Transmission dynamics of the COVID-19 epidemic in England

Liu, Yang; Tang, Julian W.; Lam, Tommy Tsan-Yuk

doi:10.1016/j.ijid.2020.12.055

Cited by 15 publications

(8 citation statements)

References 48 publications

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“…For the SIR model, the infected individuals are capable of infecting the susceptible individuals through the recovery period. Concerning the latent period, the SEIR model, which is an extended model of the SIR, has been used for the simulation of COVID-19 [14][15][16][17][18][19][20][21]. It consists of four compartments of 'Susceptible', 'Exposed who is infected but not infectious during the latent period', 'Infectious who is infectious after the latent period until the recovery period is ended' and 'Recovered'.…”

Section: Calculation For the Case Of Infection During The Latent Peri...mentioning

confidence: 99%

A flexible compartment model for simulation specific to COVID-19

Ohmori

2022

Preprint

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The dynamic relation among ‘Susceptible’, ‘Infected’, ‘Removed (Recovered)’, ‘Death’ and others for COVID-19 disease is a kind of multibody problem. It has been simulated mainly by the compartment models, of which the representative is the SIR model. For the SIR model, ‘Infected’ infects ‘Susceptible’ through the recovery period, and ‘Infected’ is removed as ‘Removed’ not only from the disease but also from the community after the recovery period is ended. For COVID-19, however, the infected individuals should be isolated from the community when they become symptomatic after the latent period is ended. Thus, the infected individuals do not infect susceptible individuals in the community after the latent period, even during the recovery period. Additionally, the infection has occurred in the community even during the latent period before the infected individuals are isolated due to being symptomatic. These two facts for COVID-19 suggest that the simulation by the SIR model would be less accurate in calculating the number of infected individuals and that the results might mislead political and medical interventions. For the model proposed here, the infected individuals are isolated from the community when they become symptomatic after the latent period is ended, but the recovered individuals who have medically recovered and have immunity return to the community, and the infection occurs even during the latent period. The model shows remarkably different results from those simulated by the SIR model. The model also provides the processes evaluating the political and social countermeasures against COVID-19.

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Section: Calculation For the Case Of Infection During The Latent Peri...mentioning

confidence: 99%

A flexible compartment model for simulation specific to COVID-19

Ohmori

2022

Preprint

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“…However, another estimate based on a bats-hosts-reservoirpeople transmission network model resulted in a value of 3.58 [30]. As the virus spread across Europe, further early R 0 estimates were published from Italy (2.43-3.10) [39] and England (2.8-3.10) [96]. The value of R 0 is studied continuously, as environmental and viral features evolve, and more data become available.…”

Section: Epidemiological Parameters Estimationmentioning

confidence: 99%

“…However, another estimate based on a bats-hosts-reservoir-people transmission network model resulted in a value of 3.58 [ 30 ]. As the virus spread across Europe, further early \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$R_0$\end{document} estimates were published from Italy (2.43–3.10) [ 39 ] and England (2.8–3.10) [ 96 ]. The value of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$R_0$\end{document} is studied continuously, as environmental and viral features evolve, and more data become available.…”

Section: Article Reviews By Topicmentioning

confidence: 99%

Impact of computational approaches in the fight against COVID-19: an AI guided review of 17 000 studies

Napolitano

Gao

2021

Briefings in Bioinformatics

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SARS-CoV-2 caused the first severe pandemic of the digital era. Computational approaches have been ubiquitously used in an attempt to timely and effectively cope with the resulting global health crisis. In order to extensively assess such contribution, we collected, categorized and prioritized over 17 000 COVID-19-related research articles including both peer-reviewed and preprint publications that make a relevant use of computational approaches. Using machine learning methods, we identified six broad application areas i.e. Molecular Pharmacology and Biomarkers, Molecular Virology, Epidemiology, Healthcare, Clinical Medicine and Clinical Imaging. We then used our prioritization model as a guidance through an extensive, systematic review of the most relevant studies. We believe that the remarkable contribution provided by computational applications during the ongoing pandemic motivates additional efforts toward their further development and adoption, with the aim of enhancing preparedness and critical response for current and future emergencies.

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“…Acheampong et al, 2022 [10] constructed a modified SEIR compartmental model to delineate the transmission dynamics of SARS-CoV-2 in Ghana and evaluate the basic reproduction number. Liu et al, 2021 [11] also proposed a Bayesian SEIR epidemiological model which can explain the transmission dynamics in the nine regions of England.…”

Section: Introductionmentioning

confidence: 99%