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Social Ecological Contexts and Alcohol Drinking Dynamics: An Application of the Survey Data-Driven Agent-Based Model for University Students

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Journal of the Indian Institute of Science Aims and scope

Abstract

College-aged students are one of the most vulnerable populations to high-risk alcohol drinking behaviors that could cause them consequences such as injury or sexual assault. An important factor that may influence college students’ decision on alcohol drinking behavior is socializing in certain contexts across the university environment. In the present study, we aim to identify and better understand the ecological conditions driving the dynamics of distribution of alcohol use among college-aged students. To this end, a pilot survey study is conducted to evaluate students’ movement patterns in different contexts across the Arizona State University (ASU) campus, and the results are used to develop an agent-based simulation model designed for examining the role of environmental factors on development and maintenance of alcohol drinking behavior by a representative sample of ASU students. The proposed model, which resembles an approximate reaction–diffusion model, accounts for movement of agents (i.e., diffusion) in various contexts and alcohol drinking influences within those contexts (i.e., reaction) via a Susceptible-Infected-Recovered type individual-based model. Of the four most visited contexts at ASU Tempe campus—Library, Memorial Union, Fitness Center, and Dorm—the context with the highest visiting probability, Memorial Union, is the most influential and most sensitive context (around sixteen times higher an impact of alcohol-related influences than the other contexts) in terms of spreading alcohol drinking behavior. Our findings highlight the crucial role of socialization in local environments on the dynamics of students’ alcohol use as well as on the long-term prediction of the college drinking prevalence.

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Notes

  1. The model ran for at least \(4\times (1000+2)=4008\) times to generate sufficient output to calculate variances for four parameters of transmission probabilities and 1000 samples for each of them59.

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Acknowledgements

The authors acknowledge students Mugdha Thakur, Meghan Werbick, Amber Andrande, Sparshee Naik, Annabel Judd, Tyler Ray and Rachel Nicely for helping with collecting of the data and review of the basic version of the model as well as Calvin Pritchard, Gil Speyer, Rebecca Belshe, Jason Yalim, and William Dizon for providing guidance with high performance computing and data storage. The authors also thank Jan Salecker (the author and maintainer of nlrx package) for his useful help and suggestions on using nlrx package.

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Authors and Affiliations

  1. Department of Mathematics, University of California Irvine, Irvine, CA, USA

    Asma Azizi

  2. Indian Institute of Technology Kanpur, Kanpur, 208016, India

    Anamika Mubayi

  3. PRECISIONheor, Los Angeles, CA, USA

    Anuj Mubayi

  4. School of Human Evolution and Social Change, Arizona State University, Tempe, AZ, USA

    Asma Azizi & Anuj Mubayi

  5. Center for Collaborative Studies in Mathematical Biology, Illinois State University, Normal, IL, USA

    Anuj Mubayi

  6. Math Department, Kennesaw State University, Kennesaw, GA, USA

    Asma Azizi

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  1. Asma Azizi
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  2. Anamika Mubayi
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Correspondence to Anuj Mubayi.

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Appendix

Appendix

Table 5: A summary of the results of CART analysis for various target variables.
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Table 6: Random variables used in ABM algorithm and their corresponding distributions.
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Table 7: The set of all parameters and attributes defined in our ABM.
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Table 8: Table of notation for a conventional model in algorithm 1.
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figure d

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Azizi, A., Mubayi, A. & Mubayi, A. Social Ecological Contexts and Alcohol Drinking Dynamics: An Application of the Survey Data-Driven Agent-Based Model for University Students. J Indian Inst Sci 101, 381–401 (2021). https://doi.org/10.1007/s41745-021-00252-2

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