Spatiotemporal Assessment of COVID-19 Spread over Oman Using GIS Techniques

Abstract

Coronavirus disease (COVID-19), caused by acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a worldwide challenge effecting millions of people in more than 210 countries, including the Sultanate of Oman (Oman). Spatiotemporal analysis was adopted to explore the spatial patterns of the spread of COVID-19 during the period from 29th April to 30th June 2020. Our assessment was made using five geospatial techniques within a Geographical Information System (GIS) context, including a weighted mean centre (WMC), standard deviational ellipses, Moran's I autocorrelation coefficient, Getis-Ord General-G high/low clustering, and Getis-Ord $G_i^{\ast }$ statistic. The Moran's I-/G- statistics proved that COVID-19 cases in datasets (numbers of cases) were clustered throughout the study period. The Moran's I and Z scores were above the 2.25 threshold (a confidence level above 95%), ranging from 2274 cases on 29th April to 40,070 cases on 30th June 2020. The results of $G_i^{\ast }$ showed varying rates of infections, with a large spatial variability between the different wilayats (district). The epidemic situation in some wilayats, such as Mutrah, As-Seeb, and Bowsher in the Muscat Governorate, was more severe, with Z score higher than 5, and the current transmission still presents an increasing trend. This study indicated that the directional pattern of COVID-19 cases has moved from northeast to northwest and southwest, with the total impacted region increasing over time. Also, the results indicate that the rate of COVID-19 infections is higher in the most populated areas. The findings of this paper provide a solid basis for future study by investigating the most resolute hotspots in more detail and may help decision-makers identify targeted zones for alleviation plans.

Supplementary information: The online version contains supplementary material available at 10.1007/s41748-020-00194-2.

Keywords: G i ∗ ; COVID-19; GIS; Moran’s I; Oman; Spatial analysis.

Fig. 1

Study area, including: a location of Oman; b distribution of the 11 governorates in the study area; and c wilayats in the study area

Fig. 2

Schematic map showing the distribution of population in Oman in 2019: a population distribution in 61 wilayats in 2019; b population density (population/km²) for each wilayat in 2019. The same scale is used for all maps

Fig. 3

A log–log plot showing the growth in the accumulative, recovered, and deaths cases between 29th April and 30th June 2020 in Oman

Fig. 4

Maps showing the number of cases per week for each wilayat between 29th April and 30th June in Oman

Fig. 5

Maps showing COVID-19 count per 100,000 and week for each wilayat between 29th April and 30th June in Oman

Fig. 6

Clustering of COVID-19 (using the infection rates for each wilayat as the attribute value). Locations with similarly high numbers of COVID-19 (hotspots) are shown in dark green. COVID-19 rates coded by Gi* statistics display the prevalence of COVID-19 based on weekly data from 29th April to 30th June 2020. The centre of COVID-19 is weighted by the number of cases and over each wilayat over the 9-weeks period (highlighted with green). Standard deviational ellipses of COVID-19 infections distribution in a study area over the 9-week period from 29th April to 30th June 2020 (highlighted in black)

Fig. 7

Shift in the weighted mean center over the study period. The centre of COVID-19 is weighted by the number of cases for each wilayat over the 9-weeks period (highlighted in green in Fig. 6)

Fig. 8

a Autocorrelation (Moran’s I) and observed General G statistic marked with black, while (Moran’s I, and G test p values) is marked with red; Fig. 4b. Autocorrelation (Moran’s I) and observed General G statistic marked with black, while (Moran’s I, and G test Z score) is marked with red