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Title: | A Bayesian Risk Assessment of the Covid-19 Pandemic Using FMEA and a Modified SEIR Epidemic Model |
Authors: | Koucha, Y Yang, Q |
Keywords: | coronavirus;COVID-19;Bayesian inference;SEIR model;stochastic epidemic models;FMEA;failure mode and effect analysis |
Issue Date: | 10-Jun-2021 |
Publisher: | EDP Sciences |
Citation: | Koucha, Y. and Yang, Q. (2021) 'A Bayesian Risk Assessment of the Covid-19 Pandemic Using FMEA and a Modified SEIR Epidemic Model', International Journal of Metrology and Quality Engineering, 12, 14, pp. 1 - 19. doi: 10.1051/ijmqe/2021012. |
Abstract: | Copyright © Y. Koucha and Q.P. Yang 2021. The COVID-19 outbreak is of great concern due to the high rates of infection and the large number of deaths worldwide. In this paper, we considered a Bayesian inference and failure mode and effects analysis of the modified susceptible-exposed-infectious-removed model for the transmission dynamics of COVID-19 with an exponentially distributed infectious period. We estimated the effective reproduction number based on laboratory-confirmed cases and death data using Bayesian inference and analyse the impact of the community spread of COVID-19 across the United Kingdom. We used the failure mode and effects analysis tool to evaluate the effectiveness of the action measures taken to manage the COVID-19 pandemic. We focused on COVID-19 infections and therefore the failure mode is taken as positive cases. The model is applied to COVID-19 data showing the effectiveness of interventions adopted to control the epidemic by reducing the reproduction number of COVID-19. Results have shown that the combination of Bayesian inference, compartmental modelling and failure mode and effects analysis is effective in modelling and studying the risks of COVID-19 transmissions, leading to the quantitative evaluation of the action measures and the identification of the lessons learned from the governmental measures and actions taken in response to COVID-19 in the United Kingdom. Analytical and numerical methods are used to highlight the practical implications of our findings. The proposed methodology will find applications in current and future COVID-19 like pandemics and wide quality engineering. |
URI: | https://bura.brunel.ac.uk/handle/2438/22735 |
DOI: | https://doi.org/10.1051/ijmqe/2021012 |
ISSN: | 2107-6839 |
Other Identifiers: | 14 |
Appears in Collections: | Dept of Mechanical and Aerospace Engineering Research Papers |
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