COVID-19: What does it mean to flatten the curve?

A brief look at COVID-19 infection rates in South Korea and Italy.

COVID-19 Situation Overview

According to the World Health Organization (WHO), COVID-19, or “coronavirus disease 2019,” is a respiratory illness caused by a newly discovered coronavirus that is believed to have originated in Wuhan City, China, in December 2019. The virus has since spread to most of the globe, with confirmed cases increasing every day.

Figure 1: Heatmap of Confirmed COVID-19 Cases, 4/1/20 (Tableau Public)

The United States has the highest number of confirmed cases worldwide, with almost double the number of confirmed cases in Italy, which has the second highest number of confirmed cases. The United States and Italy make up more than a third of the total confirmed cases worldwide. The table below shows the countries with the top 10 highest number of confirmed cases as a percent of the total number of global confirmed cases.

Table 1: Countries with Highest Confirmed Cases as Percent of Total

What is the “curve” and what does it mean to “flatten” it?

The “curve” refers to the number of total confirmed COVID-19 cases over time; a “flattened” curve is one with a slope of zero, indicating that there is no change in the total number of confirmed cases. The rationale behind the need to flatten the curve is that we want to stop the spread of the disease by decreasing the number of new cases each day until we have no new cases (and a “flat” curve).

Illustrative Case: South Korea

South Korea has been praised for its containment of COVID-19. The country was able to “flatten” its curve using the following strategies:

• Extensive testing and contact tracing.
• Full transparency.
• An all-government approach that included regional authorities.

Figure 2 below is a plot of the total confirmed cases in South Korea.

Figure 2: Total Confirmed COVID-19 Cases in South Korea (Matplotlib/ Python)

Based on Figure 2, South Korea’s curve appears to have flattened; however, the actual change in the rate of daily new confirmed cases is difficult to quantify from this plot.

By differencing the data once to look exclusively at the new confirmed cases instead of the cumulative number of cases, we are able to visualize what it actually means for a country to flatten its curve. Figure 3 below shows the plot of the number of new confirmed cases in South Korea as a function of time. To see the overall trend, I also plotted its weekly rolling average.

Figure 3: Daily Increase in Confirmed Cases in South Korea (Matplotlib/ Python)

Illustrative Case: Italy

Compared to South Korea, Italy is still working to contain the spread of COVID-19. The number of confirmed cases in Italy increased rapidly since its first cases appeared in Lombardy at the end of February.

Figure 4: Total Confirmed COVID-19 Cases in Italy (Matplotlib/ Python)

Figure 4 shows the total confirmed cases in Italy. The number of confirmed cases has increased significantly in the past month. Yet, there seems to be a slight inflection point at the end of March, potentially indicating a decrease in the rate of newly confirmed cases. Let’s look at a plot of the differenced data to visualize this better.

Figure 5: Daily Increase in Confirmed Cases in Italy (Matplotlib/ Python)

As shown in Figure 5 , the number of newly confirmed cases of COVID-19 in Italy has been decreasing overall since last week. While promising, this does not guarantee that Italy’s curve will flatten in the next couple months. It will take continued public health efforts to ensure Italy’s ultimate success in containing this virus.

Last Update: April 2, 2020

Data Sources:

1. John’s Hopkins University
2. Kaggle

Resources:

1. World Health Organization (WHO): https://www.who.int/emergencies/diseases/novel-coronavirus-2019
2. Center for Disease Control (CDC): https://www.coronavirus.gov/
3. Harvard Business Review article, “Lessons from Italy’s Response to Coronavirus”: https://hbr.org/2020/03/lessons-from-italys-response-to-coronavirus