Understanding Early Pandemic Severe Acute Respiratory Syndrome Coronavirus 2 Transmission in a Medical Center by Incorporating Public Sequencing Databases to Mitigate Bias

Author:

Turcinovic Jacquelyn12,Schaeffer Beau3,Taylor Bradford P3ORCID,Bouton Tara C4,Odom-Mabey Aubrey R25,Weber Sarah E4,Lodi Sara6,Ragan Elizabeth J4,Connor John H127,Jacobson Karen R4,Hanage William P3

Affiliation:

1. National Emerging Infectious Diseases Laboratories, Boston University , Boston, Massachusetts , USA

2. Bioinformatics Program, Boston University , Boston, Massachusetts , USA

3. Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health , Boston, Massachusetts , USA

4. Section of Infectious Diseases, Boston University School of Medicine and Boston Medical Center , Boston, Massachusetts , USA

5. Division of Computational Biomedicine, Boston University School of Medicine , Boston, Massachusetts , USA

6. Department of Biostatistics, Boston University School of Public Health , Boston, Massachusetts , USA

7. Department of Microbiology, Boston University School of Medicine , Boston, Massachusetts , USA

Abstract

Abstract Background Throughout the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, healthcare workers (HCWs) have faced risk of infection from within the workplace via patients and staff as well as from the outside community, complicating our ability to resolve transmission chains in order to inform hospital infection control policy. Here we show how the incorporation of sequences from public genomic databases aided genomic surveillance early in the pandemic when circulating viral diversity was limited. Methods We sequenced a subset of discarded, diagnostic SARS-CoV-2 isolates between March and May 2020 from Boston Medical Center HCWs and combined this data set with publicly available sequences from the surrounding community deposited in GISAID with the goal of inferring specific transmission routes. Results Contextualizing our data with publicly available sequences reveals that 73% (95% confidence interval, 63%–84%) of coronavirus disease 2019 cases in HCWs are likely novel introductions rather than nosocomial spread. Conclusions We argue that introductions of SARS-CoV-2 into the hospital environment are frequent and that expanding public genomic surveillance can better aid infection control when determining routes of transmission.

Funder

National Center for Advancing Translational Sciences

National Institutes of Health

Boston University Clinical & Translational Science Institute

National Institute of General Medical Sciences

Publisher

Oxford University Press (OUP)

Subject

Infectious Diseases,Immunology and Allergy

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