Citation

Ting, Choo Yee and Zakariah, Helmi and Kamaludin, Fadzilah and Cheng, Darryl Lin Wei and Tan, Nicholas Yu Zhe and Yee, Hui Jia (2021) Geospatial Analytics for COVID-19 Active Case Detection. Computers, Materials and Continua, 67 (1). pp. 835-848. ISSN 1546-2218, 1546-2226

Text Geospatial Analytics for COVID-19 Active Case Detection.pdf Restricted to Repository staff only Download (965kB)

Abstract

Ever since the COVID-19 pandemic started in Wuhan, China, much research work has been focusing on the clinical aspect of SARS-CoV-2. Researchers have been leveraging on various Artificial Intelligence techniques as an alternative to medical approach in understanding the virus. Limited studies have, however, reported on COVID-19 transmission pattern analysis, and using geography features for prediction of potential outbreak sites. Predicting the next most probable outbreak site is crucial, particularly for optimizing the planning of medical personnel and supply resources. To 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 outbreak. The work began with preprocessing of 1365 patient records from six districts in the most populated state named Selangor in Malaysia. The dataset was then aggregated with population density information and human elicited geography features that might promote the transmission of COVID-19. Empirical 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 sites. Such findings provided valuable insights for policymakers to perform Active Case Detection.

Item Type:	Article
Uncontrolled Keywords:	Geospatial data
Subjects:	G Geography. Anthropology. Recreation > G Geography > G1-922 Geography (General)
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	14 Mar 2021 01:23
Last Modified:	14 Mar 2021 01:23
URII:	http://shdl.mmu.edu.my/id/eprint/8588

Downloads

Edit (login required)