"If anything kills over 10 million people in the next few decades, it's most likely to be a highly infectious virus rather than a war." Bill Gates, the co-founder of Microsoft Corporation, successfully predicted that our world wasn't ready for a pandemic during a Vancouver TED talk in 2015. The American business magnate mentioned the central issue of the excessive investment towards nuclear deterrents and a lack of funding towards medical systems. With the example of Ebola, Bill Gates solidified his point on how the proper medical team was not ready, eventually leading to slower recovery and a soaring death rate. Although pandemics can be perceived as viruses that simply spread, examining the varying patterns of these viruses via mathematics provide us with a deeper understanding of how the certain virus behaves.
Coronavirus, commonly referred to as COVID-19, is an infectious disease capable of causing respiratory illness with symptoms ranging from fevers to breathing difficulties. What's dangerous about this pandemic is the virus's ability to spread. The virus travels whenever an infected individual coughs or sneezes and can also spread when a person touches a surface that has the virus. The pandemic, known to have originated from Wuhan, China, has affected 199 countries and territories as of March 29th. Moreover, as of March 29th (7:17 PM, KST), there are 672,027 confirmed cases (123,781 cases in the USA), and 31,191 confirmed deaths. These numbers will surely be on the rise in the upcoming months.
So you may be wondering... how serious this situation is?? When comparing the coronavirus case with other deadly outbreaks in the past, it is fair to say that the coronavirus is spreading rapidly compared to Ebola, SARS, and more (especially the first two months since the start of the outbreak). Although coronavirus is spreading rapidly, we are still lucky as the coronavirus's death rate is way less compared to the other viruses we had in the past. For instance, as of March 19th, the death rate is currently at around 4.1% ((8983/220313)*100). 4.1% is nowhere near Ebola's death rate of 68% (40 days after the start date) and MERS' death rate of 18.1% (40 days after the start date). The coronaviruses' death rate 40 days after the start date was 2.4%.
What is interesting though, is how Coronavirus is serious yet, statistically speaking, we have a higher chance of dying from other factors. According to Abaca (YouTuber), every single day there are 13,689 deaths from strokes, 24,641 deaths from heart diseases, 26,283 deaths from cancer, 3000 deaths from suicide, 3287 deaths from car crashes, 877 deaths from drowning, and 1095 deaths from homicide while the Coronavirus was averaging around 108 deaths per day (currently the numbers have soared, and there are cases of 800-900 deaths per day). Still, statistically, this means that you should worry more about dying from a car crash than from Coronavirus (although there are numerous factors such as where you live, your age, etc. = simply a ballpark estimate). Alongside the fact that we are making numerous assumptions, situations like car crashes, cancer, and drowning do not impact people exponentially (more and more rapidly). It's challenging to wrap our heads around how quickly exponential items grow.
Here's a good example:
Would you rather receive 500 dollars a day for a year?
... OR would you receive 1 dollar but double the money every day for a year?
Although taking 500 dollars a day may seem like a better choice, you will earn more when choosing the second option. The first scenario will give you $178,000. Yet, if you select the second option, you will have enough money to spend around $4.5 x 10^101 per SECOND!!
Inflection Points:
When attempting to understand when the pandemic will die out (number of new cases will come to an end), it is imperative to identify inflection points. Inflection points signify a change in concavity on a logistic curve where the maximum value the graph can approach is equivalent to the whole population (linear scale will always be exponential). In other words, the inflection point is when the logistic curve goes from curving upward to instead curving downwards. Yet, as there is a very slim chance of the whole world population becoming infected, the logistic curve usually never reaches the maximum population value (the similar idea of carrying capacity).
At the inflection point, the number of new cases for the virus each day, represented by the slope of the logistic curve, stops increasing and will remain constant until it soon starts to decrease. AND to understand whether our slope is either increasing or decreasing, we often look at the growth factor. The growth factor is simply the ratio between the number of new cases one day and the number of new cases the previous day. With regard to this notion, during the exponential part of the logistic curve before reaching the inflection point, the growth factor will stay consistently above 1. However, as the growth factor reaches the exact value of 1, it is likely a sign that the virus has hit the inflection point. With this information in mind, I decided to look at how the number of cases each day was changing and looked for times where the growth rate was at the value of 1. If the growth rate is 1, indicating an inflection point on the logistic curve, the total number of cases will max out at approximately two times wherever we are.
