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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
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    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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).
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