Special Update 15/03/2020 Covid-19. An Exponential Threat.

Baltic Dry Index. 631 -02   Brent Crude 33.85 Spot Gold 1530

Covid-19 Cases 29/2/20 World 85,206   Deaths 2,923 (Maybe.)

Covid-19 Cases 07/3/20 World 102,350 Deaths 3,497 (Maybe.)

Covid-19 cases 15/3/20 World 158,736 Deaths 5,954 (Maybe.)

Figures often beguile me, particularly when I have the arranging of them myself; in which case the remark attributed to Disraeli would often apply with justice and force: "There are three kinds of lies: lies, damned lies and statistics."

Mark Twain,

Today, how Rome, Paris, and Madrid got the Sars-Cov-2 virus so wrong. Why London, Washington and Ottawa are making the same mistake. GB, the USA and Canada still think by delaying hard choices they will escape Italy and Spain’s fate. But there is nothing new under the sun.

Our great global closure is just getting underway. Two weeks ago we had 85,000 known global cases, today about 157,000 known cases. Next weekend? How many unknown cases? How long before known global cases hits the million mark?

Too little to late! Our markets haven’t seen anything yet.

"Covid-19 Is An Exponential Threat" - Why Global Politicians & Business Leaders Must Act Now

Sat, 03/14/2020 - 00:05

Authored by Tomas Pueyo via Medium.com,

With everything that’s happening about the Coronavirus, it might be very hard to make a decision of what to do today. Should you wait for more information? Do something today? What?

Here’s what I’m going to cover in this article, with lots of charts, data and models with plenty of sources:

·         How many cases of coronavirus will there be in your area?

·         What will happen when these cases materialize?

·         What should you do?

·         When?

 
When you’re done reading the article, this is what you’ll take away:
 

·         The coronavirus is coming to you.

·         It’s coming at an exponential speed: gradually, and then suddenly.

·         It’s a matter of days. Maybe a week or two.

·         When it does, your healthcare system will be overwhelmed.

·         Your fellow citizens will be treated in the hallways.

·         Exhausted healthcare workers will break down. Some will die.

·         They will have to decide which patient gets the oxygen and which one dies.

·         The only way to prevent this is social distancing today. Not tomorrow. Today.

·         That means keeping as many people home as possible, starting now.

As a politician, community leader or business leader, you have the power and the responsibility to prevent this.

You might have fears today: What if I overreact? Will people laugh at me? Will they be angry at me? Will I look stupid? Won’t it be better to wait for others to take steps first? Will I hurt the economy too much?

But in 2–4 weeks, when the entire world is in lockdown, when the few precious days of social distancing you will have enabled will have saved lives, people won’t criticize you anymore: They will thank you for making the right decision.

Ok, let’s do this.

1. How Many Cases of Coronavirus Will There Be in Your Area?

Country Growth

The total number of cases grew exponentially until China contained it

----China

Source: Tomas Pueyo analysis over chart from the Journal of the American Medical Association, based on raw case data from the Chinese Center for Disease Control and Prevention

This is one of the most important charts.

It shows in orange bars the daily official number of cases in the Hubei province: How many people were diagnosed that day.

The grey bars show the true daily coronavirus cases. The Chinese CDC found these by asking patients during the diagnostic when their symptoms started.

Crucially, these true cases weren’t known at the time. We can only figure them out looking backwards: The authorities don’t know that somebody just started having symptoms. They know when somebody goes to the doctor and gets diagnosed.

What this means is that the orange bars show you what authorities knew, and the grey ones what was really happening.

On January 21st, the number of new diagnosed cases (orange) is exploding: there are around 100 new cases. In reality, there were 1,500 new cases that day, growing exponentially. But the authorities didn’t know that. What they knew was that suddenly there were 100 new cases of this new illness.

Two days later, authorities shut down Wuhan. At that point, the number of diagnosed daily new cases was ~400. Note that number: they made a decision to close the city with just 400 new cases in a day. In reality, there were 2,500 new cases that day, but they didn’t know that.

The day after, another 15 cities in Hubei shut down.

Up until Jan 23rd, when Wuhan closes, you can look at the grey graph: it’s growing exponentially. True cases were exploding. As soon as Wuhan shuts down, cases slow down. On Jan 24th, when another 15 cities shut down, the number of true cases (again, grey) grinds to a halt. Two days later, the maximum number of true cases was reached, and it has gone down ever since.

Note that the orange (official) cases were still growing exponentially: For 12 more days, it looked like this thing was still exploding. But it wasn’t. It’s just that the cases were getting stronger symptoms and going to the doctor more, and the system to identify them was stronger.

This concept of official and true cases is important. Let’s keep it in mind for later.
The rest of regions in China were well coordinated by the central government, so they took immediate and drastic measures. This is the result:

----Washington State

You’ve already seen the growth in Western countries, and how bad forecasts of just one week look like. Now imagine that containment doesn’t happen like in Wuhan or in other Eastern countries, and you get a colossal epidemic.

Let’s look at a few cases, such as Washington State, the San Francisco Bay Area, Paris and Madrid.

----Washington State is the US’s Wuhan.The number of cases there is growing exponentially. It’s currently at 140.

