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

Azubuike Izuchukwu Erike; Charles O. Ikerionwu; Yusuf Umaru Mshelia; Florence O. Elei

doi:10.4314/njt.v43i2.18

Authors

A. I. Erike Department of Software Engineering, Federal University of Technology, Owerri, Nigeria
C. O. Ikerionwu Department of Software Engineering, Federal University of Technology, Owerri, Nigeria https://orcid.org/0000-0002-9946-6307
Y. U. Mshelia Department of Software Engineering , Nile University of Nigeria, Federal Capital Territory, Abuja, Nigeria https://orcid.org/0000-0001-7799-8583
F. O. Elei Department of Software Engineering, Federal University of Technology, Owerri, Nigeria https://orcid.org/0000-0001-7515-1936

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.

References

Gebreyes, W. A., et al. “The Global One Health Paradigm: Challenges and Opportunit-ies for Tackling Infectious Diseases at the Human, Animal, and Environment Interface in Low-Resource Settings”, PLoS Negl. Trop. Dis., vol. 8, no. 11, 2014, doi: 10.1371/journal .pntd.0003257.

NCDC, “Lassa fever situation report”, Niger. Cent. Dis. Control, vol. 1, no. 1, pp. 1–6, 2022.

WHO, “World health statistics 2022: monitoring health for the SDGs, sustainable development goals”, 2022.

Venkatesh, S. “NCDC Newsletter”, www.ncdc .gov.in, vol. 3, no. 4, 2015.

“Report of the WHO-China Joint Mission on Coronavirus Disease 2019 ( COVID-19 )”, vol. 2019, no. February, pp. 16–24, 2020.

Asia, S. “COVID-19 Weekly Epidemiological Update”, World Heal. Organ., no. April, 2022.

Ozili, P. K. “COVID-19 pandemic and economic crisis : the Nigerian experience and structural causes”, SSRN Electron. J. ·, no. April, 2020, doi: 10.2139/ssrn.3567419.

Inegbedion, H. “Impact of COVID-19 on economic growth in Nigeria : opinions and attitudes”, Heliyon, vol. 7, no. March, p. e06943, 2021, doi: 10.1016/j.heliyon.2021.e0 6943.

Matić, Z., and Šantak, M. “Current view on novel vaccine technologies to combat human infectious diseases”, vol. 106, no. 1. Springer Berlin Heidelberg, 2022.

Wagner, A., and Weinberger, B. “Vaccines to Prevent Infectious Diseases in the Older Population: Immunological Challenges and Future Perspectives”, Front. Immunol., vol. 11, no. April, pp. 1–20, 2020, doi: 10.3389/fimmu.2020.00717.

Ogu, R. E., Uzoechi, L. O., Mshelia, Y. U., Erike, I. A., and Okoronkwo, C. D. “Applicability of distributed IoT-powered triage units in the management of infectious diseases in developing countries: The COVID-19 case”, Proc. 2020 IEEE 2nd Int. Conf. Cyberspace, CYBER Niger. 2020, no. Decem-ber 2019, pp. 11–15, 2021, doi: 10.1109/CYBERNIGERIA51635.2021.9428853.

Kim, Y., Lim, I., Lee, J., and Lee, J. “Sensor Based Real-Time Remote Patient Monitoring System : A Study on Mobile DB Construction of Minimum Network Traffic in Use of HTML5 WebSQL”, Procedia Eng., vol. 29, pp. 2382–2387, 2012, doi: 10.1016/j.proeng. 2012.01.319.

Taiwo, O., and Ezugwu, A. E. “Smart healthcare support for remote patient monitoring during covid-19 quarantine”, Informatics Med. Unlocked, vol. 20, p. 100428, 2020, doi: 10.1016/j.imu.2020.1004 28.

Akhbarifar, S., Javadi, H. H. S., Rahmani, A. M., and Hosseinzadeh, M. “A secure remote health monitoring model for early disease diagnosis in cloud-based IoT environment”, Pers. Ubiquitous Comput., 2020, doi: 10.1007/s00779-020-01475-3.

