ABSTRACT
Purpose of study COVID-19 has shifted the utilization of health care resources. Gaps remain in our understanding on how COVID-19 affects trends in pediatric trauma, the leading cause of mortality and morbidity during childhood and adolescence. We identified trends in the numbers and types of traumas presenting to a Level 1 Pediatric Trauma Center during the COVID-19 pandemic compared to prior years. Methods used We compared high acuity trauma visits (defined as traumas requiring admission, emergent surgical intervention or resulting in a fatality) presenting between January 1st and August 31st, 2020 to corresponding months in 2017-2019. We also evaluated the changes in mechanisms of injury during this time period. Data were analyzed using longitudinal time series analyses and t-tests. Summary of results Of 480 traumas presenting from January to August 2020, 227 (47.3%, 95%CI 42.7%-51.9%) were high acuity traumas. High acuity traumas declined significantly, as a state of emergency was declared, to a nadir of 16 in April 2020 (compared to the 2017-2019 mean of 38.3, p<0.001). As restrictions were lifted, high acuity traumas increased and surpassed previous years to a peak of 40 visits in August 2020 (2017-2019 mean 35.7, p<0.001). High acuity traumas as a proportion of total Emergency Department visits were higher from March to August 2020 compared to prior years (figure 1). There were more visits for high acuity assaults and child abuse but fewer for falls, drownings, and motor vehicle accidents from March to August 2020 compared to prior years, while visits for animal attacks remained stable Conclusions This analysis provides insight into how the COVID-19 pandemic has affected high acuity trauma in an inner-city pediatric population. Findings may be used to guide public health measures on safety and injury prevention as the pandemic continues and further restrictions are debated. (Figure Presented).
ABSTRACT
Background: Chronic urticaria (CU) is a common chronic inflammatory disease. Vaccination against viral infections including COVID-19 can induce increased CU disease activity. As of now, it is unclear how often CU exacerbations occur after COVID-19 vaccination. Method(s): COVAC-CU is an international, multicenter, observational, cross-sectional study of the global network of urticaria centers of reference and excellence (UCAREs). COVAC-CU evaluates the effects of COVID-19 vaccination in patients with CU including rates and risk factors of CU exacerbation. Here, we analyzed 1857 patients with CU who had received at least one COVID-19 vaccination. Data were collected via a questionnaire and retrieved from patient charts. Result(s): Of 1857 patients with CU (median age: 42 years;range: 18-91 years), 72.1% were female and 71.2%, 14.4% and 14.4% had chronic spontaneous urticaria, chronic inducible urticaria, or both, respectively. Most patients had received two doses of COVID-19 vaccine (79.1%), compared to one (9.7%), three (11%), or four (0.3%). Vaccine type included: BTN162b2 (58.4%;BioNTech/Pfizer), ChAdOx1 nCOV-19 (13.8%;AstraZeneca), BBIBP-CorV (8.2%;Sinopharm), Gam-COVID- Vac (8%;Sputnik), mRNA-1273 (5.3%;Moderna), and Ad26.COV 2.5 (4.7%;Janssen/J&J). Less than 10% of patients used premedication, and less than half of patients (44.4%) reported one or more adverse reactions after vaccination. The most common adverse reactions were local injection site reactions (29.6%), fatigue (19.7%), fever (19%), muscle pain (17.9%), headache (14%), and exacerbation of CU (15%). Severe allergic reactions/anaphylaxis were reported by 0.4% of CU patients. In almost all patients who experienced exacerbation of their CU, this occurred within one week after receiving the vaccine, i.e. after 1 to 12 hours (25.8 %), after 12 hours to 48 hours (31.1%) or after 2-7 days (37.9%). Conclusion(s): Most CU patients tolerate COVID-19 vaccination well;severe allergic reaction (anaphylaxis) rates were similar or lower than the self-reported rates reported in the general population. Exacerbation of urticaria was reported in one in five patients, mostly in a week after receiving the vaccine.
ABSTRACT
The COVID-19 rapid antigen self-test kits are widely administered in several countries to increase the testing frequency and reduce the load on clinics for in-person tests. Yet, the telehealth worker supervision is mandatory to ensure proper sampling procedure is followed and high-quality swab samples are taken. To reduce the load on the health workers in telehealth, we propose a system that eliminates the need for any human supervision by guiding the testers throughout the self-test to ensure the collection of high-quality swab samples. The proposed system takes a live video stream of the frontal face of a user as input and provides real-time instructions to do the self-test correctly with corrective actions when detecting wrong steps. This is mainly done using a collection of deep learning (DL) models. The system uses a novel swab position classification model, Small-MobileNetV2 with Depth-Wise Attention (S-MBNV2-DWAtt), to detect whether a swab is in one of the nostrils or not, which is an optimized version of MobileNetV2 in terms of parameter count and inference speed. The depth-wise attention block allows it to focus on specific parts of the images where the swabs would possibly lie. Lastly, a large-scale synthetic dataset is created to increase the generalization to a variety of swabs and users and a small real dataset is collected to finetune the model on scenes that are similar to the deployment scenarios. The proposed swab position classification model is found to have outstanding performance in terms of both accuracy and speed;it outperforms the ResNet and VGG architectures by 22.83% and 35.11% respectively on a real-world test set while operating at 25 FPS on CPU. © 2022 IEEE.
ABSTRACT
The Artificial Intelligence (AI) can promote research and find optimal solutions for complex and unstable situations. COVID-19 highlights the urgent need to innovate and offer modern solutions. Those solutions must meet the business requirement but also the current circumstances. In this paper, we are going to describe a new E-service application: Online Donation to Help Fight COVID-19. Our online donation software is perfect for nonprofits. The application has many features to suit our needs and their support response time. We use the Machine learning technique K-Nearest Neighbor to identify the ideal beneficiaries (school, hospital…). Our project can resolve the problem of donation management and establish the transparency and trust. © 2021 International Information and Engineering Technology Association. All rights reserved.