The new coronavirus, known as 2019-nCoV or SARS-CoV-2, causes the flu referred to as COVID-19. It’s been an epidemic in China and is on the verge of becoming a pandemic. An “epidemic” is a large outbreak of disease that spreads among a population or region while a “pandemic” is an epidemic that has spread on a more global scale, affecting large numbers of people.
Epidemics tend to build slowly, then spike, then taper off. Below is a chart from the CDC showing the frequency of new Ebola cases in three countries in West Africa over a two year period. This spiked pattern is common for epidemics. While the exact shape varies, this typifies epidemics – a slow increase, a quicker rise and then a tapering off.
Epidemics can also occur in waves. The worst pandemic in modern history was the Spanish Flu of 1918 which killed an estimated 50 million people worldwide. About 1/3rd of the world population contracted the flu and 5% of the world population was killed by it. IFOD on Spanish Flu: The Deadliest Month in U.S. History. The 1918 flu had three waves:
How a contagious disease becomes an epidemic depends on a few factors.
Basic Reproduction Rate: R0
The first factor is the “basic reproduction rate” which is abbreviated as “R0” (pronounced “R nought”). R0 is a measure of how contagious a disease is by quantifying on average how many other people the infected person will infect. An R0 below one means the disease will stop spreading, over one and the disease will spread. An R0 of one means the disease will be “endemic” and just hang around without spreading quickly or dying out.
Measles, which is super contagious, has a R0 of 15 – 18. Ebola’s is 2, the seasonal flu varies from 1 to 2.1, 2009’s swine flu was 1.33.
The current coronavirus, 2019-nCoV, has an estimated R0 of 2 – 3.3. It’s still early and this data is from China and is considered a bit sketchy. Notably, 2019-nCoV has had a lower R0 outside of mainland China.
An important point about the basic reproduction rate: it’s not fixed. It can vary based on different environments and affect different populations differently. For example, 2019-nCoV’s R0 might be higher in China due to population density.
All-in-all, however, 2019-nCoV seems to be a relatively contagious disease.
The “Case Fatality Rate” or CFR is the proportion of deaths caused in an infected population. There usually is a trade-off between how many people a disease kills and it’s CFR. According to Dr. Chia-Yi Hou, “pathogens that are very deadly to humans typically do not make it very far because they kill their victims too soon . . . . [Extremely deadly] outbreaks will peter out after a few episodes of transmission from person to person after going too fast and too hard.”
|Disease||Total Deaths||CFR (Fatality Rate)|
|Spanish Flu 1918||50 million||2.5% – 3%|
|SARS-CoV – 2003||774||10%|
|Swine Flu (H1N1) – 2009||300,000||0.01% – 0.08%|
|MERS-CoV – 2012 – present||858||35%|
|Ebola – 2013 -2016||11,325||40%|
|Seasonal Flu||12,000 – 61,000 deaths|
annually in the U.S.
|0.06% – 0.10%|
As the above chart shows – generally big killers have low fatality rates – lots of people get infected but a small percentage die. But a really big number multiplied by a small number is still a big number and the total number of deaths can be large.
A very important consideration is that the fatality rate can vary widely by sub-group of a population. Many diseases disproportionately affect the already sick and elderly. That has thus far been the case with 2019-nCoV as the below chart from Goldman Sachs shows using the data out of China:
The SEIR Model of Epidemics
Predicting the spread and severity of an epidemic is more than just the R0 and CFR. Epidemiologists use models which rely on differential equations to predict and explain spread of disease. A commonly used model is the “SEIR” model:
The SEIR model takes into account many more variables to estimate how many people move through each category. The travel and interaction of people is a big variable. Incubation time of the disease (time between exposure and symptoms) can have a big effect on the spread. The workings of the SEIR model are roughly summarized by economist Robert Shiller as follows:
The epidemic first rises, then falls. The rising period is a time when the contagion rate, the rate of increase of newly infected people, exceeds the recovery rate plus the death rate. During the rising period, the rise in the number of infected people due to contagion outnumbers the fall in the number due to recovery or death. The process is reversed during the falling period. That is, the fall in the number of infected people due to recovery or death outweighs the rise in the number due to contagion, putting the number infected into a steady downward path marking the termination of the epidemic.
Given the high base reproduction rate (meaning that its very contagious) and low fatality rate, the CDC’s prediction that 2019-nCoV will probably spread in the U.S. seems like a likely occurrence.
Data from China found that just over 80% of the cases have been mild – about like getting a cold or are even asymptomatic (which makes stopping the disease’s spread harder). About 15% of the cases have been severe and less than 5% of the cases have been critical (meaning ICU). About half of the critcal cases resulted in death.
Thus, experts predict there is a good chance the coronavirus will spread in the U.S., and while quite a few people will be infected the vast majority of people will have mild cases. If you are older or have a compromised immune system it is essential that you take steps to avoid exposure and seek medical attention at the first signs of sickness.
What can you do? First and foremost DON’T PANIC. Here’s a great article from the Washington Post about what steps to take to prepare for 2019-nCoV: How to Prepare for the Coronavirus in the U.S.
Concerned about the stock market? Also don’t panic. I’ll be sending out later today our firm’s take on the investment markets and the coronavirus.