models or the approach to forming expectations, timeliness or quality of information, and information processing and how the information is used to make trade-offs. We develop a mechanistic model that uses microeconomic theory to describe adaptive or strategic human behavior. We show that phenomenological forecasting models and forecasting models based on classical epidemiological theory guide human behavior towards similar biological results, but different social well-being results. Moreover, we find that assumptions about information processing method, i.e., the utility function of individuals, may have a substantial influence on an epidemic.

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Notes

  1. 1.

    Agent-based [36], network [45], and distributed parameters [49] can be seen as generalization to the compartmental modeling framework.

  2. 2.

    Our notation and model development follows [20]. The framework is easily adapted to handle disease-induced mortality, but this requires tracking changes in the total population thereby adding an additional state variable. It is also possible to include population turnover (see supplemental material in [20]). However, our primary goal is to consider adaptive behavior during an epidemic such as flu and such epidemics are often managed as if they will eventually die out (see [21] for a similar treatment).

  3. 3.

    Conversely, the specification has the disadvantage of not modeling time allocation directly since contacts are not actual goods that are consumed. Bridging this divide is an active area of research.

  4. 4.

    The partial derivative in Eq. (6) is only taken with respect to C s. However, C s is substituted for \(\bar{{C}}^{s}\) after the derivative is taken prior to solving Eq. (6) for the optimal C s. This ensures that all individuals in the same class behave the same but do not consider the homogeneity of behavior when making behavioral choices.

  5. 5.

    Such an approach has a history in economics, and is not always differentiated from classical adaptive expectations [13, 37].

  6. 6.

    We do not consider rational expectations because the necessary market assumptions to impose as if rational behavior do not exist.

  7. 7.

    Minimum contacts are also greater than unit elastic in absolute value to the transmission parameter β for two of the forecasting models.

  8. 8.

    These authors point out that consumption smoothing associated with borrowing and savings over an epidemic shock implies that in the long run epidemics may have little effect on the level of economic activity in developed countries. The results of an epidemic in developing countries may be more sever [10], and there may long-run lasting effects on human capital from infection [1, 2].

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Acknowledgment

This research was made possible by grant 1R01GM100471-01 from the National Institute of General Medical Sciences (NIGMS) at the National Institutes of Health, and by grants from the National Science Foundation (NSF - Grant DMPS-0838705), the National Security Agency (NSA - Grant H98230-09-1-0104), and support from the Office of the Provost of Arizona State University.

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Authors and Affiliations

  1. School of Forestry and Environmental Studies, Yale University, New Haven, CT, 06511, USA

    Eli P. Fenichel

  2. Mathematical, Computational and Modeling Sciences Center, Arizona State University, Tempe, AZ, 85287, USA

    Xiaoxia Wang

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  1. Eli P. Fenichel
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  2. Xiaoxia Wang
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Correspondence to Eli P. Fenichel .

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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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Fenichel, E.P., Wang, X. (2013). The Mechanism and Phenomena of Adaptive Human Behavior During an Epidemic and the Role of Information. 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_10

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