Coronaviruses have been implicated in nosocomial outbreaks 1 with environmental contamination as a route of transmission. 2 Similarly, nosocomial transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been reported. 3 However, the mode of transmission and extent of environmental contamination are unknown.Methods | From January 24 to February 4, 2020, 3 patients at the dedicated SARS-CoV-2 outbreak center in Singapore in airborne infection isolation rooms (12 air exchanges per hour) with anterooms and bathrooms had surface environmental samples taken at 26 sites. Personal protective equipment (PPE) samples from study physicians exiting the patient rooms also were collected. Sterile premoistened swabs were used.Air sampling was done on 2 days using SKC Universal pumps (with 37-mm filter cassettes and 0.3-μm polytetrafluoroethylene filters for 4 hours at 5 L/min) in the room and anteroom and a Sartorius MD8 microbiological sampler (with gelatin membrane filter for 15 minutes at 6 m 3 /h) outside the room (eFigure in the Supplement).Specific real-time reverse transcriptase-polymerase chain reaction (RT-PCR) targeting RNA-dependent RNA polymerase and E genes 4 was used to detect the presence of SARS-CoV-2 (see detailed methods in the eAppendix in the Supplement). Cycle threshold values, ie, number of cycles required for the fluorescent signal to cross the threshold in RT-PCR, quantified viral load, with lower values indicating higher viral load.

Understanding the particle size distribution in the air and patterns of environmental contamination of SARS-CoV-2 is essential for infection prevention policies. Here we screen surface and air samples from hospital rooms of COVID-19 patients for SARS-CoV-2 RNA. Environmental sampling is conducted in three airborne infection isolation rooms (AIIRs) in the ICU and 27 AIIRs in the general ward. 245 surface samples are collected. 56.7% of rooms have at least one environmental surface contaminated. High touch surface contamination is 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 = 0.01, χ2 test). Air sampling is performed in three of the 27 AIIRs in the general ward, and detects SARS-CoV-2 PCR-positive particles of sizes >4 µm and 1–4 µm in two rooms, despite these rooms having 12 air changes per hour. This warrants further study of the airborne transmission potential of SARS-CoV-2.

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Background The proportion of asymptomatic carriers and transmission risk factors of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) among household and non-household contacts remains unclear. In Singapore, extensive contact tracing by the Ministry of Health for every diagnosed COVID-19 case, and legally enforced quarantine and intensive health surveillance of close contacts provided a rare opportunity to determine asymptomatic attack rates and SARS-CoV-2 transmission risk factors among community close contacts of patients with COVID-19. Methods This retrospective cohort study involved all close contacts of confirmed COVID-19 cases in Singapore, identified between Jan 23 and April 3, 2020. Household contacts were defined as individuals who shared a residence with the index COVID-19 case. Non-household close contacts were defined as those who had contact for at least 30 min within 2 m of the index case. All patients with COVID-19 in Singapore received inpatient treatment, with access restricted to health-care staff. All close contacts were quarantined for 14 days with thrice-daily symptom monitoring via telephone. Symptomatic contacts underwent PCR testing for SARS-CoV-2. Secondary clinical attack rates were derived from the prevalence of PCR-confirmed SARS-CoV-2 among close contacts. Consenting contacts underwent serology testing and detailed exposure risk assessment. Bayesian modelling was used to estimate the prevalence of missed diagnoses and asymptomatic SARS-CoV-2-positive cases. Univariable and multivariable logistic regression models were used to determine SARS-CoV-2 transmission risk factors. Findings Between Jan 23 and April 3, 2020, 7770 close contacts (1863 household contacts, 2319 work contacts, and 3588 social contacts) linked to 1114 PCR-confirmed index cases were identified. Symptom-based PCR testing detected 188 COVID-19 cases, and 7582 close contacts completed quarantine without a positive SARS-CoV-2 PCR test. Among 7518 (96·8%) of the 7770 close contacts with complete data, the secondary clinical attack rate was 5·9% (95% CI 4·9–7·1) for 1779 household contacts, 1·3% (0·9–1·9) for 2231 work contacts, and 1·3% (1·0–1·7) for 3508 social contacts. Bayesian analysis of serology and symptom data obtained from 1150 close contacts (524 household contacts, 207 work contacts, and 419 social contacts) estimated that a symptom-based PCR-testing strategy missed 62% (95% credible interval 55–69) of COVID-19 diagnoses, and 36% (27–45) of individuals with SARS-CoV-2 infection were asymptomatic. Sharing a bedroom (multivariable odds ratio [OR] 5·38 [95% CI 1·82–15·84]; p=0·0023) and being spoken to by an index case for 30 min or longer (7·86 [3·86–16·02]; p<0·0001) were associated with SARS-CoV-2 transmission among household contacts. Among non-household contacts, exposure to more than one case (multivariable OR 3·92 [95% CI 2·07–7·40], p<0·0001), being spoken to by an index case for 30 min or longer (2·67 [1·21–5·88]; p=0·...

