Public Health Importance of Emporiatrics: A Review.

People travel all around the world to explore, trade, sojourn, etc. Millions of individuals cross national and international borders. Travel medicine services are offered by general practitioners, specialized travel clinics, or immunization centers. Epidemiology, illness prevention, and travel-related self-treatment are all included in the interdisciplinary field of travel medicine. The main objective is to keep travelers alive and in good health, by reducing the effects of illness and accidents through preventative measures and self-care. The danger to a traveler's health and well-being must be understood, and the travel medicine practitioner's job is to help their patient or client recognize and manage those risks. The absence of any disease or symptom does not always indicate good health. Chronic illness sufferers, including those with cancer, diabetes, and hypertension, can maintain a reasonable level of health and mobility. Travel medicine is a rapidly developing, extremely dynamic, multidisciplinary field that calls for knowledge of a range of travel-related illnesses as well as current information on the global epidemiology of infectious and non-infectious health risks, immunization laws and requirements around the world, and the shifting trends in drug-resistant infections. Pre-travel consultation aims to reduce the traveler's risk of disease and harm while on the road through preventive counseling, education, recommended drugs, and essential vaccines. Specialized medical guidance can help reduce the potential health risks of travel. Emporiatrics is not only used for traveling advice or things to be done during the period of the journey but it also creates room in implementing the interdisciplinary subject with new methods or development of new policies, technologies, and various programs to reduce unnecessary problems of the travelers, which will boost tourism.

A Critical Review of Global Digital Divide and the Role of Technology in Healthcare.

Healthcare and technology, the fusion of these two distinct sciences can be traced back to the Vedic era. Regrettably, while it is evident that the journey of advancements in knowledge and innovation leading to the advent of technology to better the health of mankind is not a recent one, owing to inexistent means of transfer of knowledge, these contraptions stayed mostly localized to the regions of their inventors. This article seeks to review the vital role that technology has in bettering the health status of the global community and the challenges associated with healthcare technologies like inequity in connectivity, affordability, and accessibility. Technology and artificial intelligence are integrated to the best of the health systems across the world but these advancements are not accessible to a considerable part of the global population. While affordability, the absence of a steady internet supply, and the lack of a device to use the technology are the major impediments causing this digital divide, cultural factors and health literacy also contribute to this scenario. Nevertheless, access to the internet has been recognized as a basic need by all governments around the globe. The COVID-19 pandemic shook the health systems of developed and developing countries alike and has made every administration feel the urgency in making healthcare more accessible. Having seamless internet coverage and setups to make telemedicine or online consultations possible, can contribute significantly in paving the path to making our societies prosperous and healthier. With the world's consensus about this goal, efforts now should be focused on research and development for making these technologies more affordable and accessible without compromising their utility.

Correlation Between Chest CT Severity Scores and Glycosylated Haemoglobin Levels and its Outcome in Patients With COVID-19: A Retrospective Study in a Tertiary Care Hospital.

Background In this study, we aimed to compare the imaging findings between coronavirus disease (COVID-19) patients with well-controlled, poorly-controlled, and non-diabetic patients and subsequently find any relation between haemoglobin A1c (HbA1c) levels and high-resolution chest computed tomography (HRCT) chest score. Methodology A total of 200 individuals with coexisting COVID-19 and type 2 diabetes mellitus were included in this retrospective cohort study. Based on their HbA1c levels, patients were divided into three groups. The imaging data and laboratory values were obtained from the online medical records of the patients. In addition, the chest computed tomography (CT) score was evaluated as the sum of individual scores from five lung lobes: scores of 0, 1, 2, 3, 4, and 5 were assigned to each lobe. Any peripheral opacification pattern was noted. Haemoglobin A1c (HbA1c) levels and HRCT scores were then analysed by multiple linear regression models using R software. Results The prevalence of diabetes in the study population was 71.5%. Of this, 56 patients had well-controlled diabetes (28%) and 87 patients had poorly controlled diabetes (43.5%); 126 (63%) patients were male and the median age was 54.45 years (95% CI: 54.45 ± 15.53). We found that diabetes status, co-presence of ground-glass appearance with mixed consolidation, and consolidation and reverse halo sign in the HRCT findings were significant predictors of the HRCT scores in patients with COVID-19. Conclusions The presence of any co-morbidities should be viewed as a high-risk case of COVID-19. Diabetes status is significantly associated with the severity of HRCT findings in lab-confirmed COVID-19 infection. Therefore, it is important to prioritise the patients who have COVID-19 along with diabetes.

