Research

Sequencing identifies multiple early introductions of SARS-CoV-2 to the New York City region

    • 1Institute for Systems Genetics, NYU Grossman School of Medicine, New York, New York 10016, USA;
    • 2Department of Pathology, NYU Grossman School of Medicine, New York, New York 10016, USA;
    • 3Genome Technology Center, Division of Advanced Research Technologies, Office of Science and Research, NYU Langone Health, New York, New York 10016, USA;
    • 4Center for Biospecimen Research and Development, NYU Langone Health, New York, New York 10016, USA;
    • 5MRC Centre for Global Infectious Disease Analysis and Department of Infectious Disease Epidemiology, Imperial College London, London W2 1PG, United Kingdom;
    • 6Department of Dermatology, NYU Grossman School of Medicine, New York, New York 10016, USA;
    • 7Medical Center IT, NYU Langone Health, New York, New York 10016, USA;
    • 8Division of Infectious Diseases and Immunology, Department of Medicine and NYU Langone Vaccine Center, NYU Grossman School of Medicine, New York, New York 10016, USA
Published October 22, 2020. https://doi.org/10.1101/gr.266676.120
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cover of Genome Research Vol 36 Issue 6
Current Issue:
June 2026, Vol. 36, No. 6
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Abstract

Effective public response to a pandemic relies upon accurate measurement of the extent and dynamics of an outbreak. Viral genome sequencing has emerged as a powerful approach to link seemingly unrelated cases, and large-scale sequencing surveillance can inform on critical epidemiological parameters. Here, we report the analysis of 864 SARS-CoV-2 sequences from cases in the New York City metropolitan area during the COVID-19 outbreak in spring 2020. The majority of cases had no recent travel history or known exposure, and genetically linked cases were spread throughout the region. Comparison to global viral sequences showed that early transmission was most linked to cases from Europe. Our data are consistent with numerous seeds from multiple sources and a prolonged period of unrecognized community spreading. This work highlights the complementary role of genomic surveillance in addition to traditional epidemiological indicators.

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