Has anyone check hand washing norms in places that have fared better with the virus?

I flipped past MSNBC this afternoon and caught a segment with Chuck Todd talking to guest about receiving packages in the mail and staying safe.

It turns out the 2 month old guidance to wait a day or two before bringing the package in may be overkill.

It also turns out that studies that detected the presence of the caronavirus on surfaces days after contact may have been detecting dead, non-contagious virus, and the virus doesn’t live that long on surfaces.

Makes sense. This sort of lines up with the fact that grocery stores, so far, have not been proven to be big spreaders.

The guest recommended opening the package, disposing the box and then washing your hands thoroughly.

LOL! So, we’ve come full circle. Isn’t that where we started?

We’ve come a long way in a short time. That type of suggestion would have caused  outrage a few days ago.

But, it made me wonder if anyone has taken a deeper look at differences in hand washing culture in areas that have fared better vs. those that haven’t?

I believe I read that Denmark was perplexed to not see cases rise as they re-opened. I wondered if they, perhaps, are strong hand-washers, especially when out in public.

Just a thought.

Revenge on the Nerds III: Covid-19

I really don’t know that this is…

The original:

The collapse of Long-Term Capital Management in the 1998.

Plot summary: Geeks finally use their brains and math to beat the market, but it blows up in the face, nearly taking a good portion of the banking system with it.

Taxpayers save the day.

Subtitle: Beware of geeks bearing models.

Sequel:

2008 Financial Crisis: Beware of Geeks Bearing Models II

Plot Pitch: Long-Term Capital Management was like the Death Star. This movie needs to be bigger! The latest trilogy of Star Wars, the evil empire harnessed the power of a star. We will do something like that.

Plot Summary: Ignoring the lessons of Long-Term Capital Management, the masses again put their trust in the nerds. After all, they have discovered with their brains and math ways to hide the risk of making home loans to cats and dogs.

Let’s bury the whole economy. And, yet again, taxpayers to the rescue.

Second sequel:

2020 Covid-19 Pandemic: The Perfect Storm

The pitch: It’s 12-years later. The masses have mysteriously forgotten the damage caused by the previous generation of nerds who could not, in fact, make dogs and cats pay on their loans, and again, put their trust in them. After all, Stitch Fix’s algorithm now dresses them to look almost cool. If math can make make nerds look cool, then of course it can predict pandemics.

Meanwhile, the media has become so bad at words and math that what they report is quite often something from an alternate universe.

And, the third element of the perfect storm: reasoning has been weakened through a generation of PC culture, which evolved into cancel culture, where the only acceptable approach to disagreement is inspired by Orwell.

Mix those three elements and watch the damage caused be far greater than any one element could have done. It’ll hit the hospitals, the economy and basic human rights all at once!

Cue the heroes, yes taxpayers.

Is there a chance for a third sequel in another 9-10 years? Stay tuned. The writers are still working on the ending for this one.

It’s worth mentioning that a third sequel would be timed just about right to commemorate the 100th anniversary of the prequel to all this: The Great Depression: The Nerds Still Aren’t Sure What Caused This, So Quit Trusting Them!

Pay cuts for Congress?

I might have missed it, but do the CARES or HEROES acts include pay cuts for Congress until unemployment gets under, let’s say, 8-9%?

How we place some moratorium on campaign contributions, too?

A few interesting flu and pneumonia tidbits

A friend asked me to make a chart of H1N1 deaths in the U.S. 2009 vs. covid-19 deaths in 2020. While researching that, I found a few interesting tidbits.

Is covid-19 the first time we’ve ever tracked cases and deaths for a specific illness? It seems all data for H1N1 is estimated rather than based on specific testing data.

I thought it was interesting to learn that H1N1 did not affect those over the age of 65 as much as younger folks, because it is thought that many over that age had a prior exposure to an H1N1 strain long ago, that younger folks had not.

Also, I found some death rates by age charts for regular seasonal flu viruses that looked awfully similar to covid-19.

The estimated number of flu & pneumonia deaths going back to about year 2000 is around 60,000 and there was no spike in this in 2009 with H1N1. I expected there to be a spike that wasn’t there.

But, one of the most interesting things I found was the information in the chart below for flu and pneumonia deaths per 100,000 population going back, for select years, to 1950.