But something interesting happened early on. The death rate was through the roof. At some point, the state had 3 cases and one death.

We know from other places that the death rate of the coronavirus is anything between 0.5% and 5% (more on that later). How could the death rate be 33%?

It turned out that the virus had been spreading undetected for weeks. It’s not like there were only 3 cases. It’s that authorities only knew about 3, and one of them was dead because the more serious the condition, the more likely somebody is to be tested.

This is a bit like the orange and grey bars in China: Here they only knew about the orange bars (official cases) and they looked good: just 3. But in reality, there were hundreds, maybe thousands of true cases.

This is an issue: You only know the official cases, not the true ones. But you need to know the true ones. How can you estimate the true ones? It turns out, there’s a couple of ways. And I have a model for both, so you can play with the numbers too (direct link to copy of the model).

First, through deaths. If you have deaths in your region, you can use that to guess the number of true current cases. We know approximately how long it takes for that person to go from catching the virus to dying on average (17.3 days). That means the person who died on 2/29 in Washington State probably got infected around 2/12.

Then, you know the mortality rate. For this scenario, I’m using 1% (we’ll discuss later the details). That means that, around 2/12, there were already around ~100 cases in the area (of which only one ended up in death 17.3 days later).

Now, use the average doubling time for the coronavirus (time it takes to double cases, on average). It’s 6.2. That means that, in the 17 days it took this person to die, the cases had to multiply by ~8 (=2^(17/6)). That means that, if you are not diagnosing all cases, one death today means 800 true cases today.

Washington state has today 22 deaths. With that quick calculation, you get ~16,000 true coronavirus cases today. As many as the official cases in Italy and Iran combined.

----France and Paris

France claims 1,400 cases today and 30 deaths. Using the two methods above, you can have a range of cases: between 24,000 and 140,000.

The true number of coronavirus cases in France today is likely to be between 24,000 and 140,000.
Let me repeat that: the number of true cases in France is likely to be between one and two orders or 
magnitude higher than it is officially reported.

Don’t believe me? Let’s look at the Wuhan graph again.

More

https://www.zerohedge.com/geopolitical/covid-19-exponential-threat-why-global-politicians-business-leaders-must-act-now

Finally for the geeks among us who understand Greek notation.

Exponential family

----Exponential families have a large number of properties that make them extremely useful for statistical analysis. In many cases, it can be shown that only exponential families have these properties. Examples:

• Exponential families have sufficient statistics that can summarize arbitrary amounts of independent identically distributed data using a fixed number of values.
• Exponential families have conjugate priors, an important property in Bayesian statistics.
• The posterior predictive distribution of an exponential-family random variable with a conjugate prior can always be written in closed form (provided that the normalizing factor of the exponential-family distribution can itself be written in closed form).^[c]
• In the mean-field approximation in variational Bayes (used for approximating the posterior distribution in large Bayesian networks), the best approximating posterior distribution of an exponential-family node (a node is a random variable in the context of Bayesian networks) with a conjugate prior is in the same family as the node.^[7]

More

https://en.wikipedia.org/wiki/Exponential_family

Exponential distribution

---The exponential distribution occurs naturally when describing the lengths of the inter-arrival times in a homogeneous Poisson process. 

The exponential distribution may be viewed as a continuous counterpart of the geometric distribution, which describes the number of Bernoulli trials necessary for a discrete process to change state. In contrast, the exponential distribution describes the time for a continuous process to change state.

In real-world scenarios, the assumption of a constant rate (or probability per unit time) is rarely satisfied. For example, the rate of incoming phone calls differs according to the time of day. But if we focus on a time interval during which the rate is roughly constant, such as from 2 to 4 p.m. during work days, the exponential distribution can be used as a good approximate model for the time until the next phone call arrives. Similar caveats apply to the following examples which yield approximately exponentially distributed variables:

• The time until a radioactive particle decays, or the time between clicks of a Geiger counter
• The time it takes before your next telephone call
• The time until default (on payment to company debt holders) in reduced form credit risk modeling

Exponential variables can also be used to model situations where certain events occur with a constant probability per unit length, such as the distance between mutations on a DNA strand, or between roadkills on a given road.

More

https://en.wikipedia.org/wiki/Exponential_distribution

“Reports that say that something hasn't happened are always interesting to me, because as we know, there are known knowns; there are things we know we know. We also know there are known unknowns; that is to say we know there are some things we do not know. But there are also unknown unknowns- the ones we don't know we don't know.”

Donald Rumsfeld

This weekend’s musical diversion.  Bach rearranged Vivaldi’s violin concerto for the keyboard.

Bach: Concerto for 4 pianos BWV 1065 III. Allegro (D. Fray, J. Rouvier, E. Christien, A. Vigoureux)

https://www.youtube.com/watch?v=Di2k06uNU1U 

The monthly Coppock Indicators finished February

DJIA: 25,409 -75 Down. NASDAQ: 8,567 +171 Up. SP500: 2,954 +133 Up.

A mixed bag. But given the severity of the still growing coronavirus crisis, I wouldn’t follow technical signals in what I think will turn into the first depression since the 1930s. Barring a miracle recovery in all three markets, the monthly Coppock indicators are heading for a reversal.