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Background Key knowledge gaps remain in the understanding of viral dynamics and immune response of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection. Methods We evaluated these characteristics and established their association with clinical severity in a prospective observational cohort study of 100 patients with PCR-confirmed SARS-CoV-2 infection (mean age, 46 years; 56% male; 38% with comorbidities). Respiratory samples (n = 74) were collected for viral culture, serum samples for measurement of IgM/IgG levels (n = 30), and plasma samples for levels of inflammatory cytokines and chemokines (n = 81). Disease severity was correlated with results from viral culture, serologic testing, and immune markers. Results Fifty-seven (57%) patients developed viral pneumonia, of whom 20 (20%) required supplemental oxygen, including 12 (12%) with invasive mechanical ventilation. Viral culture from respiratory samples was positive for 19 of 74 patients (26%). No virus was isolated when the PCR cycle threshold (Ct) value was &gt;30 or &gt;14 days after symptom onset. Seroconversion occurred at a median (IQR) of 12.5 (9–18) days for IgM and 15.0 (12–20) days for IgG; 54/62 patients (87.1%) sampled at day 14 or later seroconverted. Severe infections were associated with earlier seroconversion and higher peak IgM and IgG levels. Levels of IP-10, HGF, IL-6, MCP-1, MIP-1α, IL-12p70, IL-18, VEGF-A, PDGF-BB, and IL-1RA significantly correlated with disease severity. Conclusions We found virus viability was associated with lower PCR Ct value in early illness. A stronger antibody response was associated with disease severity. The overactive proinflammatory immune signatures offer targets for host-directed immunotherapy, which should be evaluated in randomized controlled trials.

BackgroundThe United States FDA approved an over-the-counter HIV self-test, to facilitate increased HIV testing and earlier linkage to care. We assessed the accuracy of self-testing by untrained participants compared to healthcare worker (HCW) testing, participants’ ability to interpret sample results and user-acceptability of self-tests in Singapore.Methodology/Principal FindingsA cross-sectional study, involving 200 known HIV-positive patients and 794 unknown HIV status at-risk participants was conducted. Participants (all without prior self-test experience) performed self-testing guided solely by visual instructions, followed by HCW testing, both using the OraQuick ADVANCE Rapid HIV 1/2 Antibody Test, with both results interpreted by the HCW. To assess ability to interpret results, participants were provided 3 sample results (positive, negative, and invalid) to interpret. Of 192 participants who tested positive on HCW testing, self-testing was positive in 186 (96.9%), negative in 5 (2.6%), and invalid in 1 (0.5%). Of 794 participants who tested negative on HCW testing, self-testing was negative in 791 (99.6%), positive in 1 (0.1%), and invalid in 2 (0.3%). Excluding invalid tests, self-testing had sensitivity of 97.4% (95% CI 95.1% to 99.7%) and specificity of 99.9% (95% CI: 99.6% to 100%). When interpreting results, 96%, 93.1% and 95.2% correctly read the positive, negative and invalid respectively. There were no significant demographic predictors for false negative self-testing or wrongly interpreting positive or invalid sample results as negative. Eighty-seven percent would purchase the kit over-the-counter; 89% preferred to take HIV tests in private. 72.5% and 74.9% felt the need for pre- and post-test counseling respectively. Only 28% would pay at least USD15 for the test.Conclusions/SignificanceSelf-testing was associated with high specificity, and a small but significant number of false negatives. Incorrectly identifying model results as invalid was a major reason for incorrect result interpretation. Survey responses were supportive of making self-testing available.

Background Rapid identification of COVID-19 cases, which is crucial to outbreak containment efforts, is challenging due to the lack of pathognomonic symptoms and in settings with limited capacity for specialized nucleic acid–based reverse transcription polymerase chain reaction (PCR) testing. Methods This retrospective case-control study involves subjects (7–98 years) presenting at the designated national outbreak screening center and tertiary care hospital in Singapore for SARS-CoV-2 testing from 26 January to 16 February 2020. COVID-19 status was confirmed by PCR testing of sputum, nasopharyngeal swabs, or throat swabs. Demographic, clinical, laboratory, and exposure-risk variables ascertainable at presentation were analyzed to develop an algorithm for estimating the risk of COVID-19. Model development used Akaike’s information criterion in a stepwise fashion to build logistic regression models, which were then translated into prediction scores. Performance was measured using receiver operating characteristic curves, adjusting for overconfidence using leave-one-out cross-validation. Results The study population included 788 subjects, of whom 54 (6.9%) were SARS-CoV-2 positive and 734 (93.1%) were SARS-CoV-2 negative. The median age was 34 years, and 407 (51.7%) were female. Using leave-one-out cross-validation, all the models incorporating clinical tests (models 1, 2, and 3) performed well with areas under the receiver operating characteristic curve (AUCs) of 0.91, 0.88, and 0.88, respectively. In comparison, model 4 had an AUC of 0.65. Conclusions Rapidly ascertainable clinical and laboratory data could identify individuals at high risk of COVID-19 and enable prioritization of PCR testing and containment efforts. Basic laboratory test results were crucial to prediction models.