{"id":6159,"date":"2026-05-10T06:30:47","date_gmt":"2026-05-09T22:30:47","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=6159"},"modified":"2026-07-03T14:45:16","modified_gmt":"2026-07-03T06:45:16","slug":"towards-derail-of-global-pandemics-via-patient-trajectory","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=towards-derail-of-global-pandemics-via-patient-trajectory","title":{"rendered":"Towards derail of global pandemics via patient trajectory"},"content":{"rendered":"\n
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<\/i> 2020<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 23, Issue 3<\/a><\/p>\n<\/div>\n\n\n\n

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Daniel Adu-Gyamfi1,2<\/sup><\/i><\/a> and Fengli Zhang1<\/sup><\/p>\n\n\n\n

1<\/sup>School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China<\/p>\n\n\n\n

2<\/sup>Department of Computer Science and Informatics, University of Energy and Natural Resources, P O Box 214 Sunyani, Ghana<\/p>\n<\/div>\n\n\n\n

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Received:\u00a0September 24, 2019
Accepted:\u00a0February 13, 2020
Publication Date:\u00a0May 10, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

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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 \u201cBase Station\u201d is the medical centre where the GPS data are processed by the data analytic experts.<\/p>\n<\/div>\n<\/div>\n\n\n\n

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

Download Citation:  BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202009_23(3).0005<\/a>  <\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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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.<\/p>\n\n\n\n

Keywords:\u00a0Health Information System; Mobile Data Analysis; Mobile Outdoor Patient; Patient Monitoring Technique; Spatio-Temporal Trajectory<\/em><\/p>\n\n\n\n

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