An analysis of the dynamic spatial spread of COVID-19 in South Korea


Weekly incidence of COVID-19 cases

Figure 1 shows the time series plots of newly confirmed cases and cumulative confirmed cases each week. The bars indicate weekly new cases with the left axis and the blue line indicates cumulative cases with the right axis. In the time domain of the study, from February 18, 2020 to May 31, 2021, the highest number of new cases was 6887 between December 15 and December 21, 2020 (inclusive). A total of 132,060 patients were diagnosed with COVID-19 during the study period. To understand and compare temporal patterns of weekly case counts, we split the dataset into six time periods based on case counts. If the number of cases in each week was above/below the mean plus/minus the standard deviation of the number of cases for the previous three weeks and the length of the period was greater than 4 weeks, then the new period was determined . Table 1 provides summary statistics for each period. The number of new cases was highest from November 10, 2020 to January 18, 2021 (weekly average of 4290 cases) and lowest from April 7 to August 10, 2021 (weekly average of 138 cases).

Figure 1

Time series plot for weekly confirmed cases and cumulative confirmed cases of COVID-19 in South Korea from February 18, 2020 to May 11, 2021 (the colors of the bars distinguish the six time periods based on Table 1).

Table 1 Summary statistics of the number of weekly cases over six periods in South Korea.

After February 18, 2020, the number of confirmed cases increased significantly until early March 2020. During this period, massive transmission occurred in Daegu and Gyeongsangbuk-do. From February 18 to March 9, 2020, a total of 7,021 patients were diagnosed with COVID-19 in Daegu and Gyeongsangbuk-do, accounting for 90% of the total number of COVID-19 patients in South Korea during this period. Later, the number of new infections increased sharply again since November 2020, mainly in metropolitan areas, including Seoul, Gyeonggi and Incheon. From December 2020 to May 2021, a total of 68,952 cases were reported in Seoul, Gyeonggi and Incheon, accounting for 68% of the cases nationwide during the period. Weekly cases have never been below 3000 cases since April 2021.

To study the geographical distribution of the number of cases, we produced a map of cumulative cases for 250 administrative areas of South Korea (Fig. 2a) and 77 administrative areas of three metropolitan cities of Seoul, Gyeonggi and Incheon (Fig. 2b). . Cases were highest around metropolitan areas and Daegu. Additionally, a strong spatial dependence was found, and most areas of Seoul had more than 1,000 cases.

Figure 2
Figure 2

Map of cumulative confirmed cases of COVID-19 in South Korea from February 18, 2020 to May 31, 2021.

Spatio-temporal analysis over the entire area

We calculated the global Moran (I) statistics for each week over the entire area to verify the spatial association in the number of confirmed cases. In Figure 3, the black and red lines indicate the statistic and its p-value, respectively. Moran’s I p-values ​​were less than 0.0001 at 61 weeks (approximately 91% of the time domain), showing highly significant spatial autocorrelation. Additionally, p-values ​​at 5 weeks ranged between 0.005 and 0.025, providing moderately significant spatial autocorrelation. When the number of new cases increased significantly, the statistics also tended to increase, such as in August and November 2020. This implies that the coronavirus spread spatially when the number of new infections increased. In particular, in 2021, the statistic tends to increase from March 2021.

picture 3
picture 3

Time Series Plot for Global Moran (I) statistic (black line) and p-value (red line) of COVID-19 cases for each week in South Korea from February 18, 2020 to May 11, 2021.

In addition to Moran (I), we calculated the number of areas with a number of cases above a threshold (5, 10, 15, 20 and 25 cases) for each week to study the spatial diffusion, as shown in Fig. 4. The higher the number the more spatial distribution is active. The left side of the (y)– the axis indicates the number of zones, and the right side indicates the number of zones divided by the total number of zones (250 zones). The five lines show a time trend similar to Moran’s (I) statistics in Fig. 3. This pattern indicates that the virus actively spread during peak seasons in South Korea. For example, before August 2020, less than 20% of 250 areas had more than five cases. In contrast, after November 2020, more than 50% of the 250 areas had more than five cases.

Figure 4
number 4

Time series plot for the number of areas with COVID-19 cases above a threshold in South Korea from February 18, 2020 to May 11, 2021.

To detect the high-risk spatial cluster, we used the spatial scan statistic for two peak seasons: the first from February 18, 2020 to mid-March 2020 and the second from December 1 to December 28, 2020. During the first peak season Daegu areas were detected as clusters (Fig. 5, Table 2): the black bordered areas in Fig. 5 represent the clusters. During this period, the number of new infections mainly increased in Daegu and Gyeongsangbuk-do.

Figure 5
number 5

Maps of COVID-19 clusters in South Korea from February 18 to March 10, 2020.

Table 2 Information on COVID-19 clusters in South Korea from February 18 to March 10, 2020.

Unlike the first peak, all clusters were in metropolitan areas in December 2020 (Fig. 6, Table 3). Most of them were in Seoul, and some in Gyeonggi and Incheon. As the maps show, the number of cases was concentrated in metropolitan areas in early December, and the number increased in other areas as the coronavirus spread geographically.

Figure 6
number 6

Maps of COVID-19 clusters in South Korea in December 2020.

Table 3 Information on COVID-19 clusters in South Korea as of December 2020.

Spatio-temporal analysis on metropolitan areas

The population of South Korea’s metropolitan areas was approximately 25,674,800 in 2018, more than 50% of the total population. The number of cases in metropolitan areas has been dominant since April 2020. Prior to the detection of clusters, we calculated the global Moran (I) statistic for each week to examine the spatial distribution in metropolitan areas (Fig. 7). There was statistical significance in many time periods, such as August 2020 and May 2021. Additionally, we counted the number of metropolitan areas with the number of cases above a threshold (Fig. 8). In August 2020, the number of areas with more than five cases increased significantly to more than 80% of the entire area. The rate has not fallen below 80% since December 2020. This implies that spatial spread has occurred in metropolitan areas, justifying the need for a spatial survey of the number of cases in areas. metropolitan.

Picture 7
number 7

Time series plot for Moran (I) statistic and p-value for weekly COVID-19 cases in metropolitan areas of South Korea from February 18, 2020 to May 11, 2021 (No calculation of Moran’s I the week of April 28 to May 4, 2020, due to lack of information about the data).

Figure 8
figure 8

Time series plot for the number of metropolitan areas with the number of COVID-19 cases above a threshold in South Korea from February 18, 2020 to May 11, 2021.

We detected high-risk spatial clusters using an analysis statistic for metropolitan areas from August to September 2020 (Fig. 9, Table 4). Most of the neighborhoods were in Seoul, and only a few were in Gyeonggi.

Figure 9
number 9

Maps of COVID-19 case clusters for metropolitan areas in South Korea from August to September 2020.

Table 4 Information on COVID-19 clusters for metropolitan areas in South Korea from August to September 2020.

The cluster sizes detected in May 2021 were larger than those detected in August-September 2020, and the number of cases in the clusters detected increased accordingly (Fig. 10, Table 5).

Picture 10
number 10

Maps of COVID-19 case clusters for metropolitan areas in South Korea as of May 2021.

Table 5 COVID-19 cluster information for metropolitan areas in South Korea as of May 2021.
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