Evidence map›Paper›PMID 39019935›Full record

ArticleScientific reports2024

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

Muhammad Farman, Ali Akgül, Muhammad Sultan, Sidra Riaz, Hira Asif, Praveen Agarwal, Murad Khan Hassani

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Muhammad FarmanMathematics Research Center, Department of Mathematics, Near East University, Near East Boulevard, 99138, Nicosia, Mersin 10, Turkey.
Ali AkgülDepartment of Computer Science and Mathematics, Lebanese American University, Beirut, Lebanon. aliakgul00727@gmail.com.
Muhammad SultanData Analytics, Department of Design and Visual Arts, Computer Studies, Georgian College, Barrie, Canada.
Sidra RiazMathematics Research Center, Department of Mathematics, Near East University, Near East Boulevard, 99138, Nicosia, Mersin 10, Turkey.
Hira AsifMathematics Research Center, Department of Mathematics, Near East University, Near East Boulevard, 99138, Nicosia, Mersin 10, Turkey.
Praveen AgarwalDepartment of Mathematics, Anand International College of Engineering, Jaipur, India.
Murad Khan HassaniDepartment of Mathematics, Ghazni University, Ghazni, Afghanistan. mhassani@gu.edu.af.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

CoinfectionCOVID-19Diabetes MellitusFractalsSARS-CoV-2Computer SimulationHumansCOVID-19DiabetesFractal–fractional operatorMittg-Leffler kernel

Identifiers

PMID39019935
PMCPMC11254938

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.