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Modeling the effects of contact-tracing apps on the spread of the coronavirus disease: mechanisms, conditions, and efficiency

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  • Chiba, Asako

Abstract

This study simulates the spread of the coronavirus disease (COVID-19) using a detailed agent-based model and the census data of Japan to provide a comprehensive analysis of the effects of contact-tracing apps. The results reveal some crucial characteristics of these apps. First, with regard to contacts of those diagnosed with COVID-19, the apps that require them to be quarantined upon receiving an alert are successful in achieving containment; however, the apps that require them to get tested have a limited curve-flattening effect. Second, the former category of apps perform better than the latter because they quarantine those who are infected but have not become infectious yet; these are individuals who cannot be detected by the current testing technology. Third, if the download rate of the apps is extremely high, the apps that require quarantine achieve containment with a small number of quarantined people, thereby indicating high efficiency. Finally, given a fixed download rate, increasing the number of tests per day enhances the effectiveness of the apps, although the degree of improved effectiveness is not proportional to the change in the number of tests.

Suggested Citation

  • Chiba, Asako, 2020. "Modeling the effects of contact-tracing apps on the spread of the coronavirus disease: mechanisms, conditions, and efficiency," MPRA Paper 103299, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:103299
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    References listed on IDEAS

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    1. Judd, Kenneth L., 2006. "Computationally Intensive Analyses in Economics," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 17, pages 881-893, Elsevier.
    2. Silva, Petrônio C.L. & Batista, Paulo V.C. & Lima, Hélder S. & Alves, Marcos A. & Guimarães, Frederico G. & Silva, Rodrigo C.P., 2020. "COVID-ABS: An agent-based model of COVID-19 epidemic to simulate health and economic effects of social distancing interventions," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    3. Leigh Tesfatsion & Kenneth L. Judd (ed.), 2006. "Handbook of Computational Economics," Handbook of Computational Economics, Elsevier, edition 1, volume 2, number 2.
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    RePEc Biblio mentions

    As found on the RePEc Biblio, the curated bibliography for Economics:
    1. > Economics of Welfare > Health Economics > Economics of Pandemics > Specific pandemics > Covid-19 > Health > Tracing

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    Cited by:

    1. So Kubota, 2021. "The macroeconomics of COVID-19 exit strategy: the case of Japan," The Japanese Economic Review, Springer, vol. 72(4), pages 651-682, October.

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    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • I00 - Health, Education, and Welfare - - General - - - General
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health

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