The aim of this study is to predict the COVID-19 infection fifth wave in South Africa using the Gaussian mixture model for the available data of the early four waves for March 18, 2020-April 13, 2022. The quantification data is considered, and the time unit is used in days. We give the modeling of COVID-19 in South Africa and predict the future fifth wave in the country. Initially, we use the Gaussian mixture model to characterize the coronavirus infection to fit the early reported cases of four waves and then to predict the future wave. Actual data and the statistical analysis using the Gaussian mixture model are performed which give close agreement with each other, and one can able to predict the future wave. After that, we fit and predict the fifth wave in the country and it is predicted to be started in the last week of May 2022 and end in the last week of September 2022. It is predicted that the peak may occur on the third week of July 2022 with a high number of 19383 cases. The prediction of the fifth wave can be useful for the health authorities in order to prepare themselves for medical setup and other necessary measures. Further, we use the result obtained from the Gaussian mixture model in the new model formulated in terms of differential equations. The differential equations model is simulated for various values of the model parameters in order to determine the disease's possible eliminations.
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http://dx.doi.org/10.1155/2022/9932483 | DOI Listing |
Neural Comput
January 2025
Department of Advanced Data Science, Institute of Statistical Mathematics, Tachikawa, Tokyo 190-8562, Japan
Standard domain adaptation methods do not work well when a large gap exists between the source and target domains. Gradual domain adaptation is one of the approaches used to address the problem. It involves leveraging the intermediate domain, which gradually shifts from the source domain to the target domain.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, Netherlands.
Background: There is a strong link between tau and progression of Alzheimer's disease (AD), necessitating an understanding of tau spreading mechanisms. Prior research, predominantly in typical AD, suggested that tau propagates from epicenters (regions with earliest tau) to functionally connected regions. However, given the constrained spatial heterogeneity of tau in typical AD, validating this connectivity-based tau spreading model in AD variants with distinct tau deposition patterns is crucial.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea, Republic of (South).
Background: We aimed to investigate whether the quantitative analysis of plasma biomarkers could distinguish the pathology stages indicated by positron emission tomography (PET)-based Thal phase of amyloid and Braak stage of tau.
Method: A total of 232 participants were enrolled, all of whom underwent F-florbetaben (FBB), F-flortaucipir (FTP) PET, plasma p-tau217/np-tau217 ratio, p-tau217, and Aβ ratio. To differentiate between image-based Thal phases and Braak stages, region-of-interests (ROIs) were constructed, and cut-off points were established at each stage using Gaussian mixture modeling.
Alzheimers Dement
December 2024
UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK.
Background: Connectome-based models of disease propagation are used to probe mechanisms of pathology spread in neurodegenerative disease. We present our network spreading model toolbox that allows the user to compare model fits across different models and parameters. We apply the toolbox to assess whether local amyloid levels affect production of pathological tau.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea, Republic of (South).
Background: We aimed to validate the ability of Alzheimer's disease plasma biomarkers to predict clinical diagnosis and the ability to predict image-based A/T/N biomarkers positivity in Asian population. We also sought to define the optimal cut point for each biomarker.
Method: 232 participants were enrolled, all of whom underwent F-florbetaben (FBB), F-flortaucipir (FTP) PET, volumetric MRI, and neuropsychological assessments.
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