Currently, the spread of COVID-19 is running at a constant pace. The current situation is not so alarming, but every pandemic has a history of three waves. Two waves have been seen, and now expecting the third wave. Compartmental models are one of the methods that predict the severity of a pandemic. An enhanced SEIR model is expected to predict the new cases of COVID-19. The proposed model has an additional compartment of vaccination. This proposed model is the SEIRV model that predicts the severity of COVID-19 when the population is vaccinated. The proposed model is simulated with three conditions. The first condition is when social distancing is not incorporated, while the second condition is when social distancing is included. The third one condition is when social distancing is combined when the population is vaccinated. The result shows an epidemic growth rate of about 0.06 per day, and the number of infected people doubles every 10.7 days. Still, with imparting social distancing, the proposed model obtained the value of R is 1.3. Vaccination of infants and kids will be considered as future work.
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http://dx.doi.org/10.3390/life12050647 | DOI Listing |
BMC Public Health
January 2025
Department of Health Behavior and Social Medicine, West China School of Public Health and West China Fourth Hospital, Research Center for Palliative Care, West China-PUMC C.C. Chen Institute of Health, Sichuan University, Chengdu, P.R. China.
Background: The promotion of healthy dietary behaviors in adolescence is critical, which have long-term implications for lifelong health. Integration is an important method for improving limited theories of dietary behavior change. The present study proposes an integrated model aimed at identifying the diverse determinants of healthy dietary behaviors in adolescents and assesses its stage-specific nature as the potential for effective interventions.
View Article and Find Full Text PDFBMC Ophthalmol
January 2025
Department of Ophthalmology, Peking University People's Hospital, Beijing Key Laboratory of Ocular Disease and Optometry Science, Beijing, China.
Background: To analyze the demographic characteristics of retinopathy of prematurity (ROP) in China, attempting to propose optimized screening criteria and hopefully providing valuable information for future updates to the ROP guideline.
Methods: A multicenter, retrospective-cohort study was conducted. The study included infants born between January 1, 2018, and July 31, 2023, who underwent ROP screening and were diagnosed with ROP at seven screening centers in China.
BMC Gastroenterol
January 2025
Department of Gastroenterology and Hepatology, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, No. 55 Zhenhai Road, Xiamen, 361003, China.
Background: Metabolic dysfunction-associated steatotic liver disease (MASLD) has become the most common chronic liver disease worldwide. The pan-immune-inflammation value (PIV) has been proposed as a biomarker for assessing immune status and inflammation. There is currently no evidence regarding the effect of PIV on the risk of MASLD.
View Article and Find Full Text PDFBMC Med Imaging
January 2025
Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, 38541, Republic of Korea.
A bone fracture is a medical condition characterized by a partial or complete break in the continuity of the bone. Fractures are primarily caused by injuries and accidents, affecting millions of people worldwide. The healing process for a fracture can take anywhere from one month to one year, leading to significant economic and psychological challenges for patients.
View Article and Find Full Text PDFMed Biol Eng Comput
January 2025
Department of Computer Science and Engineering, Shri Shankaracharya Institute of Professional Management and Technology, Raipur, (C.G.), India.
This study presents an advanced methodology for 3D heart reconstruction using a combination of deep learning models and computational techniques, addressing critical challenges in cardiac modeling and segmentation. A multi-dataset approach was employed, including data from the UK Biobank, MICCAI Multi-Modality Whole Heart Segmentation (MM-WHS) challenge, and clinical datasets of congenital heart disease. Preprocessing steps involved segmentation, intensity normalization, and mesh generation, while the reconstruction was performed using a blend of statistical shape modeling (SSM), graph convolutional networks (GCNs), and progressive GANs.
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