IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2021i17p9166-d625998.html
   My bibliography  Save this article

A Statistical Model to Assess Risk for Supporting COVID-19 Quarantine Decisions

Author

Listed:
  • Sonja Jäckle

    (Fraunhofer Institute for Digital Medicine MEVIS, 23562 Lübeck, Germany)

  • Elias Röger

    (Fraunhofer Institute for Industrial Mathematics ITWM, 67663 Kaiserslautern, Germany)

  • Volker Dicken

    (Fraunhofer Institute for Digital Medicine MEVIS, 28359 Bremen, Germany)

  • Benjamin Geisler

    (Fraunhofer Institute for Digital Medicine MEVIS, 28359 Bremen, Germany)

  • Jakob Schumacher

    (Health Department Berlin-Reinickendorf, 13407 Berlin, Germany)

  • Max Westphal

    (Fraunhofer Institute for Digital Medicine MEVIS, 28359 Bremen, Germany)

Abstract

In Germany, local health departments are responsible for surveillance of the current pandemic situation. One of their major tasks is to monitor infected persons. For instance, the direct contacts of infectious persons at group meetings have to be traced and potentially quarantined. Such quarantine requirements may be revoked, when all contact persons obtain a negative polymerase chain reaction (PCR) test result. However, contact tracing and testing is time-consuming, costly and not always feasible. In this work, we present a statistical model for the probability that no transmission of COVID-19 occurred given an arbitrary number of negative test results among contact persons. Hereby, the time-dependent sensitivity and specificity of the PCR test are taken into account. We employ a parametric Bayesian model which combines an adaptable Beta-Binomial prior and two likelihood components in a novel fashion. This is illustrated for group events in German school classes. The first evaluation on a real-world dataset showed that our approach can support important quarantine decisions with the goal to achieve a better balance between necessary containment of the pandemic and preservation of social and economic life. Future work will focus on further refinement and evaluation of quarantine decisions based on our statistical model.

