Are Stay-at-Home and Face Mask Orders Effective in Slowing Down COVID-19 Transmission? – A Statistical Study of U.S. Case Counts in 2020

Whether the stay-at-home order and face mask mandate are effective in slowing down the COVID-19 virus transmission is up for debate. To investigate this matter, we employ a unique angle. A two-wave logistic equation is proposed and then fitted to the cumulative case counts of all 50 states in the U.S. from the onset to early December of 2020 when vaccinating begins at large scale. The data period is confined to isolate the effects of executive orders from that of vaccination. The length of the first wave's accelerating phase is regressed on variables describing the stay-at-home order and face mask mandate, along with control variables. A state's lockdown duration is discovered to be negatively related to the time it takes for the virus to transit from accelerating to decelerating rates. This finding provides statistical support to the executive orders and can be useful in guiding risk management of future pandemics.

Deep learning of chest X-rays can predict mechanical ventilation outcome in ICU-admitted COVID-19 patients.

The COVID-19 pandemic repeatedly overwhelms healthcare systems capacity and forced the development and implementation of triage guidelines in ICU for scarce resources (e.g. mechanical ventilation). These guidelines were often based on known risk factors for COVID-19. It is proposed that image data, specifically bedside computed X-ray (CXR), provide additional predictive information on mortality following mechanical ventilation that can be incorporated in the guidelines. Deep transfer learning was used to extract convolutional features from a systematically collected, multi-institutional dataset of COVID-19 ICU patients. A model predicting outcome of mechanical ventilation (remission or mortality) was trained on the extracted features and compared to a model based on known, aggregated risk factors. The model reached a 0.702 area under the curve (95% CI 0.707-0.694) at predicting mechanical ventilation outcome from pre-intubation CXRs, higher than the risk factor model. Combining imaging data and risk factors increased model performance to 0.743 AUC (95% CI 0.746-0.732). Additionally, a post-hoc analysis showed an increase performance on high-quality than low-quality CXRs, suggesting that using only high-quality images would result in an even stronger model.

Tracking and predicting COVID-19 radiological trajectory on chest X-rays using deep learning.

Radiological findings on chest X-ray (CXR) have shown to be essential for the proper management of COVID-19 patients as the maximum severity over the course of the disease is closely linked to the outcome. As such, evaluation of future severity from current CXR would be highly desirable. We trained a repurposed deep learning algorithm on the CheXnet open dataset (224,316 chest X-ray images of 65,240 unique patients) to extract features that mapped to radiological labels. We collected CXRs of COVID-19-positive patients from an open-source dataset (COVID-19 image data collection) and from a multi-institutional local ICU dataset. The data was grouped into pairs of sequential CXRs and were categorized into three categories: 'Worse', 'Stable', or 'Improved' on the basis of radiological evolution ascertained from images and reports. Classical machine-learning algorithms were trained on the deep learning extracted features to perform immediate severity evaluation and prediction of future radiological trajectory. Receiver operating characteristic analyses and Mann-Whitney tests were performed. Deep learning predictions between "Worse" and "Improved" outcome categories and for severity stratification were significantly different for three radiological signs and one diagnostic ('Consolidation', 'Lung Lesion', 'Pleural effusion' and 'Pneumonia'; all P < 0.05). Features from the first CXR of each pair could correctly predict the outcome category between 'Worse' and 'Improved' cases with a 0.81 (0.74-0.83 95% CI) AUC in the open-access dataset and with a 0.66 (0.67-0.64 95% CI) AUC in the ICU dataset. Features extracted from the CXR could predict disease severity with a 52.3% accuracy in a 4-way classification. Severity evaluation trained on the COVID-19 image data collection had good out-of-distribution generalization when testing on the local dataset, with 81.6% of intubated ICU patients being classified as critically ill, and the predicted severity was correlated with the clinical outcome with a 0.639 AUC. CXR deep learning features show promise for classifying disease severity and trajectory. Once validated in studies incorporating clinical data and with larger sample sizes, this information may be considered to inform triage decisions.

A Systematic Style-Based Blended Teaching for Competence Enhancement of Lecturers in the COVID-19 Pandemic Situation: A Case Study for Teaching in Higher Education

Currently, the global Covid-19 epidemic has been having an impact on the trend of the organization and implementation training activities of higher education. Blended teaching and building a systematic Style-Based Blended teaching model still have certain limitations for teachers. This study has surveyed and evaluated the results of implementing systematic Style-Based Blended teaching model with 135 lecturers from universities in Vietnam from the academic year 2019 - 2020. This study used a combination of qualitative and quantitative methods, the parameters of this research results were verified through evaluating the applied results before and after implementing systematic Style-Based Blended teaching model in teaching at experimental universities. Research results with this Blended teaching model present evidence in implementing and developing a systematic StyleBased Blended teaching model with effective effects on improving teaching performance of teachers, contributing to improve the quality of teaching in higher education._

A Systematic Style-Based Blended Teaching for Competence Enhancement of Lecturers in the COVID-19 Pandemic Situation: A Case Study for Teaching in Higher Education

Currently, the global Covid-19 epidemic has been having an impact on the trend of the organization and implementation training activities of higher education. Blended teaching and building a systematic Style-Based Blended teaching model still have certain limitations for teachers. This study has surveyed and evaluated the results of implementing systematic Style-Based Blended teaching model with 135 lecturers from universities in Vietnam from the academic year 2019 - 2020. This study used a combination of qualitative and quantitative methods, the parameters of this research results were verified through evaluating the applied results before and after implementing systematic Style-Based Blended teaching model in teaching at experimental universities. Research results with this Blended teaching model present evidence in implementing and developing a systematic Style-Based Blended teaching model with effective effects on improving teaching performance of teachers, contributing to improve the quality of teaching in higher education.