The construction of a hospital disease tracking and control system with a disease infection probability model

Abstract

With relatively short latency and rapid propagation, viral diseases could be transmitted through the air to medical personnel or the public during the incubation period. To reduce the possibilities of spread, this research creates an infection probability model based on the settling velocity and concentration distribution of infectious droplets. Then, radio frequency identification (RFID) technology is employed to track the travel history (time, date and place) of the infected patients. A tree structure algorithm and an infection probability model are applied to trace the transmission routes, discover the correlations between carriers and suspected cases, and finally calculate the infection probability on the basis of time interval. In case of an epidemic outbreak or once an infected case is confirmed, the disease tracking and control system could be initiated by accessing RFID logs to plot the carriers' time of onset and to trace possible routes of transmission via tree diagrams. The disease tracking and control system developed in this research can assist hospitals in assessing the risk of infection among medical personnel, as well as in prompt implementation of infection prevention and control measures, in order to reduce hospital acquired infections and provide a safe health care setting.

Fuzzy-logic-based IoMT framework for COVID19 patient monitoring

“…The relevance of Radio-Frequency IDentification (RFID) technology for managing affected people’s medical information and surveillance devices without being contaminated is highlighted by Huang and Xie ( Huang & Xie, 2014 ). Alshraideh et al.…”