IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v17y2020i7p2237-d337411.html
   My bibliography  Save this article

Contact Mixing Patterns and Population Movement among Migrant Workers in an Urban Setting in Thailand

Author

Listed:
  • Wiriya Mahikul

    (Department of Fundamentals of Public Health, Faculty of Public Health, Burapha University, Chon Buri 20131, Thailand)

  • Somkid Kripattanapong

    (Bureau of Epidemiology Department of Disease Control, Bangkok 11000, Thailand)

  • Piya Hanvoravongchai

    (Department of Preventive and Social Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok 10330, Thailand)

  • Aronrag Meeyai

    (Department of Epidemiology, Faculty of Public Health, Mahidol University, Bangkok 10400, Thailand
    Department of Global Health and Development, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK)

  • Sopon Iamsirithaworn

    (Department of Disease Control, Ministry of Public Health, Bangkok 11000, Thailand)

  • Prasert Auewarakul

    (Institute of Molecular Biosciences (MB), Mahidol University, Nakhon Pathom 73170, Thailand)

  • Wirichada Pan-ngum

    (Department of Tropical Hygiene, Faculty of Tropical Medicine, Mahidol University Bangkok, Bangkok 10400, Thailand
    Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok 10400, Thailand)

Abstract

Data relating to contact mixing patterns among humans are essential for the accurate modeling of infectious disease transmission dynamics. Here, we describe contact mixing patterns among migrant workers in urban settings in Thailand, based on a survey of 369 migrant workers of three nationalities. Respondents recorded their demographic data, including age, sex, nationality, workplace, income, and education. Each respondent chose a single day to record their contacts; this resulted in a total of more than 8300 contacts. The characteristics of contacts were recorded, including their age, sex, nationality, location of contact, and occurrence of physical contact. More than 75% of all contacts occurred among migrants aged 15 to 39 years. The contacts were highly clustered in this age group among migrant workers of all three nationalities. There were far fewer contacts between migrant workers with younger and older age groups. The pattern varied slightly among different nationalities, which was mostly dependent upon the types of jobs taken. Half of migrant workers always returned to their home country at most once a year and on a seasonal basis. The present study has helped us gain a better understanding of contact mixing patterns among migrant workers in urban settings. This information is useful both when simulating disease epidemics and for guiding optimal disease control strategies among this vulnerable section of the population.

