IDEAS home Printed from https://ideas.repec.org/a/spr/eurphb/v94y2021i10d10.1140_epjb_s10051-021-00222-8.html
   My bibliography  Save this article

Differences in social activity increase efficiency of contact tracing

Author

Listed:
  • Bjarke Frost Nielsen

    (University of Copenhagen)

  • Kim Sneppen

    (University of Copenhagen)

  • Lone Simonsen

    (Roskilde University)

  • Joachim Mathiesen

    (University of Copenhagen)

Abstract

Digital contact tracing has been suggested as an effective strategy for controlling an epidemic without severely limiting personal mobility. Here, we use smartphone proximity data to explore how social structure affects contact tracing of COVID-19. We model the spread of COVID-19 and find that the effectiveness of contact tracing depends strongly on social network structure and heterogeneous social activity. Contact tracing is shown to be remarkably effective in a workplace environment and the effectiveness depends strongly on the minimum duration of contact required to initiate quarantine. In a realistic social network, we find that forward contact tracing with immediate isolation can reduce an epidemic by more than 70%. In perspective, our findings highlight the necessity of incorporating social heterogeneity into models of mitigation strategies. Graphic abstract

Suggested Citation

  • Bjarke Frost Nielsen & Kim Sneppen & Lone Simonsen & Joachim Mathiesen, 2021. "Differences in social activity increase efficiency of contact tracing," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 94(10), pages 1-11, October.
  • Handle: RePEc:spr:eurphb:v:94:y:2021:i:10:d:10.1140_epjb_s10051-021-00222-8
    DOI: 10.1140/epjb/s10051-021-00222-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1140/epjb/s10051-021-00222-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1140/epjb/s10051-021-00222-8?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Vedran Sekara & Sune Lehmann, 2014. "The Strength of Friendship Ties in Proximity Sensor Data," PLOS ONE, Public Library of Science, vol. 9(7), pages 1-8, July.
    2. Alberto Aleta & David Martín-Corral & Ana Pastore y Piontti & Marco Ajelli & Maria Litvinova & Matteo Chinazzi & Natalie E. Dean & M. Elizabeth Halloran & Ira M. Longini Jr & Stefano Merler & Alex Pen, 2020. "Modelling the impact of testing, contact tracing and household quarantine on second waves of COVID-19," Nature Human Behaviour, Nature, vol. 4(9), pages 964-971, September.
    3. Per Block & Marion Hoffman & Isabel J. Raabe & Jennifer Beam Dowd & Charles Rahal & Ridhi Kashyap & Melinda C. Mills, 2020. "Social network-based distancing strategies to flatten the COVID-19 curve in a post-lockdown world," Nature Human Behaviour, Nature, vol. 4(6), pages 588-596, June.
    4. Md Arif Billah & Md Mamun Miah & Md Nuruzzaman Khan, 2020. "Reproductive number of coronavirus: A systematic review and meta-analysis based on global level evidence," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-17, November.
    5. Danielle Miller & Michael A. Martin & Noam Harel & Omer Tirosh & Talia Kustin & Moran Meir & Nadav Sorek & Shiraz Gefen-Halevi & Sharon Amit & Olesya Vorontsov & Avraham Shaag & Dana Wolf & Avi Peretz, 2020. "Full genome viral sequences inform patterns of SARS-CoV-2 spread into and within Israel," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
    6. Andre, M. & Ijaz, K. & Tillinghast, J.D. & Krebs, V.E. & Diem, L.A. & Metchock, B. & Crisp, T. & McElroy, P.D., 2007. "Transmission network analysis to complement routine tuberculosis contact investigations," American Journal of Public Health, American Public Health Association, vol. 97(3), pages 470-477.
    7. Don Klinkenberg & Christophe Fraser & Hans Heesterbeek, 2006. "The Effectiveness of Contact Tracing in Emerging Epidemics," PLOS ONE, Public Library of Science, vol. 1(1), pages 1-7, December.
    8. 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.
    9. Lorenzo Pellis & Simon Cauchemez & Neil M. Ferguson & Christophe Fraser, 2020. "Systematic selection between age and household structure for models aimed at emerging epidemic predictions," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
    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. 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.
    2. P. Battiston & M. Menegatti, 2022. "Interaction in Prevention: A General Theory and an Application to COVID-19 Pandemic," Economics Department Working Papers 2022-EP02, Department of Economics, Parma University (Italy).
    3. Andrew Perrault & Marie Charpignon & Jonathan Gruber & Milind Tambe & Maimuna Majumder, 2020. "Designing Efficient Contact Tracing Through Risk-Based Quarantining," NBER Working Papers 28135, National Bureau of Economic Research, Inc.
    4. Shakhany, Mohammad Qaleh & Salimifard, Khodakaram, 2021. "Predicting the dynamical behavior of COVID-19 epidemic and the effect of control strategies," Chaos, Solitons & Fractals, Elsevier, vol. 146(C).
    5. Choi, K. & Choi, Hoyun & Kahng, B., 2022. "COVID-19 epidemic under the K-quarantine model: Network approach," Chaos, Solitons & Fractals, Elsevier, vol. 157(C).
    6. Yun Jo & Andy Hong & Hyungun Sung, 2021. "Density or Connectivity: What Are the Main Causes of the Spatial Proliferation of COVID-19 in Korea?," IJERPH, MDPI, vol. 18(10), pages 1-16, May.
    7. 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.
    8. 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.
    9. 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.
    10. Houštecká, Anna & Koh, Dongya & Santaeulàlia-Llopis, Raül, 2021. "Contagion at work: Occupations, industries and human contact," Journal of Public Economics, Elsevier, vol. 200(C).
    11. Kuchler, Theresa & Russel, Dominic & Stroebel, Johannes, 2022. "JUE Insight: The geographic spread of COVID-19 correlates with the structure of social networks as measured by Facebook," Journal of Urban Economics, Elsevier, vol. 127(C).
    12. Ian E. Fellows & Mark S. Handcock, 2023. "Modeling of networked populations when data is sampled or missing," METRON, Springer;Sapienza Università di Roma, vol. 81(1), pages 21-35, April.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. Wang, Richard & Ye, Zhongnan & Lu, Miaojia & Hsu, Shu-Chien, 2022. "Understanding post-pandemic work-from-home behaviours and community level energy reduction via agent-based modelling," Applied Energy, Elsevier, vol. 322(C).
    18. Shahadat Uddin & Arif Khan & Haohui Lu & Fangyu Zhou & Shakir Karim, 2022. "Suburban Road Networks to Explore COVID-19 Vulnerability and Severity," IJERPH, MDPI, vol. 19(4), pages 1-9, February.
    19. 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.
    20. Elisabetta De Cao & Alessia Melegaro & Rogier Klok & Maarten Postma, 2014. "Optimising Assessments of the Epidemiological Impact in the Netherlands of Paediatric Immunisation with 13-Valent Pneumococcal Conjugate Vaccine Using Dynamic Transmission Modelling," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-9, April.

    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:spr:eurphb:v:94:y:2021:i:10:d:10.1140_epjb_s10051-021-00222-8. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.