Abstract:
This study introduces an epidemic model with a Beddington–DeAngelis-type incidence rate and Holling type II treatment rate. The Bedding-
ton–DeAngelis incidence rate is used to evaluate the effectiveness of inhibitory measures implemented by susceptible and infected individuals.
Moreover, the choice of Holling type II treatment rate in our model aims to assess the impact of limited treatment facilities in the context of
disease outbreaks. First, the well-posed nature of the model is analyzed, and then, we further investigated the local and global stability analysis
along with bifurcation of co-dimensions 1 (transcritical, Hopf, saddle-node) and 2 (Bogdanov–Takens, generalized Hopf) for the system.
Moreover, we incorporate a time-delayed model to investigate the effect of incubation delay on disease transmission. We provide a rigorous
demonstration of the existence of chaos and establish the conditions that lead to chaotic dynamics and chaos control. Additionally, sensitivity
analysis is performed using partial rank correlation coefficient and extended Fourier amplitude sensitivity test methods. Furthermore, we
delve into optimal control strategies using Pontryagin’s maximum principle and assess the influence of delays in state and control parame-
ters on model dynamics. Again, a stochastic epidemic model is formulated and analyzed using a continuous-time Markov chain model for
infectious propagation. Analytical estimation of the likelihood of disease extinction and the occurrence of an epidemic is conducted using the
branching process approximation. The spatial system presents a comprehensive stability analysis and yielding criteria for Turing instability.
Moreover, we have generated the noise-induced pattern to assess the effect of white noise in the populations. Additionally, a case study has
been conducted to estimate the model parameters, utilizing COVID-19 data from Poland and HIV/AIDS data from India. Finally, all theo-
retical results are validated through numerical simulations. This article extensively explores various modeling techniques, like deterministic,
stochastic, statistical, pattern formation(noise-induced), model fitting, and other modeling perspectives, highlighting the significance of the
inhibitory effects exerted by susceptible and infected populations.