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Introduction: Health care workers (HCWs) are directly involved in processes linked with diagnosis, management, and assistance of coronavirus disease-19 (COVID-19) patients which could have direct implications on their physical and emotional health. Emotional aspects of working in an infectious pandemic situation is often neglected in favour of the more obvious physical ramifica-tions. This single point assessment study aimed to explore the factors related to stress, anxiety and depression among HCWs consequent to working in a pandemic. Material and Methods: This was a cross-sectional study involving healthcare workers who were working in COVID-19 inpatient ward, COVID-19 screening area, suspect ward, suspect intensive care unit (ICU) and COVID-19 ICU across four hospitals in India. A web-based survey questionnaire was designed to elicit responses to daily challenges faced by HCWs. The questionnaire was regressed using machine-learning algo-rithm (Cat Boost) against the standardized Depression, Anxiety and Stress Scale-21 (DASS 21) which was used to quantify emotional distress experienced by them. Results: A total of 156 participants were included in this study. As per DASS-21 scoring, severe stress was seen in ~17% of respondents. We could achieve an R2 of 0.28 using our machine-learning model. The major factors responsible for stress were decreased time available for personal needs, increasing age, being posted out of core area of expertise, setting of COVID-19 care, increasing duty hours, increasing duty days, marital status and being a resident physician. Conclusions: Factors elicited in this study that are associated with stress in HCWs need to be addressed to provide wholesome emotional support to HCWs bat-tling the pandemic. Targeted interventions may result in increased emotional resilience of the health-care system.

Introduction: Machine learning algorithms have been used to develop prediction models in various infectious and non-infectious settings including interpretation of images in predicting the outcome of diseases. We demonstrate the application of one such simple automated machine learning algorithm to a dataset obtained about COVID-19 spread in South Korea to better understand the disease dynamics. Material and methods: Data from 20th January 2020 (when the first case of COVID-19 was detected in South Korea) to 4th March 2020 was accessed from Korea’s centre for disease control (KCDC). A future time-series of specified length (taken as 7 days in our study) starting from 5th March 2020 to 11th March 2020 was generated and fed to the model to generate predictions with upper and lower trend bounds of 95% confidence intervals. The model was assessed for its ability to reliably forecast using mean absolute percentage error (MAPE) as the metric. Results: As on 4th March 2020, 145,541 patients were tested for COVID-19 (in 45 days) in South Korea of which 5166 patients tested positive. The predicted values approximated well with the actual numbers. The difference between predicted and observed values ranged from 4.08% to 12.77% . On average, our predictions differed from actual values by 7.42% (MAPE) over the same period. Conclusion: Open source and automated machine learning tools like Prophet can be applied and are effective in the context of COVID-19 for forecasting spread in naïve communities. It may help countries to efficiently allocate healthcare resources to contain this pandemic.

As no definitive therapy or vaccine is yet available for COVID-19, in a desperate attempt repurposed drugs are being explored as an option. A drug repurposing study identified Ciclesonide as a potential candidate. We reviewed the available evidence and clinical trials on the use of Ciclesonide in COVID-19. At present the evidence is limited to a report of three cases. However, five clinical trials are underway, and their results will help in elucidating the role of Ciclesonide in COVID-19.

As no definitive therapy or vaccine is yet available for COVID-19, in a desperate attempt repurposed drugs are being explored as an option. A drug repurposing study identified Ciclesonide as a potential candidate. We reviewed the available evidence and clinical trials on the use of Ciclesonide in COVID-19. At present the evidence is limited to a report of three cases. However, five clinical trials are underway, and their results will help in elucidating the role of Ciclesonide in COVID-19.