(Last updated on 2020-11-13 18:12:17. A version as of April 26th was published at Medium)
(Note: as of August 19th, 2020, the numnber of new cases (7-day rolling average) has surpassed the stable phase threshold in South Koare. So do several other countries that used to be in the stable phase.)
We have learned how South Korea has implemented a trace-test-isolate strategy - universally across the country and consistently from the outset of the epidemic. So, how does the flattened curve in South Korea look like today, and how does it compare to other countries?
We will compare metrics of the epidemic curve in South Korea and other high-resource settings, specifically the Organisation for Economic Co-operation and Development (OECD) member countries. In addition, Taiwan and Singapore are included, considering their exemplary handling of the epidemic (at least until recently). Understanding different epidemic curves to date will help us not only address the current crisis but also prevent, delay, and flatten the next waves.
Hover over each figure to see values and more options.
See data sources and methods at the end.
Before we check the epidemic curve to date, let’s briefly compare cumulative incidence (number of confirmed cases per 100,000 population) and mortality rates. In South Korea (blue bars below), incidence rate is relatively low (left panel), despite widespread testing. Considering different testing rates, COVID-19 specific mortality rate per population (right panel) may be more appropriate to compare across countries. Singapore, South Korea, and Taiwan all have substantially low COVID specific mortality rates. For data values and interactive options, see this.
And, in case you wonder about case fatality rate. South Korea might have relatively less clinically severe patient population, with relatively young patient population.
The epidemic curve in this analysis was constructed based on the number of new confirmed cases each day. Considering vastly different population sizes, the number of new confirmed cases each day per 100,000 population was used, instead of the absolute number. In addition, 7-day rolling averages were used to avoid any isolated peaks/drops, which can be caused by various reasons other than the true course of the epidemic itself (e.g., changes in definition, and lab process delay). Despite varying testing rates, especially at the outset, the shape of the curve provides insight about the epidemic in each country. See annex for further information about methods.
Let’s focus on the first wave of the epidemic, which we define to start when cumulative incidence exceeds 1 confirmed case per 100,000 population. The peak is when the number of new confirmed cases is at its maximum. There are three distinctive phases:
With successful control of the epidemic, we will see both narrow (in length) and short (in height) peak.
The data clearly show the three phases in South Korea. The cumulative incidence rate (gray bars, left axis) exceeded 1 per 100,000 population on 2020-02-23, and the daily new cases (black line, right axis) increased steeply, reaching the peak of 1.2 new confirmed cases per 100,000 population on 2020-03-01. Then, new cases started to drop consistently, entering to the second phase (orange shade). Note that the cumulative incidence still increases substantially during the second phase. For over a month, the country has been well into the relatively stable phase (yellow shade) since 2020-09-05. Cumulative incidence has increased at a much lower rate recently.
However, there was a second peak (though relatively small) in late August.
Among OECD members, currently 2 other countries also have entered the relatively stable phase, although the first wave started later. Holding beginning of the first wave on day 0, the following figure presents the length and height of the peak.
It is notable that several countries with a substantially high peak also have entered this phase.
Note: The x-axis is days from the first day of phase 1. The y-axis is daily new cases per 100,000 population (necessary for comparing big and small countries). The dotted horizontal line is the number of new cases at the start of the first wave.
Meanwhile, a majority of countries have not yet entered the relatively stable phase. Most of these countries have a substantially higher peak, compared to the above countries.
All countries are presented below in groups, due to varying range of peak height. Top panel has countries with relatively lower peak, and the bottom panel shows countries with higher peak.
Again, the x-axis is days from the first day of phase 1. The y-axis is daily new cases per 100,000 population.
Now, back to the three which have been spotlighted for their exemplary handling of the epidemic. Taiwan has shown the best results of epidemic control - with very slow or no apparent progression to the first wave. In Singapore, the first wave started around the same time with South Korea. The outbreak had been controlled successfully for over a month, but recently the number of new cases has increased exponentially. The Singapore case shows the challenges to control the epidemic even under strong public health systems.
To summarize, the first figure below shows the timeline of the first wave and its phases among OECD countries. South Korea had the first wave earlier than any other countries (red dots). It also has one of the shortest length of the peak (between red and yellow dots). Again, the peak length is still to be determined in some countries (with no yellow dot below).
The next figure shows the height of the peak (the maximum number of new confirmed cases per 100,000 population) by country. The peak was relatively low in South Korea, following several other countries.
Finally, if we put together the height of the peak (on a log scale, Y axis) and time to the peak (distance between the red and orange dots above), a positive correlation appears (i.e., the longer the time to peak, the higher the peak).
(Note: The unique curve in Singapore stresses that the numeric measures must be interpreted with the curve itself. Though Singapore’s peak is high, the duration of its true peak is much shorter than that in other countries.)In summary, data confirm that South Korea is one of several high-resource countries that successfully controlled the first wave of COVID-19 and have moved into the relatively stable final phase of the first wave. A majority of OECD countries have approached or passed the peak, when the number of new cases starts to decrease, though significant rebounds have occurred in some places.
Moving into the more stable phase, in South Korea and all other countries, public heath authorities need to take full advantage of opportunities to realign resources in order to prevent, delay, and flatten the next wave.
METHODS
Data
1. All COVID-19 data (i.e., cumulative confirmed cases and deaths by day) come from JHU/CSSE. Accessed on 2020-11-13.
2. All data on country population come from UN World Population Prospects 2019 Revision. Accessed on April 18, 2020.
Note on comparability: JHU/CSSE compiles the best available data, but a definition of confirmed cases (even COVID-19 deaths) may differ across countries and even within a country over time. The abrupt increase in the number of new cases in France, for example, likely reflect changes in the definition of confirmed cases.
Measures The number of new confirmed cases on each date was calculated based on the difference between cumulative numbers over two consecutive days. Then, a seven-day rolling average was calculated, hereinafter referred to as the smoothed number of new confirmed cases. Then, the smoothed number was divided by the total population in the country: the smoothed number of new confirmed cases per 100,000 population.
COVID-mortality may be a more comparable indicator to understand the full extent of the epidemic, given considerably different testing strategies and testing rates. However, countries are currently at different stages of the curve, and comparison of mortality data would be possible once most countries are in a similar phase of the epidemic (i.e., well in the the stable phase).
See GitHub for data, code, and more information. For typos, errors, and questions, contact me at www.isquared.global.
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