Suggested Citation

  • Wiriya Mahikul & Somkid Kripattanapong & Piya Hanvoravongchai & Aronrag Meeyai & Sopon Iamsirithaworn & Prasert Auewarakul & Wirichada Pan-ngum, 2020. "Contact Mixing Patterns and Population Movement among Migrant Workers in an Urban Setting in Thailand," IJERPH, MDPI, vol. 17(7), pages 1-11, March.
  • Handle: RePEc:gam:jijerp:v:17:y:2020:i:7:p:2237-:d:337411
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/17/7/2237/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/17/7/2237/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Erik M Volz & Joel C Miller & Alison Galvani & Lauren Ancel Meyers, 2011. "Effects of Heterogeneous and Clustered Contact Patterns on Infectious Disease Dynamics," PLOS Computational Biology, Public Library of Science, vol. 7(6), pages 1-13, June.
    2. Neil M. Ferguson & Derek A. T. Cummings & Christophe Fraser & James C. Cajka & Philip C. Cooley & Donald S. Burke, 2006. "Strategies for mitigating an influenza pandemic," Nature, Nature, vol. 442(7101), pages 448-452, July.
    3. Aronrag Meeyai & Naiyana Praditsitthikorn & Surachai Kotirum & Wantanee Kulpeng & Weerasak Putthasri & Ben S Cooper & Yot Teerawattananon, 2015. "Seasonal Influenza Vaccination for Children in Thailand: A Cost-Effectiveness Analysis," PLOS Medicine, Public Library of Science, vol. 12(5), pages 1-25, May.
    4. Carol Y. Lin, 2008. "Modeling Infectious Diseases in Humans and Animals by KEELING, M. J. and ROHANI, P," Biometrics, The International Biometric Society, vol. 64(3), pages 993-993, September.
    5. Joël Mossong & Niel Hens & Mark Jit & Philippe Beutels & Kari Auranen & Rafael Mikolajczyk & Marco Massari & Stefania Salmaso & Gianpaolo Scalia Tomba & Jacco Wallinga & Janneke Heijne & Malgorzata Sa, 2008. "Social Contacts and Mixing Patterns Relevant to the Spread of Infectious Diseases," PLOS Medicine, Public Library of Science, vol. 5(3), pages 1-1, March.
    6. Stephen Eubank & Hasan Guclu & V. S. Anil Kumar & Madhav V. Marathe & Aravind Srinivasan & Zoltán Toroczkai & Nan Wang, 2004. "Modelling disease outbreaks in realistic urban social networks," Nature, Nature, vol. 429(6988), pages 180-184, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Marcel Salathé & James H Jones, 2010. "Dynamics and Control of Diseases in Networks with Community Structure," PLOS Computational Biology, Public Library of Science, vol. 6(4), pages 1-11, April.
    2. Audrey McCombs & Claus Kadelka, 2020. "A model-based evaluation of the efficacy of COVID-19 social distancing, testing and hospital triage policies," PLOS Computational Biology, Public Library of Science, vol. 16(10), pages 1-18, October.
    3. Hend Alrasheed & Alhanoof Althnian & Heba Kurdi & Heila Al-Mgren & Sulaiman Alharbi, 2020. "COVID-19 Spread in Saudi Arabia: Modeling, Simulation and Analysis," IJERPH, MDPI, vol. 17(21), pages 1-24, October.
    4. Wei Zhong, 2017. "Simulating influenza pandemic dynamics with public risk communication and individual responsive behavior," Computational and Mathematical Organization Theory, Springer, vol. 23(4), pages 475-495, December.
    5. John M Drake & Tobias S Brett & Shiyang Chen & Bogdan I Epureanu & Matthew J Ferrari & Éric Marty & Paige B Miller & Eamon B O’Dea & Suzanne M O’Regan & Andrew W Park & Pejman Rohani, 2019. "The statistics of epidemic transitions," PLOS Computational Biology, Public Library of Science, vol. 15(5), pages 1-14, May.
    6. Bisin, Alberto & Moro, Andrea, 2022. "Spatial‐SIR with network structure and behavior: Lockdown rules and the Lucas critique," Journal of Economic Behavior & Organization, Elsevier, vol. 198(C), pages 370-388.
    7. Stefano Guarino & Enrico Mastrostefano & Massimo Bernaschi & Alessandro Celestini & Marco Cianfriglia & Davide Torre & Lena Rebecca Zastrow, 2021. "Inferring Urban Social Networks from Publicly Available Data," Future Internet, MDPI, vol. 13(5), pages 1-45, April.
    8. Alberto Bisin & Andrea Moro, 2020. "Learning Epidemiology by Doing: The Empirical Implications of a Spatial-SIR Model with Behavioral Responses," NBER Working Papers 27590, National Bureau of Economic Research, Inc.
    9. Xiao, Yao & Yang, Mofeng & Zhu, Zheng & Yang, Hai & Zhang, Lei & Ghader, Sepehr, 2021. "Modeling indoor-level non-pharmaceutical interventions during the COVID-19 pandemic: A pedestrian dynamics-based microscopic simulation approach," Transport Policy, Elsevier, vol. 109(C), pages 12-23.
    10. Christopher Bronk Ramsey, 2020. "Human agency and infection rates: Implications for social distancing during epidemics," PLOS ONE, Public Library of Science, vol. 15(12), pages 1-17, December.
    11. Brotherhood, Luiz & Kircher, Philipp & Santos, Cezar & Tertilt, Michèle, 2023. "Optimal Age-based Policies for Pandemics: An Economic Analysis of Covid-19 and Beyond," IDB Publications (Working Papers) 13295, Inter-American Development Bank.
    12. Rakowski, Franciszek & Gruziel, Magdalena & Bieniasz-Krzywiec, Łukasz & Radomski, Jan P., 2010. "Influenza epidemic spread simulation for Poland — a large scale, individual based model study," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(16), pages 3149-3165.
    13. Bisin, Alberto & Moro, Andrea, 2022. "JUE insight: Learning epidemiology by doing: The empirical implications of a Spatial-SIR model with behavioral responses," Journal of Urban Economics, Elsevier, vol. 127(C).
    14. Janiak, Alexandre & Machado, Caio & Turén, Javier, 2021. "Covid-19 contagion, economic activity and business reopening protocols," Journal of Economic Behavior & Organization, Elsevier, vol. 182(C), pages 264-284.
    15. Wenting Yang & Jiantong Zhang & Ruolin Ma, 2020. "The Prediction of Infectious Diseases: A Bibliometric Analysis," IJERPH, MDPI, vol. 17(17), pages 1-19, August.
    16. Rocha Filho, T.M. & Moret, M.A. & Chow, C.C. & Phillips, J.C. & Cordeiro, A.J.A. & Scorza, F.A. & Almeida, A.-C.G. & Mendes, J.F.F., 2021. "A data-driven model for COVID-19 pandemic – Evolution of the attack rate and prognosis for Brazil," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    17. Sangwon Chae & Sungjun Kwon & Donghyun Lee, 2018. "Predicting Infectious Disease Using Deep Learning and Big Data," IJERPH, MDPI, vol. 15(8), pages 1-20, July.
    18. Toxvaerd, Flavio & Rowthorn, Robert, 2022. "On the management of population immunity," Journal of Economic Theory, Elsevier, vol. 204(C).
    19. Rhodes, Christopher A. & House, Thomas, 2013. "The rate of convergence to early asymptotic behaviour in age-structured epidemic models," Theoretical Population Biology, Elsevier, vol. 85(C), pages 58-62.
    20. Tobias Brett & Marco Ajelli & Quan-Hui Liu & Mary G Krauland & John J Grefenstette & Willem G van Panhuis & Alessandro Vespignani & John M Drake & Pejman Rohani, 2020. "Detecting critical slowing down in high-dimensional epidemiological systems," PLOS Computational Biology, Public Library of Science, vol. 16(3), pages 1-19, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:17:y:2020:i:7:p:2237-:d:337411. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.