IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2021i11p5713-d562662.html
   My bibliography  Save this article

Using Social Networks to Estimate the Number of COVID-19 Cases: The Incident (Hidden COVID-19 Cases Network Estimation) Study Protocol

Author

Listed:
  • Honoria Ocagli

    (Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences, and Public Health, University of Padova, Via Loredan, 18, 35121 Padova, Italy)

  • Danila Azzolina

    (Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences, and Public Health, University of Padova, Via Loredan, 18, 35121 Padova, Italy
    Research Support Unit, Department of Translational Medicine, University of Eastern Piedmont, Via Solaroli, 17, 28100 Novara, Italy)

  • Giulia Lorenzoni

    (Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences, and Public Health, University of Padova, Via Loredan, 18, 35121 Padova, Italy)

  • Silvia Gallipoli

    (Zeta Research, Via Antonio Caccia, 8, 34129 Trieste, Italy)

  • Matteo Martinato

    (Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences, and Public Health, University of Padova, Via Loredan, 18, 35121 Padova, Italy)

  • Aslihan S. Acar

    (Department of Actuarial Sciences, Hacettepe University, 06800 Ankara, Turkey)

  • Paola Berchialla

    (Department of Clinical and Biological Sciences, University of Torino, Regione Gonzole 10, 10043 Orbassano, Italy)

  • Dario Gregori

    (Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences, and Public Health, University of Padova, Via Loredan, 18, 35121 Padova, Italy)

  • on behalf of the INCIDENT Study Group

    (Membership of the INCIDENT Study Group is provided in the Acknowledgments.)

Abstract

Recent literature has reported a high percentage of asymptomatic or paucisymptomatic cases in subjects with COVID-19 infection. This proportion can be difficult to quantify; therefore, it constitutes a hidden population. This study aims to develop a proof-of-concept method for estimating the number of undocumented infections of COVID-19. This is the protocol for the INCIDENT (Hidden COVID-19 Cases Network Estimation) study, an online, cross-sectional survey with snowball sampling based on the network scale-up method (NSUM). The original personal network size estimation method was based on a fixed-effects maximum likelihood estimator. We propose an extension of previous Bayesian estimation methods to estimate the unknown network size using the Markov chain Monte Carlo algorithm. On 6 May 2020, 1963 questionnaires were collected, 1703 were completed except for the random questions, and 1652 were completed in all three sections. The algorithm was initialized at the first iteration and applied to the whole dataset. Knowing the number of asymptomatic COVID-19 cases is extremely important for reducing the spread of the virus. Our approach reduces the number of questions posed. This allows us to speed up the completion of the questionnaire with a subsequent reduction in the nonresponse rate.

Suggested Citation

  • Honoria Ocagli & Danila Azzolina & Giulia Lorenzoni & Silvia Gallipoli & Matteo Martinato & Aslihan S. Acar & Paola Berchialla & Dario Gregori & on behalf of the INCIDENT Study Group, 2021. "Using Social Networks to Estimate the Number of COVID-19 Cases: The Incident (Hidden COVID-19 Cases Network Estimation) Study Protocol," IJERPH, MDPI, vol. 18(11), pages 1-12, May.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:11:p:5713-:d:562662
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/11/5713/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/18/11/5713/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Silvia Snidero & Nicola Soriani & Ileana Baldi & Federica Zobec & Paola Berchialla & Dario Gregori, 2012. "Scale-Up Approach in CATI Surveys for Estimating the Number of Foreign Body Injuries in the Aero-Digestive Tract in Children," IJERPH, MDPI, vol. 9(11), pages 1-12, November.
    2. Palatella, Luigi & Vanni, Fabio & Lambert, David, 2021. "A phenomenological estimate of the true scale of CoViD-19 from primary data," Chaos, Solitons & Fractals, Elsevier, vol. 146(C).
    3. McCormick, Tyler H. & Salganik, Matthew J. & Zheng, Tian, 2010. "How Many People Do You Know?: Efficiently Estimating Personal Network Size," Journal of the American Statistical Association, American Statistical Association, vol. 105(489), pages 59-70.
    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. Fabio Vanni & David Lambert, 2021. "On the regularity of human mobility patterns at times of a pandemic," Papers 2104.08975, arXiv.org.
    2. Elizabeth Sully & Margaret Giorgio & Selena Anjur-Dietrich, 2020. "Estimating abortion incidence using the network scale-up method," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 43(56), pages 1651-1684.
    3. Alex Carballo-Diéguez & Ivan Balan & Rubén Marone & María A Pando & Curtis Dolezal & Victoria Barreda & Cheng-Shiun Leu & María Mercedes Ávila, 2011. "Use of Respondent Driven Sampling (RDS) Generates a Very Diverse Sample of Men Who Have Sex with Men (MSM) in Buenos Aires, Argentina," PLOS ONE, Public Library of Science, vol. 6(11), pages 1-8, November.
    4. Feehan, Dennis & Son, Vo Hai & Abdul-Quader, Abu, 2021. "Survey methods for estimating the size of weak-tie personal networks," SocArXiv z2t4p, Center for Open Science.
    5. Arun G. Chandrasekhar & Paul Goldsmith-Pinkham & Matthew O. Jackson & Samuel Thau, 2021. "Interacting regional policies in containing a disease," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 118(19), pages 2021520118-, May.
    6. Merli, M. Giovanna & Moody, James & Smith, Jeffrey & Li, Jing & Weir, Sharon & Chen, Xiangsheng, 2015. "Challenges to recruiting population representative samples of female sex workers in China using Respondent Driven Sampling," Social Science & Medicine, Elsevier, vol. 125(C), pages 79-93.
    7. Tyler H. McCormick & Tian Zheng, 2015. "Latent Surface Models for Networks Using Aggregated Relational Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1684-1695, December.
    8. Ids Baalbergen & Eva Jaspers, 2023. "Social Capital and Its Returns as an Explanation for Early Labor Market Success of Majority and Minority Members in the Netherlands," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 165(1), pages 53-76, January.
    9. Félix-Medina Martín Humberto, 2021. "Combining Cluster Sampling and Link-Tracing Sampling to Estimate Totals and Means of Hidden Populations in Presence of Heterogeneous Probabilities of Links," Journal of Official Statistics, Sciendo, vol. 37(4), pages 865-905, December.
    10. Fabio Vanni & David Lambert, 2021. "On the regularity of human mobility patterns at times of a pandemic," SciencePo Working papers Main hal-04103882, HAL.
    11. Naomi F. Sugie, 2018. "Utilizing Smartphones to Study Disadvantaged and Hard-to-Reach Groups," Sociological Methods & Research, , vol. 47(3), pages 458-491, August.
    12. Aparajithan Venkateswaran & Jishnu Das & Tyler H. McCormick, 2023. "Feasible contact tracing," Papers 2312.05718, arXiv.org.

    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:18:y:2021:i:11:p:5713-:d:562662. 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.