Fractional-order dynamics of Lassa fever: a Caputo–Fabrizio modelling framework with real statistical data

In this work, a fractional-order mathematical model with hospitalization, developed and studied within the Caputo–Fabrizio framework, is used to capture the transmission dynamics of Lassa fever. Given the periodic occurrences of Lassa fever and the known inability of integer-order models to account for long-range dependencies and memory effects, a new mathematical model is proposed to describe the dynamic interplay between the human and rodent populations. Taking into consideration the various stages of Lassa fever infection, the model captures human susceptibility, exposure, infectiousness, hospitalization, and recovery, along with rodent infection dynamics. Analytically, the existence and uniqueness of solutions are established via fixed-point iteration, and the equilibrium points together with the basic reproduction number \(R_{0}\) —further decomposed into rodent and human transmission components—are determined. Using weekly Lassa fever incidence data of the Nigeria Centre for Disease Control (NCDC), selected epidemiological parameters are estimated, showing a very good match between the simulated and reported incidence through a constrained least-squares approach. A detailed sensitivity study, including PRCC analysis, reveals rodent-to-rodent, rodent-to-human, and human-to-human transmission to be the most prominent drivers of Lassa fever incidence. The analysis further shows that hospitalization and recovery processes influence the progression of Lassa fever incidence, while the time-fractional parameter α significantly delays and suppresses incidence when \(0<\alpha <1\) . Numerical simulations additionally reveal the importance of Lassa fever control measures, which substantially reduce both disease transmission and the corresponding rodent population. Overall, the analysis demonstrates the usefulness and viability of the developed Caputo–Fabrizio mathematical model for exploring Lassa fever dynamics and highlights the support such models provide in designing strategies to control and minimize Lassa fever incidence.