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February 11, 20260 citations

Mathematical Modeling of Malaria and Typhoid Co-infection: Exploring Vector and Non-Vector Transmission Dynamics

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QAQueeneth Ojoma AhmanMOMary Bassey OkofuCEChioma Lydia Ejikeme

Key Points

  • To explore the dynamics of malaria and typhoid co-infection through mathematical modeling.
  • Developed a mathematical model for co-infection dynamics.
  • Incorporated vector and non-vector transmission routes.
  • Simulated the model parameters using MATLAB.
  • Vector-borne transmission accounts for over 80% of malaria infections.
  • Co-infected individuals make up 25-35% of total infections at peak conditions.
  • Sensitivity analysis identified transmission rates as key drivers of co-infection prevalence.

Abstract

Malaria and typhoid fever are major infectious diseases that pose significant public health challenges in many parts of the world, particularly in sub-Saharan Africa. This study develops a mathematical model to investigate the dynamics of malaria–typhoid co-infection, incorporating both vector and non-vector malaria transmission routes and environmental transmission for typhoid. Model parameters were drawn from the literature, and simulations were conducted in MATLAB. The results show that vector-borne transmission accounts for over 80% of malaria infections, while typhoid transmission sustains a persistent infection level. Co-infected individuals constitute approximately 25–35% of the total infected population at peak conditions, underscoring the substantial burden of simultaneous infection. Sensitivity analysis identifies malaria and typhoid transmission rates as key drivers of co-infection prevalence. A backward bifurcation in the malaria subsystem indicates that malaria may persist even when its reproduction number is below one, thereby continually seeding co-infection and indirectly maintaining typhoid transmission through co-infected individuals. These findings highlight the need for integrated and sustained control strategies, including vector control, typhoid vaccination, and improved sanitation, to effectively reduce the dual disease burden. Overall, the model provides a useful analytical framework to support public health planning and evidence-based resource allocation in regions where both infections remain endemic.

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Cite This Study

Ahman et al. (2026) studied this question.

synapsesocial.com/papers/698c1bdc267fb587c655dd52https://doi.org/10.1051/mmnp/2026007/pdf
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