COVID-19 image classification using deep learning: Advances, challenges and opportunities.

Corona Virus Disease-2019 (COVID-19), caused by Severe Acute Respiratory Syndrome-Corona Virus-2 (SARS-CoV-2), is a highly contagious disease that has affected the lives of millions around the world. Chest X-Ray (CXR) and Computed Tomography (CT) imaging modalities are widely used to obtain a fast and accurate diagnosis of COVID-19. However, manual identification of the infection through radio images is extremely challenging because it is time-consuming and highly prone to human errors. Artificial Intelligence (AI)-techniques have shown potential and are being exploited further in the development of automated and accurate solutions for COVID-19 detection. Among AI methodologies, Deep Learning (DL) algorithms, particularly Convolutional Neural Networks (CNN), have gained significant popularity for the classification of COVID-19. This paper summarizes and reviews a number of significant research publications on the DL-based classification of COVID-19 through CXR and CT images. We also present an outline of the current state-of-the-art advances and a critical discussion of open challenges. We conclude our study by enumerating some future directions of research in COVID-19 imaging classification.

Automated detection of COVID-19 from CT scan using convolutional neural network.

Under the prevailing circumstances of the global pandemic of COVID-19, early diagnosis and accurate detection of COVID-19 through tests/screening and, subsequently, isolation of the infected people would be a proactive measure. Artificial intelligence (AI) based solutions, using Convolutional Neural Network (CNN) and exploiting the Deep Learning model's diagnostic capabilities, have been studied in this paper. Transfer Learning approach, based on VGG16 and ResNet50 architectures, has been used to develop an algorithm to detect COVID-19 from CT scan images consisting of Healthy (Normal), COVID-19, and Pneumonia categories. This paper adopts data augmentation and fine-tuning techniques to improve and optimize the VGG16 and ResNet50 model. Further, stratified 5-fold cross-validation has been conducted to test the robustness and effectiveness of the model. The proposed model performs exceptionally well in case of binary classification (COVID-19 vs. Normal) with an average classification accuracy of more than 99% in both VGG16 and ResNet50 based models. In multiclass classification (COVID-19 vs. Normal vs. Pneumonia), the proposed model achieves an average classification accuracy of 86.74% and 88.52% using VGG16 and ResNet50 architectures as baseline, respectively. Experimental results show that the proposed model achieves superior performance and can be used for automated detection of COVID-19 from CT scans.

Modeling and prediction of COVID-19 pandemic using Gaussian mixture model.

COVID-19 is caused by a novel coronavirus and has played havoc on many countries across the globe. A majority of the world population is now living in a restricted environment for more than a month with minimal economic activities, to prevent exposure to this highly infectious disease. Medical professionals are going through a stressful period while trying to save the larger population. In this paper, we develop two different models to capture the trend of a number of cases and also predict the cases in the days to come, so that appropriate preparations can be made to fight this disease. The first one is a mathematical model accounting for various parameters relating to the spread of the virus, while the second one is a non-parametric model based on the Fourier decomposition method (FDM), fitted on the available data. The study is performed for various countries, but detailed results are provided for the India, Italy, and United States of America (USA). The turnaround dates for the trend of infected cases are estimated. The end-dates are also predicted and are found to agree well with a very popular study based on the classic susceptible-infected-recovered (SIR) model. Worldwide, the total number of expected cases and deaths are 12.7 × 106 and 5.27 × 105, respectively, predicted with data as of 06-06-2020 and 95% confidence intervals. The proposed study produces promising results with the potential to serve as a good complement to existing methods for continuous predictive monitoring of the COVID-19 pandemic.