Analysis of a Stochastic COVID-19 and Hepatitis B Coinfection Model with Brownian and Levy Noise
POBBI, M. A.; MOORE, S. E.; NAANDAM, S. M.
Show abstract
In this article, we formulate and analyse a mathematical model for the co-infection of Hepatitis B virus and COVID-19. We incorporate into our framework Hepatitis B virus prevention, COVID-19 prevention, COVID-19 vaccination, and environmental factors so as to investigate their effect on transmission dynamics. First, we derive the basic reproduction number for HBV only, COVID-19 only, and co-infection stochastic models using the next generation matrix method. Next, we establish the conditions for stability in the stochastic sense for HBV only, COVID-19 only sub-models, and the co-infection model. Furthermore, we devote our attention to finding sufficient conditions for extinction and persistence. Finally, by using the Euler-Murayama scheme, we illustrate the dynamics of the co-infection, COVID-19, HBV and the effect of some parameters on disease transmission dynamics by means of numerical simulations.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Global analysis of a cancer model with drug resistance due to Lamarckian induction and microvesicle transfer 98%
- Population dynamics of multi-host communities attacked by a common parasitoid 96%
- Bifurcation and sensitivity analysis reveal key drivers of multistability in a model of macrophage polarization. 96%
Similar papers in this journal
- Estimation and optimal control of the multi-scale dynamics of the Covid-19 98%
- Stochastic forecasting of COVID-19 daily new cases across countries with a novel hybrid time series model 96%
- Analog of the Hutchinson equation in biophysical neurodynamics: from the Morris-Lecar model to a delay differential equation 96%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.