of coronavirus disease (COVID-19) had been reported globally since December 2019 (1), severely burdening the healthcare system (2). The extremely fast transmission capability of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has aroused concern about its various transmission routes.The main transmission routes for SARS-CoV-2 are respiratory droplets and close contact (3). Knowing the extent of environmental contamination of SARS-CoV-2 in COVID-19 wards is critical for improving safety practices for medical staff and answering questions about SARS-CoV-2 transmission among the public. However, whether SARS-CoV-2 can be transmitted by aerosols remains controversial, and the exposure risk for close contacts has not been systematically evaluated. Researchers have detected SARS-CoV-2 on surfaces of objects in a symptomatic patient's room and toilet area (4). However, that study was performed in a small sample from regions with few confirmed cases, which might not reflect real conditions in outbreak regions where hospitals are operating at full capacity. In this study, we tested surface and air samples from an intensive care unit (ICU) and a general COVID-19 ward (GW) at Huoshenshan Hospital in Wuhan, China (Figure 1). The StudyFrom February 19 through March 2, 2020, we collected swab samples from potentially contaminated objects in the ICU and GW as described previously (5). The ICU housed 15 patients with severe disease and the GW housed 24 patients with milder disease. We also sampled indoor air and the air outlets to detect aerosol exposure. Air samples were collected by using a SASS 2300 Wetted Wall Cyclone Sampler (Research International, Inc., https://www.resrchintl.com) at 300 L/min for of 30 min. We used sterile premoistened swabs to sample the floors, computer mice, trash cans, sickbed handrails, patient masks, personal protective equipment, and air outlets. We tested air and surface samples for the open reading frame (ORF) 1ab and nucleoprotein (N) genes of SARS-CoV-2 by quantitative real-time PCR. (Appendix, https://wwwnc.cdc.gov/EID/ article/26/7/20-0885-App1.pdf).Almost all positive results were concentrated in the contaminated areas (ICU 54/57, 94.7%; GW 9/9, 100%); the rate of positivity was much higher for the ICU (54/124, 43.5%) than for the GW (9/114, 7.9%) (Tables 1, 2). The rate of positivity was

Objectives To develop and validate a radiomics model for predicting 2019 novel coronavirus (COVID-19) pneumonia. Methods For this retrospective study, a radiomics model was developed on the basis of a training set consisting of 136 patients with COVID-19 pneumonia and 103 patients with other types of viral pneumonia. Radiomics features were extracted from the lung parenchyma window. A radiomics signature was built on the basis of reproducible features, using the least absolute shrinkage and selection operator method (LASSO). Multivariable logistic regression model was adopted to establish a radiomics nomogram. Nomogram performance was determined by its discrimination, calibration, and clinical usefulness. The model was validated in 90 consecutive patients, of which 56 patients had COVID-19 pneumonia and 34 patients had other types of viral pneumonia. Results The radiomics signature, consisting of 3 selected features, was significantly associated with COVID-19 pneumonia (p < 0.05) in both training and validation sets. The multivariable logistic regression model included the radiomics signature and distribution; maximum lesion, hilar, and mediastinal lymph node enlargement; and pleural effusion. The individualized prediction nomogram showed good discrimination in the training sample (area under the receiver operating characteristic curve [AUC], 0.959; 95% confidence interval [CI], 0.933-0.985) and in the validation sample (AUC, 0.955; 95% CI, 0.899-0.995) and good calibration. The mixed model achieved better predictive efficacy than the clinical model. Decision curve analysis demonstrated that the radiomics nomogram was clinically useful. Conclusions The radiomics model derived has good performance for predicting COVID-19 pneumonia and may help in clinical decision-making. Key Points • A radiomics model showed good performance for prediction 2019 novel coronavirus pneumonia and favorable discrimination for other types of pneumonia on CT images. • A central or peripheral distribution, a maximum lesion range > 10 cm, the involvement of all five lobes, hilar and mediastinal lymph node enlargement, and no pleural effusion is associated with an increased risk of 2019 novel coronavirus pneumonia. • A radiomics model was superior to a clinical model in predicting 2019 novel coronavirus pneumonia.

Purpose: This study examines the mediation and moderation roles of workplace support in the effect of role stress on burnout among newly recruited social workers in mainland China. Method: A total of 1,638 newly recruited social workers, those in their first year of professional employment, are invited as participants to complete a questionnaire package, which includes their demographic information, role stress, workplace support, and Maslach’s Burnout Inventory. Results: Role stress significantly correlates with emotional exhaustion, depersonalization, and decreased personal accomplishment of burnout. Workplace support moderates the effects of role stress on three syndromes of burnout. In addition, role stress can lead to burnout by reducing perceptions of workplace support. Conclusions: Workplace support is important in buffering the effect of role stress on burnout. Results suggest interventions on improving workplace support to alleviate role stress and burnout of new social workers would be beneficial.