Note: this page is being updated continuously throughout the day. Check back soon
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Publisher’s Note: On March 23 we began posting a series of charts by Dr. Douglas G. Frank. We continue that series today, as we will each day until the model proves unsustainable or the crises diminishes. Dr. Frank’s work is part of a long-line of thinkers who look at data, analyze the data into statistics, and use the results to make informed decisions. Our prayer is that it provides comfort and insight.

Dr. Douglas G. Frank

Dr. Frank received a B.A. in Chemistry from Westmont College in Santa Barbara, California. He qualified for his doctorate at the University of California, Santa Barbara before transferring to the University of Cincinnati in 1986 as part of the Ohio Eminent Scholar program. In 1990, he received a Ph.D. in Surface Analytical Chemistry. After graduating, he formed “ADAM Instrument Company, Inc.,” named for the new surface analysis technique he discovered during his graduate studies. The “ADAM” technique brought him international acclaim, and his work was featured in several scientific books and international journals, including cover articles in Science and Naturwissenschaften. He has over 50 scientific publications, and is internationally regarded as an expert in Auger spectroscopy.

Dr. Frank currently serves as the Math and Science Chair at the Schilling School for Gifted Children in Cincinnati, OH. He is also the President of Precision Analytical Instruments.

All words below this line are compiled from Dr. Frank. Other than ordering and organizing the comments and the charts, we (DPH) have not edited any of his comments because that would be out of our area of expertise. The comments were given in a social media context, so they have a friendly, conversational tone.

Models are important!  Have you seen this?   Epidemiologist Behind Highly-Cited Coronavirus Model Admits He Was Wrong, Drastically Revises Model

For State graphs, click the state below: [This work is in progress, check back if your state is not linked]

Alabama     Alaska     Arizona     Arkansas     California.    Colorado     Connecticut     Delaware     Florida     Georgia     Hawaii     Idaho    Illinois
Indiana     Iowa     Kansas     Kentucky     Louisiana     Maine     Maryland     Massachusetts     Michigan     Minnesota     Mississippi     Missouri
Montana     Nebraska     Nevada     New Hampshire     New Jersey     New Mexico     New York     North Carolina     North Dakota     Ohio    Oklahoma     Oregon     Pennsylvania     Rhode Island    South Carolina     South Dakota.    Tennessee    Texas     Utah     Vermont.    Virginia.    Washington     West Virginia     Wisconsin     Wyoming

Models: useful things those!

In the early 90’s I was invited to participate in a special week long conference. Basically, 200 scientists in the same field, constructively ripping into each other’s work, sharing ideas, and designing collaborations. It is exhausting. So the organizers of the meeting would arrange evening sessions that were deliberately off-topic to keep our minds fresh for the following day. The sessions were naturally fun for geeks.

One speaker was the director of sewage management for the State of California. (Yes, off topic.) He said that their biggest waste management problem was daily vitamins. These vitamins would clog the various filters and jamb the machinery, and they had to take semi loads full every day to the dump. Can you imagine? People swallow a vitamin pill in the morning. It goes through their digestive track, etc… into the toilet, through the sewers, survives waste treatment fermentation, and you can still read the label on the pill. That is a very effective vitamin! In science, we call that a “placebo.” And they work surprisingly well. People are vulnerable to advertising hype.

The speaker the next night was the director of NOAA. He was describing the latest weather models running on massive supercomputers. He said, if we simultaneously knew the temperature, the pressure, and the velocity vector for every cubic meter of the atmosphere, that we could predict the weather with 50% accuracy for two weeks. But… it would take two weeks to calculate. So if you wanted a forecast, it would be much easier to just peek out the window.

Since then, of course, computers have dramatically increased in capacity. But then why are they still so marginal at predicting the weather? They’re pretty good three days out, but after that the forecasts become quite unreliable. What is wrong with the computer models? Actually, nothing at all. But because we don’t know at a particular moment in time precisely what the parameters are for every cubic meter of atmosphere, we have to make good approximations of what those values are likely to have been by tracking the weather for a few days, then back-calculating what those parameters must have been at the start. Then we forecast forward, constantly revising what the earlier parameters must have been in order observe what is actually occurring. The models are great… they just lack good and complete input information at the start. Sound familiar?

Nowadays, most weathermen just recognize the patterns and make correlations to similar conditions in the past. They have great new satellite and radar tools at their disposal, and they get pretty good at it. Experience in their regions really helps. I can look at the national radar site and tell you quite reliably when the storms are going to arrive.

Models. Useful things, those.

Dr. Frank’s Commentary on “Modeling Good Controls”

From Dr. Frank: A Common Concern

A common thread today: “I’m concerned about our economy and our recovery after this.”

I’ve seen this concern expressed in several of the comments. I get it. We’re all concerned. My thoughts on this are just as valid as yours, but putting them here once saves me typing them eighteen more times… lol

OK. Trump has strengths and weaknesses. Me too. But one of his weaknesses is also one of his strengths; we know who the guy is… he is always letting us know what is on his mind. Like it or not, you know what he is thinking.

I would stipulate that Trump has more ego than any president in my lifetime. I clearly remember Carter, Reagan, Bush, Clinton, Bush, and Obama. I’d say Trump’s ego exceeds the egos of these predecessors combined.

