Numerical study and dynamics analysis of diabetes mellitus with co-infection of COVID-19 virus by using fractal fractional operator.

Numerical study and dynamics analysis of diabetes mellitus with co-infection of COVID-19 virus by using fractal fractional operator.

Publication date: Jul 17, 2024

COVID-19 is linked to diabetes, increasing the likelihood and severity of outcomes due to hyperglycemia, immune system impairment, vascular problems, and comorbidities like hypertension, obesity, and cardiovascular disease, which can lead to catastrophic outcomes. The study presents a novel COVID-19 management approach for diabetic patients using a fractal fractional operator and Mittag-Leffler kernel. It uses the Lipschitz criterion and linear growth to identify the solution singularity and analyzes the global derivative impact, confirming unique solutions and demonstrating the bounded nature of the proposed system. The study examines the impact of COVID-19 on individuals with diabetes, using global stability analysis and quantitative examination of equilibrium states. Sensitivity analysis is conducted using reproductive numbers to determine the disease’s status in society and the impact of control strategies, highlighting the importance of understanding epidemic problems and their properties. This study uses two-step Lagrange polynomial to analyze the impact of the fractional operator on a proposed model. Numerical simulations using MATLAB validate the effects of COVID-19 on diabetic patients and allow predictions based on the established theoretical framework, supporting the theoretical findings. This study will help to observe and understand how COVID-19 affects people with diabetes. This will help with control plans in the future to lessen the effects of COVID-19.

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Concepts Keywords
Diabetes Coinfection
Future Computer Simulation
Hyperglycemia COVID-19
Mittag COVID-19
Polynomial Diabetes
Diabetes Mellitus
Fractal–fractional operator
Fractals
Humans
Mittg-Leffler kernel
SARS-CoV-2

Semantics

Type Source Name
disease MESH diabetes mellitus
disease MESH co-infection
disease MESH COVID-19
disease MESH hyperglycemia
pathway REACTOME Immune System
disease MESH hypertension
disease MESH obesity
disease MESH cardiovascular disease
disease MESH Long Covid
drug DRUGBANK Coenzyme M
disease MESH infection
disease MESH death
disease MESH interstitial lung disease
disease MESH comorbidity
disease VO Canada
drug DRUGBANK Abacavir
disease MESH hearing loss
disease MESH mumps
disease MESH influenza
disease MESH polio
disease MESH measles
pathway KEGG Measles
disease VO vaccination
disease VO population
disease IDO susceptible population

Original Article

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