A Data-Driven Caputo Fractional-Order Model for the Environmental Co-Transmission Dynamics of Salmonella Typhi and Salmonella Paratyphi Causing Typhoid Fever

Thadei Damas Sagamiko and James Philbert Mpele

Abstract and Applied Analysis, 2026, vol. 2026, 1-20

Abstract: Typhoid fever remains a major public health concern in many low- and middle-income countries, where the co-circulation of Salmonella Typhi and Salmonella Paratyphi, coupled with environmental contamination, sustains disease transmission. This study develops a data-driven Caputo fractional-order mathematical model to investigate the environmental co-transmission dynamics of these two pathogens while accounting for memory effects inherent in disease progression and environmental persistence. Model parameters are estimated by fitting the fractional-order system to surveillance data using nonlinear least-squares estimation. Analytical results establish the disease-free equilibrium, derive the effective reproduction number, and prove the local asymptotic and Hyers–Ulam stability of the proposed fractional-order system. Numerical simulations demonstrate excellent agreement between the fitted model and observed Typhi and Paratyphi incidence data, confirming the capability of the proposed framework to reproduce the observed epidemiological trends. Comparative simulations further show that co-transmission substantially increases the infectious burden and environmental contamination compared with single-pathogen transmission. Sensitivity analyses of intervention strategies reveal that strengthening preventive measures, improving treatment, and enhancing environmental decontamination substantially reduce disease burden and environmental pathogen concentrations, with combined interventions providing the greatest epidemiological benefit. The estimated fractional order indicates that memory effects play an important role in describing typhoid transmission dynamics. The proposed framework provides a realistic and data-driven tool for understanding environmental typhoid transmission and supports evidence-based planning of integrated public health interventions in endemic settings.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlaaa:1044511

DOI: 10.1155/aaa/1044511

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