By my estimates, covid-19 in the U.S. is currently over 26 per 100,000 (86,000 deaths / 3,300 (x100,000) population).

What’s striking is how high the flu and pneumonia death rates were as recently as the 1990s.

I wonder why? Have flu vaccines improved or become more widely adopted since the 2000s? Maybe. I don’t recall flu vaccines being as widely available until then.

Do those years provide better baselines for understanding what’s happening now because it better represents with viruses that have little or less prior immunity?

Flu Deaths by Year

Btw…what I found is that the CDC estimates between 8,000 and 18,000 H1N1 deaths in the U.S.in 2009 and about 60,000 deaths from flu and pneumonia causes, which compares to about 86,000 covid-19 deaths over the course of 2 months or so.

But…CDC estimates for flu deaths is always a small fraction, because many flu deaths are categorized as pneumonia because that’s eventually what causes the death.

Also, since the H1N1 was only estimated and not specifically tracked like covid-19, I felt the direct comparison is a bit apples-and-oranges.

Even with those caveats, I told him it’s easy to draw conclusion that covid-19 is, so far, worse, than H1N1, and outside the upper bound of the for typical seasonal cold and flu, with time to go.

I also mentioned, when I look at numbers that represent 0.03% of the population, it’s difficult to separate noise and signal, unless you start getting into half order of magnitude (5x) to order of magnitude (10x) differences.

CDC Deaths by Week

I found the chart I’ve been looking for on the CDC website:

CDC Death Chart

Caveat: The notes of the chart says the last few weeks are estimated.

This shows estimated excess deaths of 53,000 from March 28 to April 25, which lines up closely with estimated covid-19 deaths during that period.

It also shows that 50,000 – 60,000 people normally die each week in the U.S.

More Clay Travis

Clay Travis, a Fox Sports guy, has been one of the best sources of information on coronavirus. Good for Clay. Bad or the rest of media.

More Scott Gottlieb, less Dr. Fauci

Gottlieb provides pertinent, helpful and balanced information that can help folks make good choices and better understand their risks.

Dr. Fauci seems to have some motivation to not discuss positives. At best, he’s being overprotective to manipulate behavior in an overly cautious way. At worst, who knows?

I believe we need fewer folks manipulating our behavior for what they believe is the best for us, because they are often wrong, and more folks telling us the TRUTH.

A Covid-19 Story

covid-story

These are my thoughts on Covid-19, since about mid to late March. They might be wrong. So far, there has been growing evidence for key pieces of this story.

Here’s the story:

Red-line:

This represents total Covid-19 infections in the population. It started much earlier than we think. I’m guessing December, but I wouldn’t be surprised if it was earlier.

I think infections tend to grow to 5-10% of the population and then continue to roll through at about that same rate, driving a fairly consistent case load.

This is the weakest part of my story. I haven’t thought of a good reason why it grows to 5-10% of the population fairly quickly and then levels off. Some reasons might be that infected folks and those around them start taking basic precautions like staying home from work, washing their hands when they’re around sick folks or avoiding them altogether.

But, my observation is that’s similar to how other viruses seem to spread, roll through the population in waves, rather than exponentially. Of course, I’m no epidemiologist, so you can hurl that criticism. I’m just going off previous observations of how I see cold wave through.

This also means that the virus was likely far less deadly than we thought by just looking at tested cases, by a factor of 10 or more.

Evidence to support:

  • Folks who died in January and maybe December did, indeed, are now being tested and shown to have had Covid-19.
  • Antibody tests have been consistently supporting that actual number of cases is far greater (15-80x) higher than testing has identified and that many folks have such mild symptoms that they don’t think of getting tested.
  • H1N1 was also originally thought to be 10-20x more deadly than it turned out to be, based on the fact that early testing didn’t identify every single case. As better estimates of full infection became known, fatality rates dropped to the top-end of flu ranges.

Black-line:

We came along a few months after the virus had already steady-stated and started ramping up testing quickly. There seemed to be the belief that the testing was uncovering the virus as it spread.

I believed that testing was just uncovering infections that were already near steady state in 5-10% of the population at the time of testing.

Think of it like testing to find people who are 6′-4″ or taller. The first day we test 100 folks and find 10 are tall. The second day we test 1,000 folks and find 100 are tall.

Would we conclude that tallness is spreading exponentially? No. We would understand that folks were tall before they were tested, the testing just identified the percent of the population that was tall.

