Literature DB >> 34344318

Dynamics of the COVID-19 epidemic in Ireland under mitigation.

Bernard Cazelles1,2,3, Benjamin Nguyen-Van-Yen4, Clara Champagne5,6, Catherine Comiskey7.   

Abstract

BACKGROUND: In Ireland and across the European Union the COVID-19 epidemic waves, driven mainly by the emergence of new variants of the SARS-CoV-2 have continued their course, despite various interventions from governments. Public health interventions continue in their attempts to control the spread as they wait for the planned significant effect of vaccination.
METHODS: To tackle this challenge and the observed non-stationary aspect of the epidemic we used a modified SEIR stochastic model with time-varying parameters, following Brownian process. This enabled us to reconstruct the temporal evolution of the transmission rate of COVID-19 with the non-specific hypothesis that it follows a basic stochastic process constrained by the available data. This model is coupled with Bayesian inference (particle Markov Chain Monte Carlo method) for parameter estimation and utilized mainly well-documented Irish hospital data.
RESULTS: In Ireland, mitigation measures provided a 78-86% reduction in transmission during the first wave between March and May 2020. For the second wave in October 2020, our reduction estimation was around 20% while it was 70% for the third wave in January 2021. This third wave was partly due to the UK variant appearing in Ireland. In June 2020 we estimated that sero-prevalence was 2.0% (95% CI: 1.2-3.5%) in complete accordance with a sero-prevalence survey. By the end of April 2021, the sero-prevalence was greater than 17% due in part to the vaccination campaign. Finally we demonstrate that the available observed confirmed cases are not reliable for analysis owing to the fact that their reporting rate has as expected greatly evolved.
CONCLUSION: We provide the first estimations of the dynamics of the COVID-19 epidemic in Ireland and its key parameters. We also quantify the effects of mitigation measures on the virus transmission during and after mitigation for the three waves. Our results demonstrate that Ireland has significantly reduced transmission by employing mitigation measures, physical distancing and lockdown. This has to date avoided the saturation of healthcare infrastructures, flattened the epidemic curve and likely reduced mortality. However, as we await for a full roll out of a vaccination programme and as new variants potentially more transmissible and/or more infectious could continue to emerge and mitigation measures change silent transmission, challenges remain.
© 2021. The Author(s).

Entities:  

Keywords:  COVID-19; Ireland; Mitigation; Stochastic model; Time varying parameters

Year:  2021        PMID: 34344318     DOI: 10.1186/s12879-021-06433-9

Source DB:  PubMed          Journal:  BMC Infect Dis        ISSN: 1471-2334            Impact factor:   3.090


  16 in total

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Journal:  Nature       Date:  2020-12-21       Impact factor: 49.962

2.  [E.N.T. and hospital-acquired infections].

Authors:  D Ferquel; J C Penot; E Borderon
Journal:  Ann Otolaryngol Chir Cervicofac       Date:  1982

3.  Bayesian nowcasting during the STEC O104:H4 outbreak in Germany, 2011.

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Journal:  Biometrics       Date:  2014-06-13       Impact factor: 2.571

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Authors:  Seth Flaxman; Swapnil Mishra; Axel Gandy; H Juliette T Unwin; Thomas A Mellan; Helen Coupland; Charles Whittaker; Harrison Zhu; Tresnia Berah; Jeffrey W Eaton; Mélodie Monod; Azra C Ghani; Christl A Donnelly; Steven Riley; Michaela A C Vollmer; Neil M Ferguson; Lucy C Okell; Samir Bhatt
Journal:  Nature       Date:  2020-06-08       Impact factor: 49.962

5.  Use of serological surveys to generate key insights into the changing global landscape of infectious disease.

Authors:  C Jessica E Metcalf; Jeremy Farrar; Felicity T Cutts; Nicole E Basta; Andrea L Graham; Justin Lessler; Neil M Ferguson; Donald S Burke; Bryan T Grenfell
Journal:  Lancet       Date:  2016-04-05       Impact factor: 79.321

6.  A mathematical model reveals the influence of population heterogeneity on herd immunity to SARS-CoV-2.

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Journal:  Science       Date:  2020-06-23       Impact factor: 47.728

7.  Impact of lockdown on COVID-19 epidemic in Île-de-France and possible exit strategies.

Authors:  Laura Di Domenico; Giulia Pullano; Chiara E Sabbatini; Pierre-Yves Boëlle; Vittoria Colizza
Journal:  BMC Med       Date:  2020-07-30       Impact factor: 8.775

8.  Accounting for non-stationarity in epidemiology by embedding time-varying parameters in stochastic models.

Authors:  Bernard Cazelles; Clara Champagne; Joseph Dureau
Journal:  PLoS Comput Biol       Date:  2018-08-15       Impact factor: 4.475

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Authors:  Roy M Anderson; Hans Heesterbeek; Don Klinkenberg; T Déirdre Hollingsworth
Journal:  Lancet       Date:  2020-03-09       Impact factor: 79.321

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Authors:  Ruiyun Li; Sen Pei; Bin Chen; Yimeng Song; Tao Zhang; Wan Yang; Jeffrey Shaman
Journal:  Science       Date:  2020-03-16       Impact factor: 47.728

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Journal:  Cancers (Basel)       Date:  2022-04-30       Impact factor: 6.575

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Authors:  Kate M Bubar; Casey E Middleton; Kristen K Bjorkman; Roy Parker; Daniel B Larremore
Journal:  medRxiv       Date:  2022-03-21

4.  Robustness analysis for quantitative assessment of vaccination effects and SARS-CoV-2 lineages in Italy.

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Journal:  BMC Infect Dis       Date:  2022-04-29       Impact factor: 3.667

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