Journal of Applied Science and Engineering

Published by Tamkang University Press

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Towards derail of global pandemics via patient trajectory

 2020

 Volume 23, Issue 3

Daniel Adu-Gyamfi1,2 and Fengli Zhang1

1School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China

2Department of Computer Science and Informatics, University of Energy and Natural Resources, P O Box 214 Sunyani, Ghana

Received: September 24, 2019
Accepted: February 13, 2020
Publication Date: May 10, 2026

上傳圖片

A schematic view of tracking the position instances of a patient. Assume that a patient is traveling with a GPS enabled mobile device. The rectangles with labels A, B, C, D, E, F, G, H are used to capture the respective position instances of an outdoor mobile patient over time. The rectangle D with red broken-line denotes the optimal solution. Thus, among all the eight rectangles the D contains a total of three position instances of the patient representing the maximized weighted-sum of the position data. The “Base Station” is the medical centre where the GPS data are processed by the data analytic experts.

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation:  BibTeX | http://dx.doi.org/10.6180/jase.202009_23(3).0005  

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In recent decades, the public healthcare settings have devoted their attention to the derail of global pandemics. As a result, the public health professionals have adopted patients monitoring as one of the immediate measures to combat disease spreading. Incorrectness of data is a disadvantage of the traditional monitoring system as it is unable to efficiently handle the complex dynamic behavior of patients. The research community seeks to provide compelling techniques or algorithms that can be used to detect the location and travel of potentially hazardous and/or contagious patients in the case of pandemics. Heuristically, the mobility dynamics and activity records of patients are vital resources to support health researches. The activity records of patients contain their whereabouts in time, and that may provide some relevant knowledge for the analysis of disease spreading. This article presents a trajectory data mining technique to support the detection of outdoor mobile patient. The proposed technique examines the spatio-temporal trajectories of the patient via monitoring strategies with the aid of a global position system (GPS) device. The result of the experiment, using GeoLife big dataset has proven the proposed technique as efficient for monitoring an outdoor mobile patient, and it is easy to integrate into mobile health information systems that are intended for outdoor monitoring purposes towards derail of global pandemics.

Keywords: Health Information System; Mobile Data Analysis; Mobile Outdoor Patient; Patient Monitoring Technique; Spatio-Temporal Trajectory

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