Monthly Archives: March 2020

I’m getting a lot of stories from people in other countries. Instead of creating different posts about them, I thought it would make sense to either put the URLs here or copy the text so they all can be easily found again in one place for anyone who cares to see what is on our horizon, or look back after the tsunami hits us.

“Those who cannot remember the past are condemned to repeat it.”
George Santayana (1863-1952)

My Coronavirus timeline in Italy (by Tanya Alice)
What day are you on?

Day -1 (Feb 20 – 3 cases) – It’s something happening in a far away country.

Day 2 (Feb 23 – 152 cases) – Oh wow, there are cases of this in my country. But it’s just like the flu, no need to panic. Everyone is overreacting. And it’s well away from my area.

Day 4 (Feb 25) – They are closing schools and canceling sporting events in other parts of my country. But it really only affects old people. I’ll be fine.

Day 5 (Feb 26 – 424 cases)- Let’s talk about the politics of this. Politicizing it will probably lead to solutions. 🙄

Day 6 (Feb 27) – This is really going to hurt the tourism industry. We need to all support our friends losing jobs now.

Day 7 (Feb 28) -Ok, I think I’ve finally figured out how to wash my hands. 20 seconds seems like a really long time. Is this necessary? Why are some people wearing face masks?

Day 8 (Feb 29 – 1,128 cases)- Just getting back from visiting all my friends, hugging, eating, drinking, traveling, everything is normal.

Day 9 (Mar 1) – A lot of gossip, I heard there was someone in my city with it. It hasn’t been confirmed but I know someone who knows someone who works at a hospital.

Day 11 (Mar 3 – 2502 cases)

Day 12 (Mar 4) – All schools are closing. Should I close my school? But what about our St. Patrick’s day party? What about our plans this week? I’ve already scheduled everything. We need to meet about the Ireland trip this summer!

Day 13 (Mar 5) – A scramble to organize distance learning. Training staff over conference calls how to work remotely. Collecting things from the office to bring home.

Day 14 (Mar 6 – 4636 cases)

Day 15 (Mar 7) – Everyone you know is worried that their cold isn’t a cold. Coughing in public is frowned upon.

Day 16 (Mar 8 ) – Acceptance. This is happening. Many people are working from home. More offices are sending their workers home. More people are wearing masks and gloves in public. There are more cases reported, more fatalities, and now people closer and closer to home. This is real. We need to be part of the solution.

Day 17 (Mar 9 – 9,172 cases) – Total nationwide shutdown. #iostoacasa is trending (#ImStayingHome). You can leave your house for work, groceries or health reasons. Bars and resturants are open from 6am-6pm. No congregating on the streets. Maintain social distancing, one meter apart.

Day 19 (Mar 11) – Even more total shutdown. No more bars and resturants. All retail workers are out of work. Grocery stores and pharmacies can stay open as well as employees working on production and supply chains.

Day 20 (Mar 12 – 15,113 cases) – Cabin fever starts, but settling into remote working. More chatting and connecting with others over the phone and internet. Gosh I hope my phone doesn’t break.

Day 21 (Mar 13) – Scrolling through Facebook and realizing, most of my friends in the UK, US and Australia are on day 4.

As of March 12 there over 15,000 cases and over 1000 deaths in italy. 1,258 people have “recovered” and there are still 12,839 active cases of which 1,153 are critical, requiring hospitalisation with a ventilator.

It’s day 72 in China.

A coronavirus cautionary tale from Italy: Don’t do what we did

I still am bothered by the data out of China. Even with new cases numbering only in the hundreds since about the third week of February, The case fatality rate is still climbing. That’s distressing to me. The implication is that many people are not getting better and spending considerable time in ICU beds. Since the chance that we will build hospitals in a week here is zero, this is more evidence that we are going to have a hospital bed crisis in just a couple of weeks. The only question is where will this happen first? I’m leaning toward NYC, LA, or Seattle.

I know this is a hot button topic. I know that it will cause chaos for every business and will have disproportionate social impact. However, there seems to be pretty clear benefit when closures are put in place for at least 8-12 weeks. Of course there will be trade offs. I don’t even want to try to do a cost benefit analysis on this because I know I will miss something important because it’s more for a sociologist. However, from a disease spread standpoint, we have to bite the bullet and the sooner we do, the better.