Choyon, M. M. S., Rahman, M., Kabir, M. M., and Mridha, M. F. “IoT based Health Monitoring and Automated Predictive System to Confront COVID-19”, HONET 2020 - IEEE 17th Int. Conf. Smart Communities Improv. Qual. Life using ICT, IoT AI, pp. 189–193, 2020, doi: 10.1109/HONET50430.2020.9322 811.

Yang, T., Gentile, M., Shen, C. F., and Cheng, C. M. “Combining Point-of-Care Diagnostics and Internet of Medical Things (IoMT) to Combat the COVID-19 Pandemic”, Diagnostics, vol. 10, no. 4, pp. 4–6, 2020, doi: 10.3390/diagnostics10040224.

Meraj, M., Singh, S. P., Johri, P., and Quasim, M. T. “An investigation on infectious disease patterns using Internet of Things (IoT)”, Proc. Int. Conf. Smart Technol. Comput. Electr. Electron. ICSTCEE 2020, pp. 599–604, 2020, doi: 10.1109/ICSTCEE49637.2020.9276922.

Singh, P., and Kaur, R. “An integrated fog and Artificial Intelligence smart health framework to predict and prevent COVID-19”, Glob. Transitions, vol. 2, pp. 283–292, 2020, doi: 10.1016/j.glt.2020.11.002.

Rahman, S., et al. “Defending against the Novel Coronavirus (COVID-19) outbreak: How can the Internet of Things (IoT) help to save the world?”, Heal. Policy Technol., vol. 9, no. January, 2020.

Aman, A. H. M., Hassan, W. H., Sameen, S., Attarbashi, Z. S., Alizadeh, M., and Latiff, L. A. “IoMT amid COVID-19 pandemic: Application, architecture, technology, and security”, J. Netw. Comput. Appl., vol. 174, no. January, 2020.

Swayamsiddha, S., and Mohanty, C. “Applica-tion of cognitive Internet of Medical Things for COVID-19 pandemic”, Diabetes Metab. Syndr. Clin. Res. Rev., vol. 14, no. 5, pp. 911–915, 2020, doi: 10.1016/j.dsx.2020.06.014.

Jabbar, M. A., Kumar, S., Kumar, A., and Shandilya, S. “Applications of cognitive internet of medical things in modern healthcare”, Comput. Electr. Eng., vol. 102, no. July, p. 108276, 2022, doi: 10.1016/j.comp eleceng.2022.108276.

Roy, P. K., and Kumar, A. “Early prediction of COVID-19 using ensemble of transfer learning”, Comput. Electr. Eng., vol. 101, no. October 2021, p. 108018, 2022, doi: 10.1016/j .compeleceng.2022.108018.

Al, N., Asif, S., Al, A., Khan, J., and Sumesh, E. P. “IoT based wearable device to monitor the signs of quarantined remote patients of COVID-19”, Informatics Med. Unlocked, vol. 24, no. May, p. 100588, 2021, doi: 10.1016/j.imu.2021.100588.

Sim, S., and Cho, M. “Convergence model of AI and IoT for virus disease control system”, Pers. Ubiquitous Comput., 2021, doi: 10.1007/s00779-021-01577-6.

Saleh, N., Ayoub, M., Karamti, H., and Ahmed, N. “Internet of Things ( IoT ) enabled smart queuing model to support massive safe crowd at Ka ’ aba”, Alexandria Eng. J., vol. 61, no. 12, pp. 12713–12723, 2022, doi: 10.1016/j. aej.2022.06.053.

McCrum, C., Willems, P., Karamanidis, K., and Meijer, K. “Stability-normalised walking speed: A new approach for human gait perturbation research”, J. Biomech., vol. 87, no. 0, pp. 48–53, 2019, doi: 10.1016/j.jbiom ech.2019.02.016.