Suggested Citation

  • Sonja Jäckle & Elias Röger & Volker Dicken & Benjamin Geisler & Jakob Schumacher & Max Westphal, 2021. "A Statistical Model to Assess Risk for Supporting COVID-19 Quarantine Decisions," IJERPH, MDPI, vol. 18(17), pages 1-13, August.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:17:p:9166-:d:625998
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/17/9166/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/18/17/9166/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jos Lelieveld & Frank Helleis & Stephan Borrmann & Yafang Cheng & Frank Drewnick & Gerald Haug & Thomas Klimach & Jean Sciare & Hang Su & Ulrich Pöschl, 2020. "Model Calculations of Aerosol Transmission and Infection Risk of COVID-19 in Indoor Environments," IJERPH, MDPI, vol. 17(21), pages 1-18, November.
    2. Samui, Piu & Mondal, Jayanta & Khajanchi, Subhas, 2020. "A mathematical model for COVID-19 transmission dynamics with a case study of India," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    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. Sharma, Natasha & Verma, Atul Kumar & Gupta, Arvind Kumar, 2021. "Spatial network based model forecasting transmission and control of COVID-19," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 581(C).
    2. Talal Daghriri & Michael Proctor & Sarah Matthews, 2022. "Evolution of Select Epidemiological Modeling and the Rise of Population Sentiment Analysis: A Literature Review and COVID-19 Sentiment Illustration," IJERPH, MDPI, vol. 19(6), pages 1-20, March.
    3. Cunwei Yang & Weiqing Wang & Fengying Li & Degang Yang, 2022. "One-Size-Fits-All Policies Are Unacceptable: A Sustainable Management and Decision-Making Model for Schools in the Post-COVID-19 Era," IJERPH, MDPI, vol. 19(10), pages 1-21, May.
    4. Lina Sonne Vyas & Maren Duvendack, 2024. "The resilience of systems‐thinking approaches when faced with an evolving crisis: The case of Mumbai," Development Policy Review, Overseas Development Institute, vol. 42(3), May.
    5. Zander S. Venter & Adam Sadilek & Charlotte Stanton & David N. Barton & Kristin Aunan & Sourangsu Chowdhury & Aaron Schneider & Stefano Maria Iacus, 2021. "Mobility in Blue-Green Spaces Does Not Predict COVID-19 Transmission: A Global Analysis," IJERPH, MDPI, vol. 18(23), pages 1-12, November.
    6. Marta Baselga & Juan J. Alba & Alberto J. Schuhmacher, 2022. "The Control of Metabolic CO 2 in Public Transport as a Strategy to Reduce the Transmission of Respiratory Infectious Diseases," IJERPH, MDPI, vol. 19(11), pages 1-19, May.
    7. Hongfei Xiao & Deqin Lin & Shiyu Li, 2023. "Novel Method for Estimating Time-Varying COVID-19 Transmission Rate," Mathematics, MDPI, vol. 11(10), pages 1-18, May.
    8. Junsik Park & Gurjoong Kim, 2022. "Social Efficiency of Public Transportation Policy in Response to COVID-19: Model Development and Application to Intercity Buses in Seoul Metropolitan Area," IJERPH, MDPI, vol. 19(19), pages 1-14, September.
    9. Birte Knobling & Gefion Franke & Lisa Beike & Timo Dickhuth & Johannes K. Knobloch, 2022. "Reading the Score of the Air—Change in Airborne Microbial Load in Contrast to Particulate Matter during Music Making," IJERPH, MDPI, vol. 19(16), pages 1-13, August.
    10. Pollozhani, Fatos & McLeod, Robert S. & Schwarzbauer, Christian & Hopfe, Christina J., 2024. "Assessing school ventilation strategies from the perspective of health, environment, and energy," Applied Energy, Elsevier, vol. 353(PA).
    11. Han, Lili & Song, Sha & Pan, Qiuhui & He, Mingfeng, 2023. "The impact of multiple population-wide testing and social distancing on the transmission of an infectious disease," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    12. Thomas Harweg & Mathias Wagner & Frank Weichert, 2022. "Agent-Based Simulation for Infectious Disease Modelling over a Period of Multiple Days, with Application to an Airport Scenario," IJERPH, MDPI, vol. 20(1), pages 1-20, December.
    13. Henri Salmenjoki & Marko Korhonen & Antti Puisto & Ville Vuorinen & Mikko J Alava, 2021. "Modelling aerosol-based exposure to SARS-CoV-2 by an agent based Monte Carlo method: Risk estimates in a shop and bar," PLOS ONE, Public Library of Science, vol. 16(11), pages 1-12, November.
    14. Prem Kumar, R. & Santra, P.K. & Mahapatra, G.S., 2023. "Global stability and analysing the sensitivity of parameters of a multiple-susceptible population model of SARS-CoV-2 emphasising vaccination drive," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 203(C), pages 741-766.
    15. Chatterjee, Amar Nath & Ahmad, Bashir, 2021. "A fractional-order differential equation model of COVID-19 infection of epithelial cells," Chaos, Solitons & Fractals, Elsevier, vol. 147(C).
    16. Bera, Sovan & Khajanchi, Subhas & Roy, Tapan Kumar, 2022. "Dynamics of an HTLV-I infection model with delayed CTLs immune response," Applied Mathematics and Computation, Elsevier, vol. 430(C).
    17. Sheikh Safiullah & Asadur Rahman & Shameem Ahmad Lone & S. M. Suhail Hussain & Taha Selim Ustun, 2022. "Novel COVID-19 Based Optimization Algorithm (C-19BOA) for Performance Improvement of Power Systems," Sustainability, MDPI, vol. 14(21), pages 1-27, November.
    18. Ghazal, Ikram & Rachadi, Abdeljalil & Ez-Zahraouy, Hamid, 2022. "Optimal allocation strategies for prioritized geographical vaccination for Covid-19," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    19. Wolfgang Schade & Vladislav Reimer & Martin Seipenbusch & Ulrike Willer, 2021. "Experimental Investigation of Aerosol and CO 2 Dispersion for Evaluation of COVID-19 Infection Risk in a Concert Hall," IJERPH, MDPI, vol. 18(6), pages 1-11, March.
    20. Zhang, Zhenzhen & Ma, Xia & Zhang, Yongxin & Sun, Guiquan & Zhang, Zi-Ke, 2023. "Identifying critical driving factors for human brucellosis in Inner Mongolia, China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 626(C).

    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:17:p:9166-:d:625998. 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.