S'abonner

Early dynamics of transmission and control of COVID-19: a mathematical modelling study - 19/06/20

Doi : 10.1016/S1473-3099(20)30144-4 
Adam J Kucharski, PhD a, , Timothy W Russell, PhD a, Charlie Diamond, MSc a, Yang Liu, PhD a, John Edmunds, PhD a, Sebastian Funk, PhD a, Rosalind M Eggo, PhD a
on behalf of the

Centre for Mathematical Modelling of Infectious Diseases COVID-19 working group

  Members are listed in the Supplementary Material
Fiona Sun, Mark Jit, James D Munday, Nicholas Davies, Amy Gimma, Kevin van Zandvoort, Hamish Gibbs, Joel Hellewell, Christopher I Jarvis, Sam Clifford, Billy J Quilty, Nikos I Bosse, Sam Abbott, Petra Klepac, Stefan Flasche

a Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK 

* Correspondence to: Dr Adam J Kucharski, Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK Centre for Mathematical Modelling of Infectious Diseases London School of Hygiene & Tropical Medicine London WC1E 7HT UK

Bienvenue sur EM-consulte, la référence des professionnels de santé.
Article gratuit.

Connectez-vous pour en bénéficier!

Summary

Background

An outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to 95 333 confirmed cases as of March 5, 2020. Understanding the early transmission dynamics of the infection and evaluating the effectiveness of control measures is crucial for assessing the potential for sustained transmission to occur in new areas. Combining a mathematical model of severe SARS-CoV-2 transmission with four datasets from within and outside Wuhan, we estimated how transmission in Wuhan varied between December, 2019, and February, 2020. We used these estimates to assess the potential for sustained human-to-human transmission to occur in locations outside Wuhan if cases were introduced.

Methods

We combined a stochastic transmission model with data on cases of coronavirus disease 2019 (COVID-19) in Wuhan and international cases that originated in Wuhan to estimate how transmission had varied over time during January, 2020, and February, 2020. Based on these estimates, we then calculated the probability that newly introduced cases might generate outbreaks in other areas. To estimate the early dynamics of transmission in Wuhan, we fitted a stochastic transmission dynamic model to multiple publicly available datasets on cases in Wuhan and internationally exported cases from Wuhan. The four datasets we fitted to were: daily number of new internationally exported cases (or lack thereof), by date of onset, as of Jan 26, 2020; daily number of new cases in Wuhan with no market exposure, by date of onset, between Dec 1, 2019, and Jan 1, 2020; daily number of new cases in China, by date of onset, between Dec 29, 2019, and Jan 23, 2020; and proportion of infected passengers on evacuation flights between Jan 29, 2020, and Feb 4, 2020. We used an additional two datasets for comparison with model outputs: daily number of new exported cases from Wuhan (or lack thereof) in countries with high connectivity to Wuhan (ie, top 20 most at-risk countries), by date of confirmation, as of Feb 10, 2020; and data on new confirmed cases reported in Wuhan between Jan 16, 2020, and Feb 11, 2020.

Findings

We estimated that the median daily reproduction number (Rt) in Wuhan declined from 2·35 (95% CI 1·15–4·77) 1 week before travel restrictions were introduced on Jan 23, 2020, to 1·05 (0·41–2·39) 1 week after. Based on our estimates of Rt, assuming SARS-like variation, we calculated that in locations with similar transmission potential to Wuhan in early January, once there are at least four independently introduced cases, there is a more than 50% chance the infection will establish within that population.

Interpretation

Our results show that COVID-19 transmission probably declined in Wuhan during late January, 2020, coinciding with the introduction of travel control measures. As more cases arrive in international locations with similar transmission potential to Wuhan before these control measures, it is likely many chains of transmission will fail to establish initially, but might lead to new outbreaks eventually.

Funding

Wellcome Trust, Health Data Research UK, Bill & Melinda Gates Foundation, and National Institute for Health Research.

Le texte complet de cet article est disponible en PDF.

Plan


© 2020  The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Publié par Elsevier Masson SAS. Tous droits réservés.
Ajouter à ma bibliothèque Retirer de ma bibliothèque Imprimer
Export

    Export citations

  • Fichier

  • Contenu

Vol 20 - N° 5

P. 553-558 - mai 2020 Retour au numéro
Article précédent Article précédent
  • Campaigns for change
  • Aaron van Dorn
| Article suivant Article suivant
  • Clinical features and obstetric and neonatal outcomes of pregnant patients with COVID-19 in Wuhan, China: a retrospective, single-centre, descriptive study
  • Nan Yu, Wei Li, Qingling Kang, Zhi Xiong, Shaoshuai Wang, Xingguang Lin, Yanyan Liu, Juan Xiao, Haiyi Liu, Dongrui Deng, Suhua Chen, Wanjiang Zeng, Ling Feng, Jianli Wu

Bienvenue sur EM-consulte, la référence des professionnels de santé.

Mon compte


Plateformes Elsevier Masson

Déclaration CNIL

EM-CONSULTE.COM est déclaré à la CNIL, déclaration n° 1286925.

En application de la loi nº78-17 du 6 janvier 1978 relative à l'informatique, aux fichiers et aux libertés, vous disposez des droits d'opposition (art.26 de la loi), d'accès (art.34 à 38 de la loi), et de rectification (art.36 de la loi) des données vous concernant. Ainsi, vous pouvez exiger que soient rectifiées, complétées, clarifiées, mises à jour ou effacées les informations vous concernant qui sont inexactes, incomplètes, équivoques, périmées ou dont la collecte ou l'utilisation ou la conservation est interdite.
Les informations personnelles concernant les visiteurs de notre site, y compris leur identité, sont confidentielles.
Le responsable du site s'engage sur l'honneur à respecter les conditions légales de confidentialité applicables en France et à ne pas divulguer ces informations à des tiers.


Tout le contenu de ce site: Copyright © 2024 Elsevier, ses concédants de licence et ses contributeurs. Tout les droits sont réservés, y compris ceux relatifs à l'exploration de textes et de données, a la formation en IA et aux technologies similaires. Pour tout contenu en libre accès, les conditions de licence Creative Commons s'appliquent.