Dynamical behavior of a time-delayed infectious disease model with a non-linear incidence function under the effect of vaccination and treatment

TL;DR

This paper develops a new time-delayed infectious disease model with non-linear incidence, vaccination, and treatment functions, analyzing stability, and validating findings with COVID-19 data to inform disease control strategies.

Contribution

It introduces a novel compartmental model incorporating non-linear incidence, vaccination, and treatment delays, with comprehensive stability analysis and real data validation.

Findings

Basic reproduction number computed and analyzed.

Delay-independent global stability established.

Numerical simulations confirm model's relevance to COVID-19 data.

Abstract

When an infectious disease propagates throughout society, the incidence function may rise at first due to an increase in pathogenicity and then decrease due to inhibitory effects until it reaches saturation. Effective vaccination and treatment are very helpful for controlling the effects of such infectious diseases. To analyze the impacts of these diseases, we proposed a new compartmental model with a generalized non-linear incidence function, vaccination function, and treatment function, along with time delays in the respective functions, which show how its monotonic features influence the stability of the model. Fundamental properties of a model, such as positivity, boundedness, and the existence of equilibria, are examined in this work. The basic reproduction number has been computed, and correlative studies for local stability in view of the basic reproduction number have been examined at the disease-free and endemic equilibrium points. A delay-independent global stability result has been established, and to be more precise, we explicitly derived the result on global stability by restricting delay parameters within a very specific range. Furthermore, numerical simulations and some examples based on COVID-19 real-time data are pointed out to emphasize the significance of how the disease's dynamical behavior is characterized by various functions for controlling the spread of disease in a population and to justify the mathematical conclusions.