MODELING THE TRANSMISSION DYNAMICS OF ANTHRAX DISEASE USING FRACTIONAL ORDER COMPARTMENTAL MODEL AND PHYSICS-INFORMED NEURAL NETWORKS

Abstract

Background: This research investigates the transmission dynamics of Anthrax, a zoonotic infectious disease caused by Bacillus anthracis bacteria, with implications for both human and livestock populations.

Methods: It employs a novel integration of mathematical modeling and physics-informed neural networks; the study provides a comprehensive analysis of Anthrax spread dynamics. Fractional differential equations are formulated within the model to capture the intricate interactions governing disease transmission, considering both quantitative and qualitative aspects. Special attention is given to the examination of steady-state solutions, particularly the local asymptotic stability of the disease-free equilibrium and its associated epidemic basic reproduction number.

Results: The analysis suggests that the model performs well in predicting variables H3 (Recovered humans), V1 (Susceptible livestock), V3 (Vaccinated livestock), and V4 (Recovered livestock), while variables H2 (Infected humans) and V2 (Infected livestock) may require further investigation or model improvement to enhance predictive performance.

Conclusion: This study contributes to advancing our understanding of Anthrax transmission dynamics and underscores the importance of interdisciplinary approaches in addressing infectious disease spread. The insights gained have significant implications for public health strategies aimed at Anthrax prevention and control.