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The novel coronavirus infectious disease (or COVID-19) almost spread widely around the world and causes a huge panic in the human population. To explore the complex dynamics of this novel infection, several mathematical epidemic models have been adopted and simulated using the statistical data of COVID-19 in various regions. In this paper, we present a new nonlinear fractional order model in the Caputo sense to analyze and simulate the dynamics of this viral disease with a case study of Algeria. Initially, after the model formulation, we utilize the well-known least square approach to estimate the model parameters from the reported COVID-19 cases in Algeria for a selected period of time. We perform the existence and uniqueness of the model solution which are proved via the Picard-Lindelöf method. We further compute the basic reproduction numbers and equilibrium points, then we explore the local and global stability of both the disease-free equilibrium point and the endemic equilibrium point. Finally, numerical results and graphical simulation are given to demonstrate the impact of various model parameters and fractional order on the disease dynamics and control.
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http://dx.doi.org/10.1016/j.rinp.2021.104324 | DOI Listing |
Biosystems
September 2025
IVIRMA Global Research Alliance, The Health Research Institute La Fe (IIS La Fe), Edificio Biopolo. Av. Fernando Abril Martorell, 106 - Torre A, Planta 1, Valencia, 46026, Spain; Rey Juan Carlos University, Department of Medical Specialties and Public Health, Edificio Departamental II. Av. de Atenas
Cellular aging associated with telomeric shortening plays an important role in female fertility. In addition to natural decline, due to the loss of telomeric repeats during cell division, other factors such oxidative stress (OS), accelerate telomere shortening by causing a dramatic loss of telomeric repeats. Thus, mathematical models to better understand the accelerated aging leading to infertility are lacking in the literature.
View Article and Find Full Text PDFbioRxiv
August 2025
Department of Medicine, Division of Allergy and Clinical Immunology, Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA, Broad Institute of MIT, and Harvard, Cambridge, MA 02139, USA, Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA 02139, USA.
A key goal of vaccinology is to train the immune system to combat current pathogens while simultaneously preparing it for future evolved variants. Understanding factors contributing to anticipatory breadth, wherein affinity maturation against an ancestral strain yields neutralization capacity against evolved variants, is therefore of great importance. Here, we investigated the mechanism of anticipatory breadth development in a public antibody family targeting the functionally restricted ACE2 binding site on SARS-CoV-2.
View Article and Find Full Text PDFJ Pathol Inform
August 2025
Information Systems Institute, University of Applied Sciences Western Switzerland (HES-SO Valais), Sierre, Switzerland.
The increasing availability of biomedical data is helping to design more robust deep learning (DL) algorithms to analyze biomedical samples. Currently, one of the main limitations to training DL algorithms to perform a specific task is the need for medical experts to manually label the data. Automatic methods to label data exist; however, automatic labels can be noisy, and it is not completely clear in which situations they can be used to train DL models.
View Article and Find Full Text PDFPLOS Glob Public Health
September 2025
Centre for Infectious Disease Control, National Institute of Public Health and the Environment (RIVM), Bilthoven, the Netherlands.
The global population of older adults is growing. Older adults are more vulnerable to infectious diseases compared to younger adults. Vaccines are available to protect older adults against several infectious diseases, yet their uptake remains sub-optimal.
View Article and Find Full Text PDFSci Rep
August 2025
Department of Mathematics and Statistics, King Faisal University, 31982, Hofuf, Saudi Arabia.
This paper presents a comprehensive study of poliomyelitis transmission dynamics using two fractional-order models that incorporate the Atangana--Baleanu derivatives in the Caputo sense (ABC). The model includes critical epidemiological features, including vaccination and a post-paralytic population class. By utilizing the Mittag-Leffler kernel, the fractional framework captures memory and hereditary properties in disease progression.
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