Now, I realize this isn’t apples-to-apples, because viruses do spread, whereas tallness does not (at least not like viruses).

But, the point is that if the infections were present in 5-10% of the population, while testing was growing exponentially, it would be easy to conclude that the virus was spreading exponentially, when it really wasn’t.

Evidence to support:

  • Growth in positive tests mirrored the growth in tests. See this post.
  • Consistent positivity rate of covid-19 tests (also see that post).

After the first week, the testing positivity settled to about 20% for the next month, before dropping as test criteria started getting looser. It was 20%, because tests were only given to folks who thought they might have. So, the 20% would be a high estimate for percentage of total population that doesn’t have it.

So, this would be like if we just gave the tallness test to folks who felt they were pretty tall.

This led me to believe the 20% of the population was the way top-end estimate of percent of population infected at any one time. I guessed the actual rate of infection might be in the 5-10% range (although wouldn’t be surprised if the range was more like 3-15% just depending on local spread factors, like average number of folks in a household, mass transit, weather, etc.).

Blue-line:

The blue-line represents the positive tests. I felt the number of identified cases were likely a fraction of the actual infections (red-line).

It grew with testing — freaking people out. But, leveled off when testing leveled off. Which was somewhat comforting to me. That supported that the virus was more of a steady-stater than an exponential grower.

What about the surge?

A fair question is why did we see a surge in cases in early April? As we are finding out, the most likely way to contract the virus is with close contact and many infections spread within homes.

It wasn’t lost on me that surges in positive tests, hospitalizations and deaths happen to occur 2-3 weeks following stay-at-home orders. I wasn’t the originator of this theory, but thought it had some potential when I first saw it.

I would not find it surprising that the surge was, at least partially, caused by a lot of folks putting themselves in the most highly transmissible environment (home) at about the same time.

I also think another contributor to the surge was hysteria drove infected folks (many who would normally have just gone about their business and recovered in a few days) into hospitals without the hospitals yet having good protocols to reduce the chance of transmission to the folks already in the hospital.

I wrote about on April 15.

Evidence:

So what?

If this is true, this is what I would expect to happen from here…

Cases and deaths will remain steady for some time as the virus works its way through the population.

In places like NYC, it seems like the virus is somewhere between 20 and 30% through the population already, which means we can expect 3-5x the number of cases and deaths to occur at some point in the future. But, it could be less if the virus has a hard time sustaining itself as more folks can fight it off.

In other places in the country, it may still be more like 10-15%.

Nobody knows what the top-end number needs to get to for herd immunity. I’ve read that it ranges from virus to virus from 60-90%, but even that is a fair amount of intelligent guessing from experts.

It makes sense to me that we start to see significant slowing after about 30% of the population, because that has essentially cut your chances of spreading it others randomly by 30%. At 50%, it cuts it in half.

What about future strains and waves?

I expect both will happen, but I don’t know if it will be as rough as the first go around. Simply because as more immune systems see this type of virus, they may have better chances of fighting off future strains.

But, I could totally be wrong.

Numbers without context or bad context = propaganda, marketing or con job

It’s good to see Nate Silver calling out the media’s poor use of context when reporting numbers:

I’ve been driving my family and friends nuts with this complaint since March. Sorry family and friends! But, when you spend a good deal of your life reporting numbers, this stuff tends to get under your skin.

In the first chart in this post of mine, it’s easy to see the close relation between growth in tests given in March and positive cases, and, was rarely that mentioned by media.

Instead, media focused on the growth in positive cases and let folks assume that growth represented the spread of the virus, rather than the spread of testing.

A better stat to watch at the time was the percent of tests that came back positive, the second graph on the post I linked to above. While not perfect (it could be heavily influenced by testing criteria), it at least would have been an indication not to mistake the growth in testing for the spread rate of the virus.

No context or bad context is propaganda, marketing, or a con job. I wrote about it here in this post about lottery marketing. Your antennae should go up when anyone gives you numbers with no or bad context.

Chicken little fatigue

One constant with the coronavirus has been that each new data point is assumed as the worst case scenario.

The latest in that parade came this week as news outlets strongly warned us that some areas are seeing people with possible second infections after having previously recovered.

Turns out, that might a problem with the tests detecting leftover dead virus.