Applied Probability Insights into Nonlinear Epidemic Dynamics with Independent Jumps

TL;DR

This paper develops a stochastic epidemic model incorporating asymptomatic transmission, vaccination, and environmental randomness, providing theoretical conditions for disease extinction or persistence and validating findings through numerical simulations.

Contribution

It introduces a novel stochastic SAIRS-type model with jumps, analyzing the effects of randomness and asymptomatic carriers on epidemic dynamics.

Findings

Conditions for disease extinction and persistence derived

Random perturbations significantly influence disease outcomes

Numerical simulations confirm theoretical results

Abstract

This paper focuses on the analysis of a stochastic SAIRS-type epidemic model that explicitly incorporates the roles of asymptomatic and symptomatic infectious individuals in disease transmission dynamics. Asymptomatic carriers, often undetected due to the lack of symptoms, play a crucial role in the spread of many communicable diseases, including COVID-19. Our model also accounts for vaccination and considers the stochastic effects of environmental and population-level randomness using L\'evy processes. We begin by demonstrating the existence and uniqueness of a global positive solution to the proposed stochastic system, ensuring the model's mathematical validity. Subsequently, we derive sufficient conditions under which the disease either becomes extinct or persists over time, depending on the parameters and initial conditions. The analysis highlights the influence of random perturbations, asymptomatic transmission, and vaccination strategies on disease dynamics. Finally, we conduct comprehensive numerical simulations to validate the theoretical findings and illustrate the behavior of the model under various scenarios of randomness and parameter settings. These results provide valuable insights into the stochastic dynamics of epidemic outbreaks and inform strategies for disease management and control.