Davis, Jessica, Chinazzi, Matteo, Perra, Nicola ORCID: 0000-0002-5559-3064, Mu, Kunpeng, Pastore Y Piontti, Ana, Ajelli, Marco, Dean, Natalie, Gioannini, Corrado, Litvinova, Maria, Merler, Stefano, Rossi, Luca, Sun, Kaiyuan, Xiong, Xinyue, Longini, Ira Jr, Halloran, M. Elizabeth, Viboud, Cécile and Vespignani, Alessandro (2021) Cryptic transmission of SARS-CoV-2 and the first COVID-19 wave. Nature. ISSN 0028-0836 (Print), 1476-4687 (Online) (doi:https://doi.org/10.1038/s41586-021-04130-w)

Preview

PDF (Author's published manuscript) 34259_PERRA_Cryptic_transmission_of_SARS_CoV2.pdf - Published Version Download (8MB) | Preview

Abstract

Considerable uncertainty surrounds the timeline of introductions and onsets of local transmission of SARS-CoV-2 globally. Although a limited number of SARS-CoV-2 introductions were reported in January and February 2020, the narrowness of the initial testing criteria, combined with a slow growth in testing capacity and porous travel screening10, left many countries vulnerable to unmitigated, cryptic transmission. Here we use a global metapopulation epidemic model to provide a mechanistic understanding of the early dispersal of infections, and the temporal windows of the introduction and onset of SARS-CoV-2 local transmission in Europe and the United States. We find that community transmission of SARS-CoV-2 was likely in several areas of Europe and the United States by January 2020, and estimate that by early March, only 1 to 3 in 100 SARS-CoV-2 infections were detected by surveillance systems. The modelling results highlight international travel as the key driver of the introduction of SARS-CoV-2 with possible introductions and transmission events as early as December 2019–January 2020. We find a heterogeneous, geographic distribution of cumulative infection attack rates by 4 July 2020, ranging from 0.78%–15.2% across US states and 0.19%–13.2% in European countries. Our approach complements phylogenetic analyses and other surveillance approaches and provides insights that can be used to design innovative, model-driven surveillance systems that guide enhanced testing and response strategies.

Item Type:	Article
Uncontrolled Keywords:	COVID-19, modeling
Subjects:	H Social Sciences > HD Industries. Land use. Labor > HD61 Risk Management Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QR Microbiology > QR355 Virology
Faculty / School / Research Centre / Research Group:	Faculty of Business Faculty of Business > Department of International Business & Economics Faculty of Business > Networks and Urban Systems Centre (NUSC) Faculty of Business > Networks and Urban Systems Centre (NUSC) > Centre for Business Network Analysis (CBNA)
Related URLs:	Publisher
Last Modified:	22 Apr 2022 01:38
URI:	http://gala.gre.ac.uk/id/eprint/34259

Actions (login required)

View Item

Downloads