IDEAS home Printed from https://ideas.repec.org/a/plo/ppat00/1004206.html
   My bibliography  Save this article

The Contribution of Social Behaviour to the Transmission of Influenza A in a Human Population

Author

Listed:
  • Adam J Kucharski
  • Kin O Kwok
  • Vivian W I Wei
  • Benjamin J Cowling
  • Jonathan M Read
  • Justin Lessler
  • Derek A Cummings
  • Steven Riley

Abstract

Variability in the risk of transmission for respiratory pathogens can result from several factors, including the intrinsic properties of the pathogen, the immune state of the host and the host's behaviour. It has been proposed that self-reported social mixing patterns can explain the behavioural component of this variability, with simulated intervention studies based on these data used routinely to inform public health policy. However, in the absence of robust studies with biological endpoints for individuals, it is unclear how age and social behaviour contribute to infection risk. To examine how the structure and nature of social contacts influenced infection risk over the course of a single epidemic, we designed a flexible disease modelling framework: the population was divided into a series of increasingly detailed age and social contact classes, with the transmissibility of each age-contact class determined by the average contacts of that class. Fitting the models to serologically confirmed infection data from the 2009 Hong Kong influenza A/H1N1p pandemic, we found that an individual's risk of infection was influenced strongly by the average reported social mixing behaviour of their age group, rather than by their personal reported contacts. We also identified the resolution of social mixing that shaped transmission: epidemic dynamics were driven by intense contacts between children, a post-childhood drop in risky contacts and a subsequent rise in contacts for individuals aged 35–50. Our results demonstrate that self-reported social contact surveys can account for age-associated heterogeneity in the transmission of a respiratory pathogen in humans, and show robustly how these individual-level behaviours manifest themselves through assortative age groups. Our results suggest it is possible to profile the social structure of different populations and to use these aggregated data to predict their inherent transmission potential.Author Summary: For infections such as influenza, there are several aspects to the transmission process, including the properties of the pathogen itself, the host immune system and host behaviour. Although it has been proposed that self-reported social mixing patterns can be used to explain the behavioural component of infection – and mathematical modelling studies based on reported social contacts are used routinely to inform health policy – it is not clear how these contacts contribute to individual- and group-level infection risk. By analysing the relationship between social contacts and infection patterns during the 2009 Hong Kong influenza pandemic, we show that infection risk was strongly influenced by the average reported social mixing behaviour of an individual's age group, rather than by their personal reported contacts. We also demonstrate how social contact surveys can be combined with mathematical models to create useful tools with which to study respiratory infections in humans. This should make it possible to predict how the impact of interventions will vary from one population to the next based on their contacts and, potentially, to explain differences in infection attack rates between groups with different mixing behaviours.

Suggested Citation

  • Adam J Kucharski & Kin O Kwok & Vivian W I Wei & Benjamin J Cowling & Jonathan M Read & Justin Lessler & Derek A Cummings & Steven Riley, 2014. "The Contribution of Social Behaviour to the Transmission of Influenza A in a Human Population," PLOS Pathogens, Public Library of Science, vol. 10(6), pages 1-8, June.
  • Handle: RePEc:plo:ppat00:1004206
    DOI: 10.1371/journal.ppat.1004206
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plospathogens/article?id=10.1371/journal.ppat.1004206
    Download Restriction: no

