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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]
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Database: Engineering Source
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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]
ISSN:09600779
DOI:10.1016/j.chaos.2022.112823