A Novel Stochastic Fuzzy Epidemic Model With Cognitive Awareness and Reactive Dynamics

Abstract

According to the World Health Organization (WHO), infectious diseases are responsible for more than 13 million deaths worldwide each year. Modeling the spread of disease and awareness in a network of individuals is a challenging task that requires accounting for the complex dynamics and interactions of human states and behaviors. A majority of research works in epidemic modeling rely on compartmental models that divide the population into discrete compartments, whereas these models oversimplify the complex dynamics of disease spread. Additionally, traditional models often do not account for the role of dynamic awareness in disease prevention and control, or for the complex interactions between disease and awareness. This article proposes a new general epidemic model that addresses some limitations of the previous models by considering variable values in a continuous state space (CSS). This allows for more realistic and flexible representation of human states and disease transitions. By taking advantage of fuzzy logic, our model addresses the uncertainty and variability of human behavior (as awareness) and reactions to disease propagation (as infection). We also incorporate age-tuned learning and disease dynamics into our model, using Covasim parameters and empirical datasets, capturing age-specific behavioral and epidemiological patterns. The proposed model offers several advantages over traditional compartmental models by providing a more detailed and nuanced representation of the spread of disease and awareness. Also, our age-stratified model adjusts infection and learning rates by age, individual, and neighbor states, capturing nonlinear and stochastic disease and awareness dynamics.