A Bayesian risk assessment of the COVID-19 pandemic using FMEA and a modified SEIR epidemic model
Department of Mechanical and Aerospace Engineering, Brunel University London,
* Corresponding author: email@example.com
Accepted: 16 May 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.
Key words: Coronavirus / COVID-19 / Bayesian inference / SEIR model / stochastic epidemic models / FMEA / failure mode and effect analysis
© Y. Koucha and Q.P. Yang, Published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.