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Smart waterborne disease control for a scalable population using biodynamic model in IoT network.
Chinebu, Titus I; Okafor, Kennedy Chinedu; Anoh, Kelvin; Uzoeto, Henrietta O; Apeh, Victor O; Okafor, Ijeoma P; Adebisi, Bamidele; Okoronkwo, Chukwunenye A.
Affiliation
  • Chinebu TI; Department of Applied Sciences and Dental Therapy, Federal University of Allied Health, Trans - Ekulu, Enugu, Nigeria. Electronic address: titusifeanyi5432@gmail.com.
  • Okafor KC; Department of Mechatronics Engineering, Federal University of Technology, Owerri, Nigeria; Department of Engineering, Manchester Metropolitan University, M1 5GD Manchester, UK; School of Engineering, University of Chichester, Bognor Regis, PO21 1HR, UK; Department of Electrical and Electronic Engine
  • Anoh K; School of Engineering, University of Chichester, Bognor Regis, PO21 1HR, UK. Electronic address: k.anoh@chi.ac.uk.
  • Uzoeto HO; Department of Applied Sciences and Dental Therapy, Federal University of Allied Health, Trans - Ekulu, Enugu, Nigeria.
  • Apeh VO; Department of Applied Sciences and Dental Therapy, Federal University of Allied Health, Trans - Ekulu, Enugu, Nigeria.
  • Okafor IP; Department of Public Health, Cardiff Metropolitan University, Llandaff Campus, Western Avenue, Cardiff, CF5 2YB, UK. Electronic address: ijpeacex@gmail.com.
  • Adebisi B; Department of Engineering, Manchester Metropolitan University, M1 5GD Manchester, UK. Electronic address: b.adebisi@mmu.ac.uk.
  • Okoronkwo CA; Department of Mechatronics Engineering, Federal University of Technology, Owerri, Nigeria. Electronic address: chukwunenye.okoronkwo@futo.edu.ng.
Comput Biol Med ; 181: 109034, 2024 Oct.
Article in En | MEDLINE | ID: mdl-39217966
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, R0eff<1 and unstable if R0eff>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).
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Waterborne Diseases Limits: Humans Country/Region as subject: Africa Language: En Journal: Comput Biol Med Year: 2024 Document type: Article Country of publication:

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Waterborne Diseases Limits: Humans Country/Region as subject: Africa Language: En Journal: Comput Biol Med Year: 2024 Document type: Article Country of publication: