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Economic aspects of the detection of new strains in a multi-strain epidemiological–mathematical model.

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Title: Economic aspects of the detection of new strains in a multi-strain epidemiological–mathematical model.
Authors: Shami, Labib1 (AUTHOR) labibs@wgalil.ac.il, Lazebnik, Teddy1,2 (AUTHOR)
Source: Chaos, Solitons & Fractals. Dec2022:Part 2, Vol. 165, pN.PAG-N.PAG. 1p.
Subjects: Machine learning, Statistical sampling, Human beings, Pandemics, Genetic algorithms
Abstract: Mankind has struggled with pathogens throughout history. In this context, the contribution of vaccines to the continued economic and social prosperity of humanity is enormous, but it is constantly threatened by the development of vaccine-resistant strains of the pathogen. In this study, we investigate the usage of genomic sequencing tests to detect new strains of a pathogen in a multi-strain pandemic scenario using a mathematical–epidemiological–genomic–economic model. Our model provides a theoretical framework to explore the influence of an extensive number of pharmaceutical interventions in a dynamic multi-strain pandemic. Specifically, we show that while a genomic sequence testing policy can be both economically and epidemiologically efficient, a random sample of the population provides sub-optimal results. Moreover, we demonstrate that the optimal policy is sensitive to the social and economic settings of the population, and provide a machine learning based model that offers a solution to these challenges. [ABSTRACT FROM AUTHOR]
Copyright of Chaos, Solitons & Fractals is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: Economic aspects of the detection of new strains in a multi-strain epidemiological–mathematical model.
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  Data: <searchLink fieldCode="JN" term="%22Chaos%2C+Solitons+%26+Fractals%22">Chaos, Solitons & Fractals</searchLink>. Dec2022:Part 2, Vol. 165, pN.PAG-N.PAG. 1p.
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  Data: Mankind has struggled with pathogens throughout history. In this context, the contribution of vaccines to the continued economic and social prosperity of humanity is enormous, but it is constantly threatened by the development of vaccine-resistant strains of the pathogen. In this study, we investigate the usage of genomic sequencing tests to detect new strains of a pathogen in a multi-strain pandemic scenario using a mathematical–epidemiological–genomic–economic model. Our model provides a theoretical framework to explore the influence of an extensive number of pharmaceutical interventions in a dynamic multi-strain pandemic. Specifically, we show that while a genomic sequence testing policy can be both economically and epidemiologically efficient, a random sample of the population provides sub-optimal results. Moreover, we demonstrate that the optimal policy is sensitive to the social and economic settings of the population, and provide a machine learning based model that offers a solution to these challenges. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Chaos, Solitons & Fractals is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1016/j.chaos.2022.112823
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      – Code: eng
        Text: English
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      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Statistical sampling
        Type: general
      – SubjectFull: Human beings
        Type: general
      – SubjectFull: Pandemics
        Type: general
      – SubjectFull: Genetic algorithms
        Type: general
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      – TitleFull: Economic aspects of the detection of new strains in a multi-strain epidemiological–mathematical model.
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              Text: Dec2022:Part 2
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              Y: 2022
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              Value: 165
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