Spatiotemporal Analysis for COVID-19 Delta Variant Using GIS-Based Air Parameter and Spatial Modeling

Mokhamad Nur Cahyadi, Hepi Hapsari Handayani, Warmadewanthi Idaa, Catur Aries Rokhmana, Soni Sunarso Sulistiawan, Christrijogo Sumartono Waloedjo, Agus Budi Raharjo, Endroyono, Mohamad Atok, Shilvy Choiriyatun Navisa, Mega Wulansari and Shuanggen Jin
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Mokhamad Nur Cahyadi: Geomatics Engineering Department, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia
Hepi Hapsari Handayani: Geomatics Engineering Department, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia
Warmadewanthi Idaa: Department of Environmental Engineering, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia
Catur Aries Rokhmana: Faculty of Engineering, Universitas Gadjah Mada, Yogyakarta 5581, Indonesia
Soni Sunarso Sulistiawan: Department of Anesthesiology and Reanimation, Faculty Of Medicine, Universitas Airlangga-Dr. Soetomo Hospital, Surabaya 60132, Indonesia
Christrijogo Sumartono Waloedjo: Master Program in Disaster Management, Postgraduate School, Universitas Airlangga-Dr. Soetomo Hospital, Surabaya 60132, Indonesia
Agus Budi Raharjo: Department of Informatics Engineering, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia
Endroyono: Department of Electrical Engineering, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia
Mohamad Atok: Department of Statistics, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia
Shilvy Choiriyatun Navisa: Geomatics Engineering Department, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia
Mega Wulansari: Geomatics Engineering Department, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia
Shuanggen Jin: Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai 200030, China

IJERPH, 2022, vol. 19, issue 3, 1-20

Abstract: The coronavirus disease of 2019 (COVID-19) pandemic is currently a global challenge, with 210 countries, including Indonesia, seeking to minimize its spread. Therefore, this study aims to determine the spatiotemporal spread pattern of this virus in Surabaya using various data on confirmed cases from 28 April to 26 October 2021. It also aims to determine the relationship between pollutant parameters, such as carbon monoxide (CO), nitrogen dioxide (NO 2 ), sulfur dioxide (SO 2 ), and ozone (O 3 ), as well as the government’s high social restrictions policy in Java-Bali. Several methods, such as the weighted mean center, directional distribution, Getis–Ord Gi*, Moran’s I, and geographically weighted regression, were used to identify the spatial spread pattern of the virus. The weighted mean center indicated that the epicenter location of the outbreak moved randomly. The directional distribution demonstrated a decrease of 21 km 2 at the end of the study phase, which proved that its spread has significantly reduced in Surabaya. Meanwhile, the Getis–Ord Gi* results demonstrated that the eastern and southern parts of the study region were highly infected. Moran’s I demonstrate that COVID-19 cases clustered during the spike. The geographically weighted regression model indicated a number of influence zones in the northeast, northwest, and a few in the southwest parts at the peak of R 2 0.55. The relationship between COVID-19 cases and air pollution parameters proved that people living at the outbreak’s center have low pollution levels due to lockdown. Furthermore, the lockdown policy reduced CO, NO 2 , SO 2 , and O 3 . In addition, increase in air pollutants; namely, NO 2 , CO, SO 2 and O 3 , was recorded after 7 weeks of lockdown implementation (started from 18 August).

Keywords: COVID-19; lockdown; spatial pattern; air pollutions (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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