On March 1st, when there were 88,585 cases of the coronavirus, it was the first time the growth rate had arrived at a growth rate of 1. Hence, it made sense to assume that the inflection point may have been reached. If the virus had hit the inflection, in the upcoming days, the growth rate would have remained at 1 or decrease to values lower than 1. On March 2nd, there were 90,443 cases with a growth rate of 0.94. This meant that the slope mentioned earlier on was starting to decrease, possible hinting at the inflection and the termination of the virus in the upcoming months. Yet, the growth rate of the coronavirus shot above 1 once again hitting values ranging from 1.04 to 1.59.
The second time the coronavirus hit a growth rate of precisely 1 was 182,490 cases on March 16th. Nonetheless, the following day the growth rate once again hit 1.22, which was above the 1.00 growth rate value. Moreover, if the virus did actually hit the inflection point on March 16th, the number of cases would've maxed out at 364,980 cases (182,490*2), yet as of March 29th, there are more than 672,000 deaths. Examining the inflection point pattern helped me understand how the virus was behaving, yet was still uncertain as to when the virus was going to reach its maximum value (maximum number of cases).
We Can All Work Together To Change Things Around:
Undoubtedly, the community's reaction will determine the final number of cases of the current coronavirus. As shown in a 3Blue1Brown YouTube video, the change over a day for the coronavirus is said to be E*p*N. E is the average number of people someone infected is exposed, p being the probability of each exposure becoming an infection and N denoting the number of cases on a given day. Thus, we know that if E and p decrease (which inevitably will), the change over a day will also decline. Actions that slow the rate of spreading such as washing our hands, wearing masks, and following the social distancing policy contribute to the E and p factor. Thus, hinting at the Reverse Tinkerbell effect: the more dangerous we believe the coronavirus will be, the less dangerous it will be as we will take certain actions that we wouldn't have taken if we thought that the virus was not deadly.
In addition, 'flatten the curve' has become a popular phrase among the societal platform as people want to emphasize the significance of how we can all play a role in slowing the coronavirus down. As there is no vaccine or developed medication to treat COVID-19, and as testing opportunities are extremely limited in the United States, the only way to flatten this curve is through collaboration -- such means of closing down schools and moving into online classes, having employees work at home, and quarantine policies.
The curve can come in varying shapes, depending on the virus's infection rate. It could be in the form of a steep curve, denoting that the virus is spreading exponentially (cases doubling at a consistent rate), and the total number of cases reaches its peak within the upcoming weeks. Infection curves with a steep rise will have a precipitous fall; after the virus infects the whole population, case numbers begin to drop exponentially.
The quicker the infection curve rises, the faster the health care systems become overloaded with infected individuals, unfortunately going beyond its capacity to treat people. In countries like Italy, more patients may be forced to go without ICU (Intensive Care Unit) beds, and hospitals may run out of the necessary supplies they need to respond to COVID-19 in the upcoming week.
Yet, a flatter curve will mean the same number of ultimate cases but over a more extended period. A slower infection rate implies a less crammed health care system, fewer hospital visits per day, and fewer sick people being turned away due to the lack of medical equipment.
Think of it this way:
There is a public bathroom with a limited number of stalls in a park. If everyone decides to go at the same time, there will definitely be problems as there are not enough stalls for everyone. If the same number of people need to go to the bathroom, but over multiple hours, everything is fine.
Here's a link to a detailed explanation of 'flattening the curve' presented by Vox.
Although no one is sure when the coronavirus will come to an end, the medical staff team and doctors are working arduously to find a solution. As stated earlier, we all play an immense role in terminating the virus. We are all in this together, and this is an excellent opportunity for all of us to learn, grow, and experience. Stay safe, stay strong, and stay positive!
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