Temporal and Spatial Distribution of SARS-CoV-2 Aerosols in a Large-Scale Fangcang Shelter Hospital in Shanghai,China

The coronavirus disease 2019(COVID-19)pandemic caused by frequently mutating severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)has had a worldwide impact.However,detailed data on the potential aerosol transmission of SARS-CoV-2 in real-world and controlled laboratory settings remain sparse.During the COVID-19 pandemic in Shanghai,China in 2022,samples were collected in a Fangcang shelter hospital,a large-scale temporary hospital rapidly built by converting the existing National Exhibition and Convention Center(Shanghai)into a health care facility.Aerosol samples at different sites and intervals around patients and in public areas,surface samples,and pharyngeal swab samples from corresponding patients were included.Samples were tested for SARS-CoV-2 using real-time quantitative polymerase chain reaction(RT-qPCR)assays,followed by sequencing if the cycle threshold(Ct)value was<30.The positivity rate for SARS-CoV-2 in aerosol samples was high in contaminated zones(37.5%,104/277),especially around the bed(41.2%,68/165)and near ventilation inlets(45.2%,14/31).The prevalence of SARS-CoV-2 around the bed,public areas,and air inlets of exhaust vents fluctuated and was closely related to the positivity rate among patients at corresponding sampling sites.Some surface samples of different personal protective equipment from medical staff had high positivity rates.Sixty sequences of joined ORF1ab and spike genes obtained from sixty samples represented two main clusters of Omicron SARS-CoV-2.There was consistency in virus sequences from the same patient and their environment,and the detected virus sequences matched those of virus strains in circulation during the collection periods,which indicated a high likelihood of cross-contamination in the Fangcang shelter hospital.In summary,the results provide a quantitative and real landscape of the aerosol transmission of SARS-CoV-2 and a patient-centered view of contamination in large and enclosed spaces and offer a useful guide for taking targeted measures to avoid nosocomial infections during the management of SARS-CoV-2 or other respiratory virus diseases in a Fangcang shelter hospital.

Factors Predicting Progression to Severe COVID-19: A Competing Risk Survival Analysis of 1753 Patients in Community Isolation in Wuhan, China

Most studies of coronavirus disease 2019(COVID-19)progression have focused on the transfer of patients within secondary or tertiary care hospitals from regular wards to intensive care units.Little is known about the risk factors predicting the progression to severe COVID-19 among patients in community iso-lation,who are either asymptomatic or suffer from only mild to moderate symptoms.Using a multivari-able competing risk survival analysis,we identify several important predictors of progression to severe COVID-19—rather than to recovery—among patients in the largest community isolation center in Wuhan,China from 6 February 2020(when the center opened)to 9 March 2020(when it closed).All patients in community isolation in Wuhan were either asymptomatic or suffered from mild to moderate COVID-19 symptoms.We performed competing risk survival analysis on time-to-event data from a cohort study of all COVID-19 patients(n=1753)in the isolation center.The potential predictors we inves-tigated were the routine patient data collected upon admission to the isolation center:age,sex,respira-tory symptoms,gastrointestinal symptoms,general symptoms,and computed tomography(CT)scan signs.The main outcomes were time to severe COVID-19 or recovery.The factors predicting progression to severe COVID-19 were:male sex(hazard ratio(HR)=1.29,95%confidence interval(CI)1.04–1.58,p=0.018),young and old age,dyspnea(HR=1.58,95%CI 1.24–2.01,p<0.001),and CT signs of ground-glass opacity(HR=1.39,95%CI 1.04–1.86,p=0.024)and infiltrating shadows(HR=1.84,95%CI 1.22–2.78,p=0.004).The risk of progression was found to be lower among patients with nausea or vomiting(HR=0.53,95%CI 0.30–0.96,p=0.036)and headaches(HR=0.54,95%CI 0.29–0.99,p=0.046).Our results suggest that several factors that can be easily measured even in resource-poor set-tings(dyspnea,sex,and age)can be used to identify mild COVID-19 patients who are at increased risk of disease progression.Looking for CT signs of ground-glass opacity and infiltrating shadows may be an affordable option to support triage decisions in resource-rich settings.Common and unspecific symptoms(headaches,nausea,and vomiting)are likely to have led to the identification and subsequent community isolation of COVID-19 patients who were relatively unlikely to deteriorate.Future public health and clinical guidelines should build on this evidence to improve the screening,triage,and monitoring of COVID-19 patients who are asymtomatic or suffer from mild to moderate symptoms.