TY - JOUR
T1 - Smart waterborne disease control for a scalable population using biodynamic model in IoT network
AU - Chinebu, Titus I
AU - Okafor, Kennedy Chinedu
AU - Anoh, Kelvin
AU - Uzoeto, Henrietta O
AU - Apeh, Victor O
AU - Okafor, Ijeoma P
AU - Adebisi, Bamidele
AU - Okoronkwo, Chukwunenye A
N1 - Copyright © 2024 Elsevier Ltd. All rights reserved.
PY - 2024/8/31
Y1 - 2024/8/31
N2 - 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
0
eff<1 and unstable if R
0
eff>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).
AB - 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
0
eff<1 and unstable if R
0
eff>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).
KW - Biodynamic model
KW - IoT network
KW - Optimal control
KW - Waterborne diseases
KW - Nigeria - epidemiology
KW - Humans
KW - Models, Biological
KW - Waterborne Diseases - prevention & control - epidemiology
KW - Fractional order derivative
KW - Stability
KW - Smart control
KW - Scalable population
KW - Internet of Things
UR - http://www.scopus.com/inward/record.url?scp=85202717596&partnerID=8YFLogxK
U2 - 10.1016/j.compbiomed.2024.109034
DO - 10.1016/j.compbiomed.2024.109034
M3 - Article
C2 - 39217966
SN - 1879-0534
VL - 181
SP - 109034
JO - Computers in biology and medicine
JF - Computers in biology and medicine
M1 - 109034
ER -