Internet of medical things (IOMT) enabled third-party monitoring model for infectious diseases control during epidemics

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.