INTERNET OF MEDICAL THINGS (IOMT) ENABLED THIRD-PARTY MONITORING MODEL FOR INFECTIOUS DISEASES CONTROL DURING EPIDEMICS

Authors

DOI:

https://doi.org/10.4314/njt.v43i2.18

Keywords:

Remote monitoring, Epidemic Response, Third-Party Monitoring, Internet-of-Medical Things (IoMT),, Infectious Disease Control, Smart health, Vector Monitoring

Abstract

Infectious diseases pose a very significant threat to development of the society and the world at large. With several outbreaks of diseases like Monkeypox, Lassa fever, SARS, COVID-19, etc, the global economy was grossly affected. The rate of transfer and mortality associated with similar outbreaks is alarming. This research presents a novel approach utilizing the Internet of Medical Things (IoMT) to develop a third-party notification model. This model uses IoMT's ubiquitous connectivity to notify even ordinary individuals of the presence of an infectious disease vector within a specified range. A four-tier architecture, including cloud and web API blocks, healthcare provider management, IoT sensory, and notification blocks forms the bedrock of the model. The research focuses on developing a Location Tracking Device (LTD) prototype that incorporates the Haversine formula for real-time distance calculation between individuals performed at the edge using the location data supplied by the LTDs as input parameters. The optimization of data reception rates was based on the average human walking speed in order to enhance response time of the system. Results from testing the prototype demonstrate an average of 4.68s response delay which corresponds to an offset of about 6.85m from the real vector distance calculation. The research implementation challenges include the internet connection speed, network availability, and topography. Despite these challenges, the IoMT-enabled model introduces a promising approach to infectious disease-carrier monitoring, integrating personalized carrier/vector-presence awareness with associated risks within the disease control ecosystem. Hence, every user can use the LTD during an epidemic to help track the user’s nearness to a symptomatic person thereby helping to control the spread of infectious diseases during epidemics.

Author Biographies

  • C. O. Ikerionwu, Department of Software Engineering, Federal University of Technology, Owerri, Nigeria

    Dr. Charles Ikerionwu is a Senior Software Engineer, and Researcher at the School of Computing and Information Technology, Federal University of Technology, Owerri, Nigeria. Currently, the Head, Department of Software Engineering. He holds a PhD in Software Engineering from Glasgow Caledonian 
    University, Scotland, UK; BSc, MSc(IT), MCA from Panjab University, and Punjab Technical University respectively, India. Charles is an astute National Information Technology Development Agency (NITDA) Scholar. He has won several research grants from TetFund NRF, NCC, and NITDA focusing on Artificial Intelligence and software solutions. His current research interests include but not limited to Software 
    process improvement, Artificial Intelligence, and Data Science. 

  • Y. U. Mshelia, Department of Software Engineering , Nile University of Nigeria, Federal Capital Territory, Abuja, Nigeria

    Yusuf U. Mshelia is a proficient academic and researcher with expertise in Software Engineering. He currently serves as a Lecturer in the Software Engineering Department at the Nile University of Nigeria
    and is also actively involved in innovative research in the Department of Computer Engineering at the University of Benin (UNIBEN). While he’s a PhD candidate of Computer Engineering with speciality 
    in Software Engineering at the University of Benin, he earned his Master’s degree in Computer Engineering at the same 
    institution and a Bachelor’s degree in Software Engineering. 
    He’s a member of the PILAB Project, Data Aid Project and 
    Non-Digital-Native (NDN) Consortium.

  • F. O. Elei, Department of Software Engineering, Federal University of Technology, Owerri, Nigeria

    Engr. Dr. Florence O. Elei is a lecturer in the department of software Engineering at Federal University of Technology Owerri (FUTO) Nigeria. She earned her bachelor's and Master’s degree in Electrical and Electronic Engineering, a Master of Science (MSc.) and a PhD in Computer Science. She is a professional 
    engineer and a member of the AI and IoT research group.Her main research work focuses on Data Mining, IoT, Communication Technology and Computational Intelligence. She has over 15 years of combined teaching & research experience.

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Published

2024-06-12

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Section

Computer, Telecommunications, Software, Electrical & Electronics Engineering

How to Cite

INTERNET OF MEDICAL THINGS (IOMT) ENABLED THIRD-PARTY MONITORING MODEL FOR INFECTIOUS DISEASES CONTROL DURING EPIDEMICS. (2024). Nigerian Journal of Technology, 43(2). https://doi.org/10.4314/njt.v43i2.18