ABSTRACT
The COVID-19 pandemic has affected our lives in many ways. Many people faced different challenges during the pandemic to accomplish their daily activities. Many people faced various challenges during the pandemic might have been very stressful, overwhelming, and disgusting. Therefore, it is common to feel stress, irritation, mood swings, and anxiety during the pandemic. Different methodologies by medical practitioners are being taken. Additionally, researchers from academia are also trying to strengthen the methods. Unfortunately, the way for automatic, continuous, and invisible stress detection by the researchers are insufficient and not studied in depth. It becomes essential in the post-pandemic scenario due to COVID-19 disease. This paper studies the impact of stress on people during the COVID-19 pandemic. The study includes origin, classification, impact on health, prevention solutions, etc. Further statistics on the affected people by the stress during the period are provided. © 2023 IEEE.
ABSTRACT
This paper proposes a custom deep neural network-based scheme for coronavirus disease 2019 (COVID-19) detection. The proposed method takes X-ray images that use transfer learning techniques on pre-trained models. One objective of this work is to quickening the detection of the virus. Another goal is to reduce the number of falsely detected cases by a significant margin. The experimental setup demonstrates promising results on the selected dataset, which achieve up to 99.74%, 99.69%, 98.80% as classification, precision, and recall accuracy. © 2021 IEEE.
ABSTRACT
Background: Telemedicine has been an option for healthcare during the COVID-19 pandemic time. According to the COVID-19 forum on the International Society for Pediatric and Adolescent Diabetes, telemedicine is now the reality for all pediatric diabetes centers around the globe. We report here the analysis of telemedicine care provided to children and adolescents with type 1 diabetes by our diabetes team during the first phase of lockdown in Bangladesh. Materials and Methods: The Diabetic Association of Bangladesh (BADAS) is providing a highly structured and organized system of diabetes care as an extended arm of the government. Since the lockdown, diabetes care for children was started through telemedicine by the diabetes team in our center. Results: We analyzed our phone calls, text messages from March 26 to April 30, 2020. During lockdown, more than 645 calls, including text messages and WhatsApp messages, were received. A total of 235 patients were given advice over the phone in 1 month. Fifty-two percent of phone calls were from the capital city Dhaka and rest from other districts of Bangladesh. While analyzing the problems, three of our patients noticed mild fever, but there was no history of contact with infected persons, and they recovered within 2-3 days. No laboratory test was done for the confirmation of COVID-19. Most of the patients developed hyperglycemia during this period. None of them required hospital admission during this month. Free insulin from CDiC (Changing Diabetes in Children) and LFAC (Life for a Child) programs was sent to the BADAS centers in different districts through courier service even during the lockdown. So, there was no patient without access to insulin during pandemic time. Conclusion: Telemedicine service has been found to be a useful medium for the care of children with diabetes in Bangladesh during COVID-19 crisis. In the future, telemedicine service could be a solution for routine care of diabetic children who are unable or unwilling to travel long distances to a clinic.