Predicting Zoonotic Spillover Events: A Fractional Modeling Framework for Nipah Virus Dynamics

Abstract

The Nipah virus, a highly virulent zoonotic pathogen, has caused recurrent outbreaks in South and Southeast Asia, presenting significant public health challenges. Traditional integer-order models often fail to capture the complex dynamics of zoonotic spillover events, which are influenced by memory effects, environmental factors, and heterogeneous interspecies interactions. In this study, we introduce a novel fractional-order mathematical model to describe the transmission dynamics of the Nipah virus, focusing on spillover events from bat reservoirs to humans and subsequent human-to-human transmission. By incorporating the Caputo fractional derivative, our model accounts for memory effects and longrange dependencies, offering a more accurate representation of disease progression. We establish the existence and uniqueness of solutions, conduct stability analysis, and employ an advanced fractional-order numerical scheme to solve the model. Validation using real-world outbreak data demonstrates the model’s superior predictive accuracy compared to classical integer-order approaches. Our findings highlight the crucial role of spillover rates and environmental factors in outbreak dynamics and suggest that targeted interventions, such as early detection and control strategies, can mitigate epidemic risks. This study advances mathematical epidemiology by providing a robust framework for predicting and managing zoonotic spillover events, with potential applications to other emerging infectious diseases.