Chinebu, T. I., Okafor, K. C., Anoh, K., Uzoeto, H. O., Apeh, V. O., Okafor, I. P., Adebisi, B. and Okoronkwo, C. A. (2024) Smart waterborne disease control for a scalable population using biodynamic model in IoT network. Computers in Biology and Medicine, 181. p. 109034. ISSN 0010-4825
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Abstract
We propose a biodynamic model for managing waterborne diseases over an Internet of Things (IoT) network, leveraging the scalability of LoRa IoT technology to accommodate a growing human population. The model, based on fractional order derivatives (FOD), enables smart prediction and control of pathogens that cause waterborne diseases using IoT infrastructure. The human-pathogen-based biodynamic FOD model utilises epidemic parameters (SVIRT: susceptibility, vaccination, infection, recovery, and treatment) transmitted over the IoT network to predict pathogenic contamination in water reservoirs and dumpsites in Iji-Nike, Enugu, the study community in Nigeria. These pathogens contribute to person-to-person, water-to-person, and dumpsite-to-person transmission of disease vectors. Five control measures are proposed: potable water supply, treatment, vaccination, adequate sanitation, and health education campaigns. A stable disease-free equilibrium point is found when the effective reproduction number of the pathogens, R <1 and unstable if R >1. While other studies showed a 98.2% reduction in infections when using IoT alone, this paper demonstrates that combining the SVIRT epidemic control parameters (such as potable water supply and health education campaign) with IoT achieves a 99.89% reduction in infected human populations and a 99.56% reduction in pathogen populations in water reservoirs. Furthermore, integrating treatment with sanitation results in a 99.97% reduction in infected populations. Finally, combining these five control strategies nearly eliminates infection and pathogen populations, demonstrating the effectiveness of multifaceted approaches in public health and environmental management. This study provides a blueprint for governments to plan sustainable smart cities for a growing population, ensuring potable water free from pathogenic contamination, in line with the United Nations Sustainable Development Goals #6 (Clean Water and Sanitation) and #11 (Sustainable Cities and Communities).
Publication Type: | Articles |
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Additional Information: | © 2024 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies. |
Uncontrolled Keywords: | Biodynamic model, IoT network, Optimal control, Waterborne diseases, Nigeria - epidemiology, Humans, Models, Biological, Waterborne Diseases - prevention & control - epidemiology, Fractional order derivative, Stability, Smart control, Scalable population, Internet of Things |
Subjects: | R Medicine > RA Public aspects of medicine R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine T Technology > T Technology (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering |
Divisions: | Academic Areas > Department of Engineering, Computing and Design Research Entities > Centre for Future Technologies |
SWORD Depositor: | Publications Router Jisc |
Depositing User: | Publications Router Jisc |
Date Deposited: | 03 Jun 2025 08:32 |
Last Modified: | 30 Aug 2025 01:10 |
URI: | https://eprints.chi.ac.uk/id/eprint/7729 |