Instead of a lot of commentary, I’ll just link to the English literature reviews I found on the topic. The primary research can be found from there. Share this with your governors, mayors, school boards, and superintendents. These are hard decisions but the conversation needs to happen. Lives are at stake.

(2009) Closure of schools during an influenza pandemic

(2014) The Effects of School Closures on Influenza Outbreaks and Pandemics: Systematic Review of Simulation Studies

(2018) School closure during novel influenza: A systematic review

We really needed to act on this a few weeks ago.

I wrote before urging people not to purchase masks for personal use. I am urging again, please leave these supplies for healthcare workers on the front lines. They are going to run out quickly.

Worse, guess where 90% of the surgical masks that are used are manufactured – China.

This supply problem is going to only get worse. I beg you. Don’t be selfish. We need to address this as a community.

Also, please read the first comment below. This is very telling about what is likely happening in many clinic settings.

I’m going to explain what frightened me when I saw it last night. Maybe you can see it from the front page of the Johns Hopkins data. Ignore the green line in the middle. The cumulative incidence of cases in China is in orange, the rest of the world is yellow.

I want to illustrate the steps I used to reach this conclusion because I think it’s important and would be more than happy to learn of any flaws in my methodology or reasoning.

My first step was to plat the number of cases in Mainland China (orange) versus the rest of the world (blue). Second, I found data points with close to the same number of cases for each (labeled).

The next step was to count the number of days of data for China up to that point. The reason is to show both of these curves on the same time scale to make them roughly equivalent measures. In addition, I eliminated a number of the early cases because I want to analyze the patterns in the climb of cases. I looked for a value near 5000 cases as a starting point and they both had values at the same point that were close.

Warning: Statistics Ahead – but it’s important. I’ll try to explain this as clearly as I can.

There is a variable known as r-squared (R² ). This variable measures how well a series of data points fit a particular model. The value ranges from 0-1. A perfect fit is 1, a totally random set of data is given a zero. Most data sets never get close to either extreme. The example below shows two data sets with the best fit line (this is also technically referred to as a linear regression). The image on the left represents a good fit, so the R² value would be considerably closer to a value of one than the one on the left.

In this case, I want to use that statistic to find if a linear regression (line) or an exponential regression (curve) fits the data points better. I’ve colored the linear models and associated R² values blue and the exponential models and associated R² values red.

For the China models, clearly the linear model is a little better, but I don’t recall any measure of a statistic to test if the difference between R² values is statistically significant. That is simply a measure of how well does chance account for the differences between the two models. In this case, I’m simply going on instinct that they are pretty close, and maybe there’s a little bit of each.

The model that excludes the data from China is much more interesting to me. There is an incredibly tight fit to an exponential model compared to the linear one.

I’ll make my argument from the extreme case. Assume that the model for China were a perfect fit for a line. That would mean that the reproduction rate (R₀) would be at the lower end of the range between 1 and 2. For the rest of the world, that would mean that the reproduction rate (R₀) would be closer to 2.

Here’s why that is important. If the reproduction rate is a perfect one, then the cumulative number of cases would follow this pattern: 1, 2, 3, 4, 5, 6, 7…and so on. If the reproduction rate were perfectly a 2, then the cumulative number of cases would follow an exponential growth pattern like this: 1, 2, 4, 16, 32, 64, 128, 256, 512, 1024…etc.

Obviously in the real world, values like that never would be that extreme. However, I think it is important to see that COVID-19 is exploding in the rest of the world much faster than it did in China. My hunch is that it is a function of the quarantine that was put into place. One way to find some support for that would be to look at the same kind of curve for South Korea and see if the regression would be more linear or exponential in nature.

Once again, I truncated the data at the beginning for values below 500 and again for the past three days of data since the number of new cases is leveling off. What I really want to know is the best model during the growth period. Once again, the linear model is a much better fit.

My conclusion is that quarantine (the extreme end of social distancing) seems to work well, which isn’t a surprise. It should serve though as a warning that we should be taking much more aggressive containment measures during this second wave of the pandemic than we currently are.

