Differing impacts of global and regional responses on SARS-CoV-2 transmission cluster dynamics.


Journal

bioRxiv : the preprint server for biology
Titre abrégé: bioRxiv
Pays: United States
ID NLM: 101680187

Informations de publication

Date de publication:
06 Nov 2020
Historique:
pubmed: 12 11 2020
medline: 12 11 2020
entrez: 11 11 2020
Statut: epublish

Résumé

Although the global response to COVID-19 has not been entirely unified, the opportunity arises to assess the impact of regional public health interventions and to classify strategies according to their outcome. Analysis of genetic sequence data gathered over the course of the pandemic allows us to link the dynamics associated with networks of connected individuals with specific interventions. In this study, clusters of transmission were inferred from a phylogenetic tree representing the relationships of patient sequences sampled from December 30, 2019 to April 17, 2020. Metadata comprising sampling time and location were used to define the global behavior of transmission over this earlier sampling period, but also the involvement of individual regions in transmission cluster dynamics. Results demonstrate a positive impact of international travel restrictions and nationwide lockdowns on global cluster dynamics. However, residual, localized clusters displayed a wide range of estimated initial secondary infection rates, for which uniform public health interventions are unlikely to have sustainable effects. Our findings highlight the presence of so-called "super-spreaders", with the propensity to infect a larger-than-average number of people, in countries, such as the USA, for which additional mitigation efforts targeting events surrounding this type of spread are urgently needed to curb further dissemination of SARS-CoV-2.

Identifiants

pubmed: 33173870
doi: 10.1101/2020.11.06.370999
pmc: PMC7654859
pii:
doi:

Types de publication

Preprint

Langues

eng

Subventions

Organisme : NIAID NIH HHS
ID : R21 AI138815
Pays : United States

Auteurs

Brittany Rife Magalis (BR)

Department of Pathology, Immunology, and Laboratory Medicine, University of Florida, Gainesville, Florida, 32610, USA.
Emerging Pathogens Institute, University of Florida, Gainesville, Florida, 32610, USA.

Andrea Ramirez-Mata (A)

Department of Pathology, Immunology, and Laboratory Medicine, University of Florida, Gainesville, Florida, 32610, USA.
Emerging Pathogens Institute, University of Florida, Gainesville, Florida, 32610, USA.

Anna Zhukova (A)

Department of Computational Biology, Institut Pasteur, Paris, 75015, France.

Carla Mavian (C)

Department of Pathology, Immunology, and Laboratory Medicine, University of Florida, Gainesville, Florida, 32610, USA.
Emerging Pathogens Institute, University of Florida, Gainesville, Florida, 32610, USA.

Simone Marini (S)

Emerging Pathogens Institute, University of Florida, Gainesville, Florida, 32610, USA.
Department of Epidemiology, University of Florida, Gainesville, Florida, 32610, USA.

Frederic Lemoine (F)

Department of Computational Biology, Institut Pasteur, Paris, 75015, France.

Mattia Prosperi (M)

Department of Epidemiology, University of Florida, Gainesville, Florida, 32610, USA.

Olivier Gascuel (O)

Department of Computational Biology, Institut Pasteur, Paris, 75015, France.

Marco Salemi (M)

Department of Pathology, Immunology, and Laboratory Medicine, University of Florida, Gainesville, Florida, 32610, USA.
Emerging Pathogens Institute, University of Florida, Gainesville, Florida, 32610, USA.

Classifications MeSH