A major public message by health authorities in the midst of the COVID-19 can probably be summarized through one concept — we must do our best to flatten the curve.

While the graphic has been considerably successful in spreading the important message in this pandemic, there are some misconceptions about this “curve” that may lead to some deadly consequences. Let’s address them.

What is the ‘curve’ and how do we ‘flatten’ it?


First, we must understand what is the “curve.” This curve, known as an epidemic curve or epi curve, is a plot of the number of new cases of a disease over time.

To “flatten” the epi curve is to slow down the growth of new cases. By slowing down the growth of cases sufficiently, we can ensure that the number of sick people that need to be tended to will be within a region’s overall healthcare system capacity, depicted as a horizontal broken line in the illustration.

Apart from of course stopping the spread on the whole, to slow the spread is equally important since this will ensure that healthcare systems will be able to properly treat those that did get infected. At the same time, this will avoid overcrowding of hospitals which may accelerate the spread of the disease.

Flattening the curve will entail preventing the virus from being passed on. Social distancing measures factors in largely in turning the purple epi curve above which exceeds the healthcare system capacity to the green epi curve.

Don’t let the bell deceive you

For those who might have taken statistics classes, these curves are eerily similar to a normal distribution. They are symmetric and are bell-shaped. Are epidemic curves depicting a normal distribution?

The short answer is a resounding no.

These graphics are nothing more but helpful and simplified illustrations to help people quickly understand the effect of taking appropriate measures to slow down the rate of infection. However, that doesn’t mean that epidemic curves are truly normal distributions.

To make it clear, here is an example of global-scale epidemic curve provided by the World Health Organization (WHO) for the 2002-2003  Severe Acute Respiratory Syndrome (SARS) outbreak:


Source: World Health Organization (WHO)

One should not be misled to think that epidemic curves are single-peaked, bell-shaped curves.  As we can see above, the number of cases might rise again so that there are multiple peaks. We can also see that the rate of decline of cases is not as dramatic as depicted in infographics.

This misconception however, may pose some deadly consequences. To illustrate, let’s look at another epidemic that is relevant to us at present — HIV.

Case example: HIV Pandemic

An old law in epidemiology, known as the Farr’s Law of Epidemics posits that epidemics rise in fall in a fairly symmetrical pattern that can be approximated by a normal distribution.

What this means is when the number of cases have “peaked,” meaning that cases have been observed to decline, then can expect pandemics to pan out naturally.

A 1990 paper, Farr’s Law Applied to AIDS Projections employed the assumptions of Farr’s Law to predict when the AIDS outbreak at the time would be gone.

Using a bell curve assumption, they projected that the HIV epidemic will end before 2000. But, WHO said that as of 2018, 32 million have already died of HIV. More than 75 million are infected with the virus over the world.

We can see that this assumption that disease outbreaks would just die down as depicted by a  bell curve is a grossly erroneous one. There are so many factors that make pandemics complex that we cannot just expect them to die down. What does this mean for us?

This means that we should not be complacent soon as we see cases go down. If we proceed to lift measures recklessly soon as the cases go down, secondary peaks can easily form and all-out outbreaks and can even be renewed. Flattening the epidemic curve is more than just bringing the cases down — it is also a matter of implementing measures that will ensure that it stays down for good.

Previous Machine Learning Pushes Quantum Computing Forward
Next Red Hat Accelerates AI/ML Workflows And Delivery Of AI-Powered Intelligent Applications With Red Hat OpenShift