EDDAMAP: efficient data-dependent approach for monitoring asymptomatic patient
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(2020) 20:245
RESEARCH ARTICLE
Open Access
EDDAMAP: efficient data-dependent approach for monitoring asymptomatic patient Daniel Adu-Gyamfi1,2*
, Fengli Zhang2 and Albert Kofi Kwansah Ansah2,3
Abstract Background: A pandemic affects healthcare delivery and consequently leads to socioeconomic complications. During a pandemic, a community where there lives an asymptomatic patient (AP) becomes a potential endemic zone. Assuming we want to monitor the travel and/or activity of an AP in a community where there is a pandemic. Presently, most monitoring algorithms are relatively less efficient to find a suitable solution as they overlook the continuous mobility instances and activities of the AP over time. Conversely, this paper proposes an EDDAMAP as a compelling data-dependent technique and/or algorithm towards efficient continuous monitoring of the travel and/or activity of an AP. Methods: In this paper, it is assumed that an AP is infected with a contagious disease in which the EDDAMAP technique exploits a GPS-enabled mobile device by tagging it to the AP along with its travel within a community. The technique further examines the Spatio-temporal trajectory of the AP to infer its spatial time-bounded activity. The technique aims to learn the travels of the AP and correlates them to its activities to derive some classes of point of interests (POIs) in a location. Further, the technique explores the natural occurring POIs via modelling to identify some regular stay places (SP) and present them as endemic zones. The technique adopts concurrent object feature localization and recognition, branch and bound formalism and graph theory to cater for the worst error-guaranteed approximation to obtain a valid and efficient query solution and also experiments with a real-world GeoLife dataset to confirm its performance. Results: The EDDAMAP technique proofs a compelling technique towards efficient monitoring of an AP in case of a pandemic. Conclusions: The EDDAMAP technique will promote the discovery of endemic zones and hence some public healthcare facilities can rely on it to facilitate the design of patient monitoring system applications to curtail a global pandemic. Keywords: Asymptomatic patient, Data-dependent technique, Health decision-support system, Pandemic, Patient monitoring system, Place of interests, Preventive intervention, Public health, Stay place, Trajectory data mining
*Correspondence: [email protected] Department of Computer Science and Informatics, University of Energy and Natural Resources, P O Box 214 Sunyani, Ghana 2 School of Information and Software Engineering, University of Electronic Science and Technology of China, 610054 Chengdu, China Full list of author information is available at the end of the article 1
© The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original autho
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