Detection of Air and Surface Contamination by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in Hospital Rooms of Infected Patients

Po Ying Chia(Nanyang Technological University), Kristen K. Coleman(National University of Singapore), Yian Kim Tan(DSO National Laboratories), Sean Wei Xiang Ong(Tan Tock Seng Hospital), Marcus Gum(DSO National Laboratories), Sok Kiang Lau(DSO National Laboratories), Stephanie Sutjipto(Tan Tock Seng Hospital), Pei Hua Lee(Tan Tock Seng Hospital), Than The Son(National University of Singapore), Barnaby Edward Young(Nanyang Technological University), Donald K. Milton, Gregory C. Gray(National University of Singapore), Stephan C. Schuster(Nanyang Technological University), Timothy Barkham(National University of Singapore), Partha Prathim De(Nanyang Technological University), Shawn Vasoo(Nanyang Technological University), Mónica Chan(Tan Tock Seng Hospital), Brenda Ang(National University of Singapore), Boon Huan Tan(DSO National Laboratories), Yee‐Sin Leo(National University of Singapore), Oon Tek Ng(Nanyang Technological University), Michelle Wong(DSO National Laboratories), Kalisvar Marimuthu(National University of Singapore)
medRxiv
April 1, 2020
Cited by 97Open Access
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Abstract

Abstract Understanding the particle size distribution in the air and patterns of environmental contamination of SARS-CoV-2 is essential for infection prevention policies. We aimed to detect SARS-CoV-2 surface and air contamination and study associated patient-level factors. 245 surface samples were collected from 30 airborne infection isolation rooms of COVID-19 patients, and air sampling was conducted in 3 rooms. Air sampling detected SARS-CoV-2 PCR-positive particles of sizes >4 µm and 1-4 µm in two rooms, which warrants further study of the airborne transmission potential of SARS-CoV-2. 56.7% of rooms had at least one environmental surface contaminated. High touch surface contamination was shown in ten (66.7%) out of 15 patients in the first week of illness, and three (20%) beyond the first week of illness (p = 0.010).


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