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Assessing spread risk of COVID-19 within and beyond China in early 2020
Data Science and Management ; 2022.
Article in English | ScienceDirect | ID: covidwho-2004024
ABSTRACT
A novel coronavirus emerged in Wuhan in late 2019 and has caused the COVID-19 pandemic announced by the World Health Organization on March 12, 2020. This study was originally conducted in January 2020 to estimate the potential risk and geographic range of COVID-19 spread within and beyond China at the early stage of the pandemic. A series of connectivity and risk analyses based on domestic and international travel networks were conducted using historical aggregated mobile phone data and air passenger itinerary data. We found that the cordon sanitaire of Wuhan was likely to have occurred during the latter stages of peak population numbers leaving the city, with travellers departing into neighbouring cities and other megacities in China. We estimated that 59,912 air passengers, of which 834 (95% uncertainty interval 478–1349) had COVID-19 infection, travelled from Wuhan to 382 cities outside of mainland China during the two weeks prior to the city’s lockdown. Most of these destinations were located in Asia, but major hubs in Europe, the US and Australia were also prominent, with a strong correlation seen between the predicted risks of importation and the number of imported cases found. Given the limited understanding of emerging infectious diseases in the very early stages of outbreaks, our approaches and findings in assessing travel patterns and risk of transmission can help guide public health preparedness and intervention design for new COVID-19 waves caused by variants of concern and future pandemics to effectively limit transmission beyond its initial extent.
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Full text: Available Collection: Databases of international organizations Database: ScienceDirect Type of study: Prognostic study Language: English Journal: Data Science and Management Year: 2022 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: ScienceDirect Type of study: Prognostic study Language: English Journal: Data Science and Management Year: 2022 Document Type: Article