Influenza is a potentially life-threatening virus that spreads quickly from person to person. It is a significant public health issue in this country, with 10–20 percent of New Zealanders infected every year.
Imagine 10% of New Zealanders got Corona-virus, approximately 500,000 people. Imagine 2% of this 500,000 died, which is approximately 10,000 people. If 20% got infected, and the death rate was 4% then 40,000 could die. It is important to understand this is just an educated guess. No one knows what the future will bring.
How does this compare with our annual death rate? In 2018: 33,225 deaths were registered in New Zealand (Nz Stats). So 10,000 to 40,000 deaths is a lot of people, the impact would be felt deeply at a person level and perhaps change society as a whole. The overall impact may depend on how people react. The impact on the economy due to fear, the lack of travel insurance, the uncertainty and the impact on overloaded health services is significant.
Understanding the potential cost of doing nothing is important. If the government closes schools, we can support this decision when we understand it.
For COVID-19, data to date suggest that 80% of infections are mild or asymptomatic, 15% are severe infection, requiring oxygen and 5% are critical infections, requiring ventilation. These fractions of severe and critical infection would be higher than what is observed for influenza infection.
SOURCE: https://www.who.int PDF Coronavirus disease 2019 (COVID-19)
Imagine 20% of the people who get Coronavirus get very sick. 20% of 500,000 people is 100,000 people. I wonder how many hospital beds we have to look after these people? Evidence from 2012 estimates under 15,000 hospital beds in New Zealand. Now we start to see the scale of the problem, even if we have 30,000 hospital beds, they are already in use, so what do we do with 100,000 very sick people? Hence, we need to "Flatten the curve" in order to ensure that when people are sick enough to be in hospital, there is a bed for them, oxygen, medicine and care.
There was a wonderful paper published that analyzed data regarding the Spanish flu in 1918, examining proactive versus reactive school closures. When did [regional] authorities close the schools relative to when the epidemic was spiking? What they found was that proactive school closing saved substantial numbers of lives. St. Louis closed the schools about a day in advance of the epidemic spiking, for 143 days. Pittsburgh closed 7 days after the peak and only for 53 days. And the death rate for the epidemic in St. Louis was roughly one-third as high as in Pittsburgh. These things work.
From: Science Mag
You'll probably see a lot of explanations about exponential growth of viruses in populations. It surprises our intuition when the time it takes for the disease to move from 10 to 1000 cases is the same it takes for it to move from 10 thousand to 1 million cases. Which means that, pretty much wherever you are, you should start social distancing now to avoid the rush.
Quote from: Dan Finkel (Prime Climb & Math for Love).
Maths with Stats ran the numbers and predict America will run out of hospital beds in May 2020.
What does the coronavirus mean for the U.S. health care system? Some simple math offers alarming answers. If 20% of cases require hospitalization, we run out of beds by about May 4. If only 5% of cases require it, we can make it until about May 16, and a 2.5% rate gets us to May 22.
More excellent links:
We need your support to protect New Zealand and eradicate COVID-19. Enforcement measures may be used to ensure everyone acts together, now.
Don’t worry, during this time, you will be able to access all the essentials you need, including medicine, food and other home supplies.
This graph is available on https://www.worldometers.info/coronavirus/coronavirus-death-toll/ along with the data.
The source of this data has many issues relating to accuracy and reporting, however, he overall trend is very easy to see.
This graph is created using https://grapher.nz/ Time series module. You can see the red is the prediction which has a narrow margin of error. The blue trend curve shows this mathematical model fits the raw data well.