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Understanding Early Pandemic Severe Acute Respiratory Syndrome Coronavirus 2 Transmission in a Medical Center by Incorporating Public Sequencing Databases to Mitigate Bias.
Turcinovic, Jacquelyn; Schaeffer, Beau; Taylor, Bradford P; Bouton, Tara C; Odom-Mabey, Aubrey R; Weber, Sarah E; Lodi, Sara; Ragan, Elizabeth J; Connor, John H; Jacobson, Karen R; Hanage, William P.
  • Turcinovic J; National Emerging Infectious Diseases Laboratories, Boston University, Boston, Massachusetts, USA.
  • Schaeffer B; Bioinformatics Program, Boston University, Boston, Massachusetts, USA.
  • Taylor BP; Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.
  • Bouton TC; Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.
  • Odom-Mabey AR; Section of Infectious Diseases, Boston University School of Medicine and Boston Medical Center, Boston, Massachusetts, USA.
  • Weber SE; Bioinformatics Program, Boston University, Boston, Massachusetts, USA.
  • Lodi S; Division of Computational Biomedicine, Boston University School of Medicine, Boston, Massachusetts, USA.
  • Ragan EJ; Section of Infectious Diseases, Boston University School of Medicine and Boston Medical Center, Boston, Massachusetts, USA.
  • Connor JH; Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA.
  • Jacobson KR; Section of Infectious Diseases, Boston University School of Medicine and Boston Medical Center, Boston, Massachusetts, USA.
  • Hanage WP; National Emerging Infectious Diseases Laboratories, Boston University, Boston, Massachusetts, USA.
J Infect Dis ; 226(10): 1704-1711, 2022 Nov 11.
Article in English | MEDLINE | ID: covidwho-2117943
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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: SARS-CoV-2 / COVID-19 Type of study: Observational study / Prognostic study Limits: Humans Language: English Journal: J Infect Dis Year: 2022 Document Type: Article Affiliation country: Infdis

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Full text: Available Collection: International databases Database: MEDLINE Main subject: SARS-CoV-2 / COVID-19 Type of study: Observational study / Prognostic study Limits: Humans Language: English Journal: J Infect Dis Year: 2022 Document Type: Article Affiliation country: Infdis