The global situation does not look good. We reached a new threshold today of 5000 new cases in a single day. It may look like that has happened previously, but the two times in early February were likely due to delayed reporting and the big spike in mid month was to a change in how China was diagnosing cases.

Italy is not promising either. I’ve dramatically emphasized the case fatality rate line. I am suspecting that this is climbing because they have run out of medical resources, which I expect to be the case in many locations eventually. France, Germany, and Spain each have about 1500 cases each now as well.

We can learn some very important lessons from China and South Korea. Quarantine works. China took the brute force approach, while South Korea is using a high-tech approach using smartphones. Compared to what is happening in other countries, quarantine, banning events, and closing schools seems like the correct route to go.

There is no gentle way to put this. The longer we wait to take similar measures, the worse off we will be and the more deaths we will incur in the US. It’s time to put these same measures in place for the reasons I illustrated yesterday in my opinion. I expect to see that happen in Washington state very soon.

I suspect that we will have shortages of PPE as this unfolds. I would like to point people to a position paper that I was a secondary author on regarding the reuse of respirators. It’s time to start thinking through how we will need to manage scarce resources.

http://www.apic.org/Resource_/TinyMceFileManager/Advocacy-PDFs/APIC_Position_Ext_the_Use_and_or_Reus_Resp_Prot_in_Hlthcare_Settings1209l.pdf

I’ve heard numerous people saying that the flu is worse or that this will just be like a bad influenza season. I’m going to try to dispel this notion. It’s a false equivalency.

1. The Reproduction Rate (R₀)
One way to put this in perspective is to review the R₀ for all of the influenza pandemics since 1918. This was done in a meta analysis of multiple studies.

1918: 1.80
1957: 1.65
1968: 1.80
2009: 1.46

Since the argument is being made that it is simply similar to a bad influenza season, the best value to compare it to is the median found in 24 studies looking at seasonal influenza epidemics, 1.27. That’s the best value to use for a comparison.

One very valuable unintentional laboratory to calculate the value for COVID-19 was the Diamond Princess cruise ship. The researchers calculated the R₀ value as 2.28. The WHO estimate is 2.0-2.5.

2. Susceptibility
Since influenza circulates widely around the world, most populations have had some exposure in prior years. “There may be some level of immunity existing in populations even to novel influenza strains with pandemic potential.” In addition, influenza vaccination hovers around 40% each year in the US, meaning that a large proportion of the population has some immunity. In addition, others who weren’t vaccinated benefit from herd immunity.

3. Case Fatality Rate (CFR)
Seasonal influenza has a CFR of 0.1%. My calculations using the Johns Hopkins data set at the time of this writing at 3.52% globally, but there is a large range across the nations I’ve been tracking so far:

Iran: 3.31%
Italy: 5.05%
South Korea: 0.61%
USA: 3.64%
China: 3.85%

Granted, these numbers are possibly higher than the true figures assuming we do not have good data on the numbers of people with mild illness or are asymptomatic. However, other countries have tested much bigger proportions of their populations than the US.

There is something very important here to note though. Influenza testing also is likely to miss large number in the denominator. That effectively means that the CFR for seasonal influenza may just as likely be lower as it is for COVID-19.

There are three reasons identified by the CDC that the number of influenza deaths may be inaccurate:
1. The sheer volume of deaths to be counted
2. The lack of testing (sound familiar?)
3. Different coding of deaths

4. The Tip of the Iceberg
The other piece that doesn’t make sense in trying to draw this comparison is that influenza is already common all over the world. We have decades of influenza data to calculate annual mortality figures. COVID-19 is an emerging disease starting about November (using genetic calculations) and is only just making it’s way into populations and has exposed a very small proportion of humanity. For example, let’s assume that 10 million people have been exposed (a very high overestimate in my judgement at this time), that would mean that only 0.13% of the global population has been exposed to this disease. To me, this is the most important reason that these diseases cannot be compared at this time.

If you hear people trying to draw this comparison, please send them a link to this and dispel this myth.

I found another way to show the difference since I first wrote this.