IoT-Enabled Biodynamic Surveillance for Multi-Route Disease Risk Management in Environmental Reservoirs and Dumpsites

Building on previous work in IoT-enabled disease monitoring, this study develops a biodynamic model of waterborne disease tailored for African settings, leveraging AI and IoT networks to support scalable population health management. The framework is entirely analytical and simulation-based, without hardware-in-loop implementation. Our approach involved enabling real-time monitoring of pathogen levels in water reservoirs and informal dumpsites sources including person-to-person, water-to-person, and environment-to-person transmission in rural and urban communities. Using a Caputo fractional-order approach, the model characterises the dynamics of pathogen concentrations and disease spread, while deriving optimal control strategies via Pontryagin’s maximum principle to reduce infections and environmental pathogen loads. Analytical results establish positivity and boundedness of solutions, computation of the effective reproduction number ( R 0 eff ), and stability of disease-free equilibria ( R 0 eff 0 < 1 stable; R 0 eff 0 > 1 unstable). Numerical simulations evaluate the effects of fractional-order dynamics and intervention strategies, showing that integrated measures—including potable water provision, improved sanitation, health education, vaccination, and treatment—are most effective in mitigating disease incidence. This framework provides actionable insights for local governments and public health planners, supporting scalable, data-driven strategies for clean water provision and disease prevention in resource-constrained African communities.