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Geographic information system-based spatio-temporal detection and mapping of COVID–19 hot/cold spots in Oman

Al-Mulla, YaseenAl–Muqaimi, MohammedAli, AhsanAl-Badi, TaifParimi, KrishnaChowdary, Anusha
International Journal of Electrical and Computer Engineering (IJECE) (Sinta 1)Vol. 0 No. 01 Oktober 2024
DOI10.11591/ijece.v14i5.pp5779-5801

Abstrak

Infected COVID-19 patients, especially after March 11, 2020, grew drastically in Oman. Hence, a variety of measures were issued to restrict all social gatherings, commercial activities, and mandating preventative health practices. This study aimed to i) understand distribution patterns and impact of decisions and responses at the spread of confirmed cases; ii) highlight and verify most concentrated regions with infections; and iii) overview spatial changes of cases overtime. The analysis was carried out using inverse–distance-weighted interpolation and hotspot (Getis–Ord GI*) techniques. Results showed a substantial relationship between spatial structure of COVID–19 and population distribution and density. COVID–19 has increased by 11.5% weekly in the capital, which were locked down since April 2020. However, after health quarantine was lifted on May 29, 2020, weekly cases surged in the capital. Al-Batinah-North and Dhofar recorded an increase of 32.1% and 30.5%, respectively, after restrictions had eased. The analysis illustrated that spread of COVID–19 was shifting from Northeast to Southeast and later shifted back to the Northeast of the country at the end of year 2022. This study is beneficial for pertinent organizations to perform detailed studies for developing and monitoring disease systems and dominating relevant factors.

Kata Kunci

Computer Science and Information TechnologyCold spotsCOVID–19Geographic information systemHot spotsInverse distance weighted analysis

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Geographic information system-based spatio-temporal detection and mapping of COVID–19 hot/cold spots in Oman | International Journal of Electrical and Computer Engineering (IJECE) | Publiora