4.7 Article

Modeling, analysis and prediction of new variants of covid-19 and dengue co-infection on complex network

Journal

CHAOS SOLITONS & FRACTALS
Volume 150, Issue -, Pages -

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.chaos.2021.111008

Keywords

Dengue; Covid-19; Stability analysis; Optimization; Predictor-corrector scheme

Funding

  1. SERB [TAR/2018/000001]
  2. DST [DST/INT/DAAD/P21/2019, INT/RUS/RFBR/308]
  3. NBHM (DAE) [02011/12/2020 NBHM (R.P)/RD II/7867]

Ask authors/readers for more resources

This paper presents hybrid mathematical models of four new strains of SARS-COV-2 and co-infection to evaluate and predict the transmission dynamics of both deadly viruses. Stability analysis, numerical methods, and sensitivity analysis are used to assess the effects of various biological parameters on the dynamics of the viruses, comparing results with reported data.
Recently, four new strains of SARS-COV-2 were reported in different countries which are mutants and considered as 70% more dangerous than the existing covid-19 virus. In this paper, hybrid mathematical models of new strains and co-infection in Caputo, Caputo-Fabrizio, and Atangana-Baleanu are presented. The idea behind this co-infection modeling is that, as per medical reports, both dengue and covid-19 have similar symptoms at the early stages. Our aim is to evaluate and predict the transmission dynamics of both deadly viruses. The qualitative study via stability analysis is discussed at equilibria and reproduction number R-0 is computed. For the numerical purpose, Adams-Bashforth-Moulton and Newton methods are employed to obtain the approximate solutions of the proposed model. Sensitivity analysis is carried out to assessed the effects of various biological parameters and rates of transmission on the dynamics of both viruses. We also compared our results with some reported data against infected, recovered, and death cases. (C) 2021 Elsevier Ltd. All rights reserved.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available