Modeling the impact of food insecurity on the transmission dynamics of monkeypox using three different fractional operators

Abstract

The global emergence of Monkeypox presents significant public health challenges, particularly in regions with varying levels of food insecurity. This study develops and analyzes a novel mathematical model examining the dynamics of Monkeypox transmission across food-secure and food-insecure human populations, while incorporating animal reservoirs. We investigate the system through three distinct arbitrary-order derivative operators: the Caputo derivative with power law, the Caputo-Fabrizio derivative with non-singular kernel, and the Atangana-Baleanu derivative incorporating the Mittag-Leffler function. The model explicitly considers the impact of food insecurity status on disease transmission rates, recovery patterns, and intervention effectiveness. Through numerical simulations, we demonstrate that food-insecure populations experience significantly higher infection peaks (approximately 12.5 million cases) compared to food-secure populations (approximately 9.5 million cases). Our analysis reveals how varying fractional orders (θ = 0.95, 0.85, and 0.75) influence the temporal effects and overall disease dynamics. The model parameters, estimated from current epidemiological data and literature, provide insights into the critical role of food insecurity in disease mitigation. Surface plots analyzing the basic reproduction number R0 against various parameters demonstrate the sensitivity of disease spread to contact rates, recovery rates, and food insecurity status. These findings emphasize the importance of integrating food security measures into public health interventions for effective Monkeypox control, particularly in vulnerable populations.