    File URL: https://journals.plos.org/plospathogens/article/file?id=10.1371/journal.ppat.1004206&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.ppat.1004206?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. 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.
    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. Ichino, Andrea & Favero, Carlo A. & Rustichini, Aldo, 2020. "Restarting the economy while saving lives under Covid-19," CEPR Discussion Papers 14664, C.E.P.R. Discussion Papers.
    2. M. Hashem Pesaran & Cynthia Fan Yang, 2022. "Matching theory and evidence on Covid‐19 using a stochastic network SIR model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(6), pages 1204-1229, September.
    3. 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.
    4. S. M. Niaz Arifin & Christoph Zimmer & Caroline Trotter & Anaïs Colombini & Fati Sidikou & F. Marc LaForce & Ted Cohen & Reza Yaesoubi, 2019. "Cost-Effectiveness of Alternative Uses of Polyvalent Meningococcal Vaccines in Niger: An Agent-Based Transmission Modeling Study," Medical Decision Making, , vol. 39(5), pages 553-567, July.
    5. 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.
    6. Mirjam Kretzschmar & Rafael T Mikolajczyk, 2009. "Contact Profiles in Eight European Countries and Implications for Modelling the Spread of Airborne Infectious Diseases," PLOS ONE, Public Library of Science, vol. 4(6), pages 1-8, June.
    7. Andrei I. Vlad & Alexei A. Romanyukha & Tatiana E. Sannikova, 2024. "Parameter Tuning of Agent-Based Models: Metaheuristic Algorithms," Mathematics, MDPI, vol. 12(14), pages 1-21, July.
    8. Gillis, Melissa & Urban, Ryley & Saif, Ahmed & Kamal, Noreen & Murphy, Matthew, 2021. "A simulation–optimization framework for optimizing response strategies to epidemics," Operations Research Perspectives, Elsevier, vol. 8(C).
    9. Valentina Marziano & Giorgio Guzzetta & Alessia Mammone & Flavia Riccardo & Piero Poletti & Filippo Trentini & Mattia Manica & Andrea Siddu & Antonino Bella & Paola Stefanelli & Patrizio Pezzotti & Ma, 2021. "The effect of COVID-19 vaccination in Italy and perspectives for living with the virus," Nature Communications, Nature, vol. 12(1), pages 1-8, December.
    10. Nikolaos P. Rachaniotis & Thomas K. Dasaklis & Filippos Fotopoulos & Platon Tinios, 2021. "A Two-Phase Stochastic Dynamic Model for COVID-19 Mid-Term Policy Recommendations in Greece: A Pathway towards Mass Vaccination," IJERPH, MDPI, vol. 18(5), pages 1-21, March.
    11. Thomas Ash & Antonio M. Bento & Daniel Kaffine & Akhil Rao & Ana I. Bento, 2022. "Disease-economy trade-offs under alternative epidemic control strategies," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    12. Lewandowski, Piotr, 2020. "Occupational Exposure to Contagion and the Spread of COVID-19 in Europe," IZA Discussion Papers 13227, Institute of Labor Economics (IZA).
    13. Ruenzi, Stefan & Maeckle, Kai, 2023. "Friends with Drugs: The Role of Social Networks in the Opioid Epidemic," VfS Annual Conference 2023 (Regensburg): Growth and the "sociale Frage" 277574, Verein für Socialpolitik / German Economic Association.
    14. Laura Ozella & Francesco Gesualdo & Michele Tizzoni & Caterina Rizzo & Elisabetta Pandolfi & Ilaria Campagna & Alberto Eugenio Tozzi & Ciro Cattuto, 2018. "Close encounters between infants and household members measured through wearable proximity sensors," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-16, June.
    15. Mohamed Ismail, 2023. "The Effect of Social Contacts on the Uptake of Health Innovations among Older Ethnic Minorities in the UK: A Mixed Methods Study," Sustainability, MDPI, vol. 15(14), pages 1-19, July.
    16. 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.
    17. Charles Stoecker & Nicholas J. Sanders & Alan Barreca, 2016. "Success Is Something to Sneeze At: Influenza Mortality in Cities that Participate in the Super Bowl," American Journal of Health Economics, MIT Press, vol. 2(1), pages 125-143, January.
    18. Étienne Dagorn & Martina Dattilo & Matthieu Pourieux, 2022. "Preferences matter! Political Responses to the COVID-19 and Population’s Preferences," Economics Working Paper Archive (University of Rennes & University of Caen) 2022-01, Center for Research in Economics and Management (CREM), University of Rennes, University of Caen and CNRS.
    19. Batabyal, Saikat, 2021. "COVID-19: Perturbation dynamics resulting chaos to stable with seasonality transmission," Chaos, Solitons & Fractals, Elsevier, vol. 145(C).
    20. Anna Houstecka & Dongya Koh & Raül Santaeulà lia-Llopis, 2020. "Contagion at Work," Working Papers 1225, Barcelona School of Economics.

    More about this item

    Statistics

    Access and download statistics

    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:plo:ppat00:1004206. 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: plospathogens (email available below). General contact details of provider: https://journals.plos.org/plospathogens .

    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.