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International Conference on Complex Networks and Their Applications

COMPLEX NETWORKS 2021: Complex Networks & Their Applications X pp 229–240Cite as

Story of Two Populations in Epidemics: Is Every Infection Counted?

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Part of the Studies in Computational Intelligence book series (SCI,volume 1016)

Abstract

Many previous studies on epidemic processes assume that most infected nodes that contribute to the spread of infection can be identified and accounted for. But, in some cases, this assumption may be invalid and many infection cases may go undetected. For example, the operators of subsystems in complex systems may be unaware that their systems are compromised by malware or a virus, which may go undetected for a long period. Similarly, an infectious disease can cause widely varying symptoms and some infected individuals may exhibit little to no symptoms. In these scenarios, it would be difficult to identify all infected systems or individuals, which can play a critical role in spreading the infection by malware, virus or disease. For this reason, it is of interest to devise a means of quickly determining the presence of such undetected infection cases in a network. We propose a simple optimization-based approach that can be used to determine whether or not a significant fraction of infection cases are undetected and thus are missing in reported statistics. We present numerical results obtained in a case study using publicly available COVID-19 data from four countries.

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Notes

  1. 1.

    Once widespread public health measures are in place, the spread dynamics are likely to change significantly with different parameters. For this reason, we considered the infection numbers in early stages.

  2. 2.

    Any mention of commercial products in this paper is for information only and is not intended for an endorsement or recommendation by NIST.

  3. 3.

    This estimate is for the period of February 2020 through May 2021.

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Author information

Authors and Affiliations

  1. ANTD, NIST, Gaithersburg, MD, 20899, USA

    Van Sy Mai

  2. ACMD, NIST, Gaithersburg, MD, 20899, USA

    Richard J. La

Authors
  1. Van Sy Mai
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  2. Richard J. La
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Correspondence to Richard J. La .

Editor information

Editors and Affiliations

  1. Escuela Técnica Superior de Ingeniería Agronómica, Alimentaria y de Biosistemas Universidad Politécnica de Madrid, Universidad Politécnica de Madrid, Madrid, Madrid, Spain

    Rosa Maria Benito

  2. IUT Lumière, University of Lyon, Bron Cedex, France

    Chantal Cherifi

  3. LE2I, UFR Sciences et Techniques, University of Burgundy, Dijon, France

    Hocine Cherifi

  4. Grupo Interdisciplinar de Sistemas Compl, Universidad Carlos III de Madrid, Leganés, Madrid, Spain

    Esteban Moro

  5. Center for Complex Networks and Systems Research School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA

    Luis M. Rocha

  6. Department of Chemical Engineering, Universitat Rovira i Virgili, Tarragona, Tarragona, Spain

    Marta Sales-Pardo

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Mai, V.S., La, R.J. (2022). Story of Two Populations in Epidemics: Is Every Infection Counted?. In: Benito, R.M., Cherifi, C., Cherifi, H., Moro, E., Rocha, L.M., Sales-Pardo, M. (eds) Complex Networks & Their Applications X. COMPLEX NETWORKS 2021. Studies in Computational Intelligence, vol 1016. Springer, Cham. https://doi.org/10.1007/978-3-030-93413-2_20

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  • DOI: https://doi.org/10.1007/978-3-030-93413-2_20

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  • Online ISBN: 978-3-030-93413-2

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