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J Travel Med ; 27(8)2020 12 23.
Article in English | MEDLINE | ID: covidwho-889576

ABSTRACT

BACKGROUND: The COVID-19 pandemic has posed an ongoing global crisis, but how the virus spread across the world remains poorly understood. This is of vital importance for informing current and future pandemic response strategies. METHODS: We performed two independent analyses, travel network-based epidemiological modelling and Bayesian phylogeographic inference, to investigate the intercontinental spread of COVID-19. RESULTS: Both approaches revealed two distinct phases of COVID-19 spread by the end of March 2020. In the first phase, COVID-19 largely circulated in China during mid-to-late January 2020 and was interrupted by containment measures in China. In the second and predominant phase extending from late February to mid-March, unrestricted movements between countries outside of China facilitated intercontinental spread, with Europe as a major source. Phylogenetic analyses also revealed that the dominant strains circulating in the USA were introduced from Europe. However, stringent restrictions on international travel across the world since late March have substantially reduced intercontinental transmission. CONCLUSIONS: Our analyses highlight that heterogeneities in international travel have shaped the spatiotemporal characteristics of the pandemic. Unrestricted travel caused a large number of COVID-19 exportations from Europe to other continents between late February and mid-March, which facilitated the COVID-19 pandemic. Targeted restrictions on international travel from countries with widespread community transmission, together with improved capacity in testing, genetic sequencing and contact tracing, can inform timely strategies for mitigating and containing ongoing and future waves of COVID-19 pandemic.


Subject(s)
Air Travel , COVID-19 , Communicable Disease Control , Disease Transmission, Infectious , Global Health/statistics & numerical data , SARS-CoV-2/isolation & purification , Air Travel/statistics & numerical data , Air Travel/trends , Bayes Theorem , COVID-19/epidemiology , COVID-19/prevention & control , COVID-19/transmission , Communicable Disease Control/methods , Communicable Disease Control/organization & administration , Disease Transmission, Infectious/prevention & control , Disease Transmission, Infectious/statistics & numerical data , Epidemiologic Measurements , Epidemiological Monitoring , Humans , Phylogeny , Spatio-Temporal Analysis
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