Impact of COVID-19 Pandemic on Patterns of Care and Outcome of Head and Neck Cancer: Real-World Experience From a Tertiary Care Cancer Center in India.

PURPOSE: The COVID-19 pandemic has caused unprecedented health, social, and economic unrest globally, particularly affecting resource-limited low-middle-income countries. The resultant curfew had made the access to and delivery of cancer care services an arduous task. We have reported the patterns of care and 1-year outcome of head and neck squamous cell carcinoma (HNSCC) treatment before and during COVID-19 lockdown at our institution. MATERIALS AND METHODS: Patients who underwent radiation therapy (RT) for nonmetastatic HNSCC between March 1, 2020, and July 31, 2020, were included in the COVID-RT group, and those who were treated between October 1, 2019, and February 29, 2020, were included in the preCOVID-RT group. RESULTS: A total of 25 patients were in the COVID-RT group, and 51 patients were in the preCOVID-RT group. An increase in the incidence of locally advanced cancers across all subsites was observed in the COVID-RT group. There was a steep increase in the median overall RT treatment duration (52 v 44) and median break days during RT (10 v 2) in the COVID-RT group. The median follow-up period of all patients was 18 months. The progression-free survival at 1 year in the COVID-RT group and preCOVID-RT group was 84% and 90%, respectively (P = .08), and overall survival at 1 year was 86% and 96%, respectively (P = .06). CONCLUSION: Our study elucidates the adverse impact of the COVID-19 curfew on cancer care and has demonstrated safe delivery of RT for HNSCC without major acute adverse effects. Despite a significant increase in treatment breaks, early outcome data also suggest that 1-year progression-free survival and overall survival are comparable with that of the pre-COVID-19 times; however, longer follow-up is warranted.

A Survey on Mathematical, Machine Learning and Deep Learning Models for COVID-19 Transmission and Diagnosis.

COVID-19 is a life threatening disease which has a enormous global impact. As the cause of the disease is a novel coronavirus whose gene information is unknown, drugs and vaccines are yet to be found. For the present situation, disease spread analysis and prediction with the help of mathematical and data driven model will be of great help to initiate prevention and control action, namely lockdown and qurantine. There are various mathematical and machine-learning models proposed for analyzing the spread and prediction. Each model has its own limitations and advantages for a particluar scenario. This article reviews the state-of-the art mathematical models for COVID-19, including compartment models, statistical models and machine learning models to provide more insight, so that an appropriate model can be well adopted for the disease spread analysis. Furthermore, accurate diagnose of COVID-19 is another essential process to identify the infected person and control further spreading. As the spreading is fast, there is a need for quick auotomated diagnosis mechanism to handle large population. Deep-learning and machine-learning based diagnostic mechanism will be more appropriate for this purpose. In this aspect, a comprehensive review on the deep learning models for the diagnosis of the disease is also provided in this article.

Acquired tracheoesophageal fistula in a pregnant patient with COVID-19 pneumonia on prolonged invasive ventilation.

A previously healthy pregnant woman was diagnosed with COVID-19 pneumonia and was subsequently intubated. Throughout the course of her illness, the patient was treated for recurrent bouts of pneumonia. A high-resolution chest and neck CT scan confirmed the presence of a tracheoesophageal fistula (TEF), which may have been caused by the presence of the overinflated endotracheal cuff, prolonged steroid use, hypoxic injury and possible direct injury of the tracheal mucosa from COVID-19 itself. A temporising procedure, involving tracheostomy with an extended-length tracheal tube, was performed. Unfortunately, the patient succumbed to infection prior to definitive repair. This case highlights the importance of keeping a high index of suspicion for tracheal injury in patients who experience prolonged periods of intubation. It also underlies the high morbidity and mortality rate associated with TEF, although being a rare disease.

Psychological Impact of Coronavirus Disease 2019 Among Italians During the First Week of Lockdown.

Pandemics and government-mandated quarantining measures have a substantial impact on mental health. This study investigated the psychological impact of the coronavirus disease 2019 (COVID-19) crisis on Italian residents during the first week of government-imposed lockdown and the role of defense mechanisms as protective factors against distress. In this cross-sectional study, 5,683 Italians responded to an online survey assessing socio-demographics, overall psychological distress, post-traumatic symptoms, and defense mechanisms using validated measures as the Symptom Checklist-90 (SCL-90), the Impact of Event Scale-Revised (IES-R), and the Defense Mechanisms Rating Scale-Self-Report-30 (DMRS-SR-30). Data were collected from March 13 to March 18, within the first week of lockdown in Italy. Results showed that younger age and female gender were associated with increased psychological distress. Having positive cases nearby, more days on lockdown, and having to relocate were also associated with greater distress. Higher overall defensive functioning (ODF) was associated with lower levels of depression (r = -.44, 95% CI -0.48, -0.40), anxiety (r = -.38, 95% CI -0.42, -0.35), and post-traumatic stress symptoms (PTSS) (r = -.34, 95% CI -0.38, -0.30). Conversely, less adaptive defensive functioning was related to greater affective distress across all domains. Each increased unit of ODF decreased the chances of developing post-traumatic stress symptoms (PTSS) by 71% (odds ratio = 0.29, p < 0.001, 95% CI.026,.032). The psychological impact of COVID-19 among Italians during the early weeks of government lockdown has been significant. The pandemic continues to have extraordinary mental health impact as it moves across the globe. Given the salience of defensive functioning in psychological distress, consideration of interventions that foster the use of more adaptive defenses may be an important component of building resilience amidst a pandemic.

Dynamical Analysis of Corona-virus (COVID−19) Epidemic Model by Differential Transform Method (preprint)

The aim of this paper is applying the Differential Transformation Method (DTM) to analyze and find the solution for the mathematical model described by the system of nonlinear ordinary differential equations which describe the epidemiology of the most threatening virus called Corona-virus later labelled as COVID-19. The behaviour of the outcomes is presented in terms of plots. Finally, the present study may help you to examine the wild class of real world models and also aid to predict their behaviour with respect to parameters considered in the model. The purpose of this study is to estimate the effectiveness of preventive measures, predicting future outbreaks and potential control strategies using the mathematical model.