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Geospatial Analytics for COVID-19 Active Case Detection

Computers, Materials & Continua 2021年第4期67卷 835-848页

作者： Choo-Yee Ting Helmi Zakariah fadzilah kamaludin Darryl Lin-Wei Cheng Nicholas Yu-Zhe Tan Hui-Jia Yee Multimedia University Cyberjaya63100Malaysia AIME Healthcare Sdn Bhd Kuala Lumpur59200Malaysia

Ever since the COVID-19 pandemic started in Wuhan,China,much research work has been focusing on the clinical aspect of *** have been leveraging on various Artificial Intelligence techniques as an alternative to medical approach in understanding the *** studies have,however,reported on COVID-19 transmission pattern analysis,and using geography features for prediction of potential outbreak *** the next most probable outbreak site is crucial,particularly for optimizing the planning of medical personnel and supply *** tackle the challenge,this work proposed distance-based similarity measures to predict the next most probable outbreak site together with its magnitude,when would the outbreak likely to happen and the duration of the *** work began with preprocessing of 1365 patient records from six districts in the most populated state named Selangor in *** dataset was then aggregated with population density information and human elicited geography features that might promote the transmission of *** findings indicated that the proposed unified decision-making approach outperformed individual distance metric in predicting the total cases,next outbreak location,and the time interval between start dates of two similar *** findings provided valuable insights for policymakers to perform Active Case Detection.

关键词： COVID-19 geospatial analytics active case detection

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