Assessment of
COVID-19 Mass Testing: The Case of South Korea
Jeffhraim Balilla
Bulacan State University
Abstract
South Korea’s knowledge and experience of
the 2015 Middle East Respiratory Syndrome (MERS) allowed them to quickly react
to the 2019 Novel Corona Virus or COVID-19. One of the lapses they have
identified during the MERS outbreak was mass testing which resulted to a death
toll of 38. This paper shows how South Korea’s mass testing allowed them to
control the further increase in the number of new infections. Furthermore, it
also allowed the early detection of outbreak in the city of Daegu after a
period of 33 days from the start of testing.
I.
Introduction
The
Novel Corona Virus 2019 or COVID-19 was first detected in Wuhan, China. It was first
reported to the World Health Organization on the 31st of December
2019. Since then, the virus spread over other countries including South Korea
where it became the second country after China with the most number of
infections during the month of February 2020. On the other hand, South Korea
managed to reduce the number of new infections with their knowledge and
experience of 2015 Middle East Respiratory Syndrome (MERS) that killed around
38 people in the country. During those times South Korea failed to recognize
the importance of mass testing which resulted to exposures of people in
hospitals where people with disease scrambled in search for confirmation that
they are positive of the disease. This paper is an assessment of South Korea’s
mass testing of the new COVID-19 infection.
II.
Methodology
This
paper used Exploratory Data Analysis in assessing how effective the mass
testing South Korea employed. Dataset used came from SemanticScholar.org website which consists of a 53 day (January
1, 2020 to March 12, 2020) record in different locations in the country. Population
and population density were taken into consideration. Also, comparisons were
done across several countries in terms of the number of testing and the
predicted number of new infections. Seventeen (17) locations in South Korea
were included in the analysis namely: Seoul, Busan, Daegu, Incheon, Gwangju, Daejeon,
Ulsan, Sejong, Gyeonggi-do, Gangwon-do, Chungcheongbuk-do, Chungcheongnam-do,
Jeollabuk-do, Jeollanam-do, Gyeongsangbuk-do, Gyeongsangnam-do, and Jeju-do.
III.
Results and Discussions
Table 1.1
Mean Percent Change of Infection / Day (%)
|
Total Count of Infection
|
Total Population (2020)
|
Infected to Population Ratio (%)
|
Population Density /
 |
|
Seoul
|
15%
|
212
|
10349312
|
0.0020484%
|
17100.65
|
Busan
|
15%
|
99
|
3678555
|
0.0026913%
|
4777.34
|
Daegu
|
83%
|
5867
|
2566540
|
0.2285957%
|
2904.97
|
Incheon
|
8%
|
25
|
2628000
|
0.0009513%
|
2472.25
|
Gwangju
|
11%
|
15
|
1416398
|
0.0010590%
|
2826.01
|
Daejeon
|
15%
|
20
|
1475221
|
0.0013557%
|
2732.90
|
Ulsan
|
24%
|
25
|
962865
|
0.0025964%
|
910.94
|
Sejong
|
19%
|
15
|
230327
|
0.0065125%
|
495.11
|
yeonggi-do
|
13%
|
178
|
13530000
|
0.0013156%
|
1330.25
|
Gangwon-do
|
11%
|
29
|
1560571
|
0.0018583%
|
92.48
|
Chungcheongbuk-do
|
23%
|
27
|
1640721
|
0.0016456%
|
220.74
|
Chungcheongnam-do
|
39%
|
114
|
2194384
|
0.0051951%
|
267.48
|
Jeollabuk-do
|
6%
|
7
|
1851991
|
0.0003780%
|
229.58
|
Jeollanam-do
|
13%
|
4
|
1903383
|
0.0002102%
|
155.42
|
Gyeongsangbuk-do
|
82%
|
1143
|
2723955
|
0.0419610%
|
143.14
|
Gyeongsangnam-do
|
21%
|
85
|
3438676
|
0.0024719%
|
326.47
|
Jeju-do
|
9%
|
4
|
342711
|
0.0011672%
|
185.35
|
Table
1.1 shows the figures of average percent change in infection per day and it
shows that Daegu has the highest (83%). Daegu is the third largest city in
South Korea with a population density of 2,904.97 people per square kilometer. It
also has the most cases of confirmed infections (5,867) during the course of 53
days (January 1, 2020 to March 12, 2020). A careful look of Figure 1.1 and 1.2
below shows the number of confirmed cases in Daegu and the number of people
tested across the 17 areas. It is easy to see that South Korea increased the
frequency of testing based on the number of positive cases per day. Notice that
at day 33 (February 21, 2020) the significant increase in the number of testing
resulted to significant increase in the identified number of infected cases. Also,
at day 33, 112,570 people (in entire 17 areas) were already tested and out of
which 152 people in Daegu were identified as positive. This figures allowed the
government to take early actions to focus more efforts in containing the
breakout that will eventually happen in the area. Consequently, the northern
province of Gyeongsangbuk, where Daegu is situated appeared to have significant
increase in infection. Figure 1.3 shows the same trend in the number of cases
in Gyeongsangbuk-do as with Daegu. This is similar to what happened to the
Hubei province in China where the center of outbreak – Wuhan is situated.
IV.
Comparison between South Korea
and Other Countries
Figure 1.4 below
shows the predicted number of infection across several countries including
South Korea. Monterola & Legara (2020) showed that South Korea has
asymptotic decline in the predicted number of infections. This could be due to
other factors that were not discussed in this paper. An example would be the
government’s intensive use of artificial intelligence and big data analytics to
track down the people who were in contact with an infected. Furthermore, mass
testing could also be one of the reason why they manage to reduce the number of
new infections since it allows them to easily identify possible outbreaks in
the earliest of time. Similarly, Figure 1.5 shows the comparison between the
number of people tested across several countries of which South Korea (248,
647) is second to China (320, 000). The steps carried out by South Korea must
be adapted by other nations, particularly the mass testing since in doing so it
allowed fewer new infections because they could easily identify those who carry
the COVID-19 virus.
V.
References
[1] Max Roser, Hannah Ritchie and
Esteban Ortiz-Ospina (2020) - "Coronavirus Disease (COVID-19) – Statistics
and Research". Published online at OurWorldInData.org. Retrieved
from: 'https://ourworldindata.org/coronavirus' [Online Resource]
[2] Monterola
and Legara (2020) - “Projecting the Growth of Infected Individuals from
Confirmed Cases with COVID-19 in the Philippines.”
