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Systems Biology Approaches to Understanding COVID-19 Spread in the Population

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Systems Biology

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2745))

Abstract

In essence, the COVID-19 pandemic can be regarded as a systems biology problem, with the entire world as the system, and the human population as the element transitioning from one state to another with certain transition rates. While capturing all the relevant features of such a complex system is hardly possible, compartmental epidemiological models can be used as an appropriate simplification to model the system’s dynamics and infer its important characteristics, such as basic and effective reproductive numbers of the virus. These measures can later be used as response variables in feature selection methods to uncover the main factors contributing to disease transmissibility. We here demonstrate that a combination of dynamic modeling and machine learning approaches can represent a powerful tool in understanding the spread, not only of COVID-19, but of any infectious disease of epidemiological proportions.

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Acknowledgements

This work was supported by the Ministry of Education, Science and Technological Development of the Republic of Serbia.

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Authors and Affiliations

  1. Quantitative Biology Group, Faculty of Biology, University of Belgrade, Belgrade, Serbia

    Sofija Marković & Marko Djordjevic

  2. Institute of Physics Belgrade, National Institute of the Republic of Serbia, Belgrade, Serbia

    Igor Salom

Authors
  1. Sofija Marković
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  2. Igor Salom
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  3. Marko Djordjevic
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Correspondence to Marko Djordjevic .

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Editors and Affiliations

  1. Department of Experimental Medicine, Sapienza University, Rome, Italy

    Mariano Bizzarri

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Marković, S., Salom, I., Djordjevic, M. (2024). Systems Biology Approaches to Understanding COVID-19 Spread in the Population. In: Bizzarri, M. (eds) Systems Biology. Methods in Molecular Biology, vol 2745. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3577-3_15

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  • DOI: https://doi.org/10.1007/978-1-0716-3577-3_15

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-3576-6

  • Online ISBN: 978-1-0716-3577-3

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