Effects of face shield on an emitter during a cough process: A large-eddy simulation study.

Face shield is a common personal protection equipment for pandemic. In the present work, three-dimensional computational fluid dynamic (CFD) method is used to simulate a cough jet from an emitter who wears a face shield. A realistic manikin model with a simplified mouth cavity is employed. A large eddy simulation with a dynamic structure subgrid scale model is applied to model the turbulence. An Eulerian-Lagrangian approach is adopted to model the two-phase flows, with which the droplets are represented by a cloud of particles. The droplet breakup, evaporation, dispersion, drag force, and wall impingement are considered in this model. An inlet velocity profile that is based on a variable mouth opening area is considered. Special attentions have been put the vortex structure and droplet re-distribution induced by the face shield. It is found that the multiple vortices are formed when the cough jet impinges on the face shield. Some droplets move backward and others move downward after the impinging. It is also found that a small modification of the face shield significantly modifies the flow field and droplet distribution. We conclude that face shield significantly reduces the risk factor in the front of the emitter, meanwhile the risk factor in the back of the emitter increases. When the receiver standing in front of the emitter is shorter than the emitter, the risk is still very high. More attentions should be paid on the design of the face field, clothes cleaning and floor cleaning of the emitters with face shields. Based on the predicted droplet trajectory, a conceptual model for droplet flux is proposed for the scenario with the face shield.

Deep Learning Models for Predicting Severe Progression in COVID-19-Infected Patients: Retrospective Study.

BACKGROUND: Many COVID-19 patients rapidly progress to respiratory failure with a broad range of severities. Identification of high-risk cases is critical for early intervention. OBJECTIVE: The aim of this study is to develop deep learning models that can rapidly identify high-risk COVID-19 patients based on computed tomography (CT) images and clinical data. METHODS: We analyzed 297 COVID-19 patients from five hospitals in Daegu, South Korea. A mixed artificial convolutional neural network (ACNN) model, combining an artificial neural network for clinical data and a convolutional neural network for 3D CT imaging data, was developed to classify these cases as either high risk of severe progression (ie, event) or low risk (ie, event-free). RESULTS: Using the mixed ACNN model, we were able to obtain high classification performance using novel coronavirus pneumonia lesion images (ie, 93.9% accuracy, 80.8% sensitivity, 96.9% specificity, and 0.916 area under the curve [AUC] score) and lung segmentation images (ie, 94.3% accuracy, 74.7% sensitivity, 95.9% specificity, and 0.928 AUC score) for event versus event-free groups. CONCLUSIONS: Our study successfully differentiated high-risk cases among COVID-19 patients using imaging and clinical features. The developed model can be used as a predictive tool for interventions in aggressive therapies.