And ya know what? I’m glad. That makes him the *perfect* person for this recovery. He has more business experience than any of these predecessors, and he genuinely loves America. You can disagree with his policies, but you can’t deny that he loves America.

But Trump also wants a recovery, a big one, because his ego is on the line. He really wants to be the hero for all of us. And I’m good with that. You go, President Trump. Be the hero. I’m behind you. We need a hero right now. Be that guy. And we’ll build a statue for you after you pull it off. And you will have earned it.

We’re Americans, and we rock. When we come together, we are unstoppable. Divided… not so good. Let’s unite under our elected leader. I’ve not listened to any media for some time, but I hear that Trump is talking about timelines that line up with ours here. That is great news.

This is a unique and remarkable time across the world. It is a good thing that our country started so strong going into it. Other countries were already suffering economically, and this will devastate them. But not us. Just watch us. I know it in my gut. Not so much math.

And may God richly bless us, as we endeavor to do “whatever is true, whatever is noble, whatever is right, whatever is pure, whatever is lovely, whatever is admirable — if anything is excellent or praiseworthy — think about such things and the God of peace will be with you”

Latest World and USA Model:

(Note: Click on the heading to get a page of past charts for that heading)

March 27:

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Covid-19 “US Deaths Tracking”

Leaving the last post up for accountability.

Frank Giordano noticed a discrepancy in my data, so I went back and reconciled. My totals were correct, but the daily numbers were off. Usually they settle by the next day (when I typically reconcile), but apparently some folks made some older reconciles as well. It’s an issue with the world reporting numbers with GMT midnight as the cutoff, but the US reporting at various times. Somebody out there has a headache.

Here are the two reconciled graphs.. one smoothed, one not.

My intial gut is that New York needs to be a sharper peak. But I will come back to this later. Right now, people are sending me state updates in a flurry…

Further note about this chart, from Dr. Frank:

This graph is really revealing. And more importantly, it confirms an hypothesis we had a couple days ago. Cha-ching!

I have changed nothing about any of the models. Think of this as merely “accounting.” This is the first time I have ever looked at this plot, because I just made it. I’ve been stewing over the right way to do this for a couple days, and it was the first thought on my mind when I woke up. (The power of the subconscious mind…)

Let me walk you through it. I usually don’t like putting so many traces on a single graph for public viewing. It scares people off. But it is worth it, trust me. Take your time, digest each plot. Look at the scale for it, and don’t move on until you get it intuitively.

Let’s start with the axes. The left vertical axis is for all “Deaths/day” curves, the right vertical axis is for the two “sigmoids,” or total death curves that finish into it. (Near 1400 and 2900.)

Let’s start with the red dashed plot with the red dots, USA Reported deaths per day. The scale for this is in red, on the left. It is real data, so it is noisy. But it is reality. (Always start with the DATA!!)

The pink shaded line that is tracking the red-dots-curve is the sum of my original projection for the whole USA (deaths/day) plus the model predictions for NY (deaths/day). This “sum of models” is closely tracking what we are **actually** observing. (It is the sum of the light gray and blue peaks.)

Next, the solid dark gray line is my original model death tally estimate for the whole country (finishing on the right, near 1400 total death tally). The light gray peak is the cases/day for that model (use left scale).

The light blue curve is the peak corresponding to the deaths/day predicted by our NY model.

When you add the original model to the NY model, you get the dark red plot, finishing just under 3,000 total deaths.

The dark red dots and dashed curve is the total reported death tally for the entire US (actual data). Note the “inflection point” in the curve corresponding to when the NY death peak starts growing and the trace begins diverging from my earlier estimate. (This is exactly what a “secondary infection looks like in other countries, eg Italy.)

I am not saying NY is a secondary infection. I’m saying, when you consider the data and models separately *and* together, this approach provides valuable insight into what is going on in our country.

I will update this graph every day for a while. I might simplify it too… but there is so much good information in here, I don’t want to leave anything out. I will think on it.

What I love about this, is that we hypothesized a couple of days ago that this way of thinking about the country would match the data… AND IT DOES. An a priori hypothesis confirmed, making us more confident in our models. This is the scientific method.

It also reveals that my original assumptions (and Midwestern bias?) about our country were likely incorrect, and that I will need to revise my assumptions. Note that I am being careful to let the data guide my thinking. Make an hypothesis, then test it. Learn. The scientific method works.

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Worldwide Models

Note: for worldwide models we are only adding the latest graphs. We are not keeping updated records and comments as we are doing for the states.

Covid-19 “Quick Look at Australia”

Covid-19 “Quick Look at France”

Just added data. Uh, oh.

cases + deaths = new infection

Pray for France.

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Covid-19 “Model Update for S Korea”

Looks like the secondary infection in that rest home in S Korea is still playing out. But the cases plot is leveling, so they are on track for it to be over soon. Just added new data. Didn’t change the model at all, leaving it so you can see the secondary infection play itself out relative to the model we set up a month ago.

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Covid-19 “Italy Model Update”

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Covid-19 “Quick Look at India”


Covid-19 “Model Update for Iran”

Rats. Been nailin’ this for weeks. This is the first time in some time that the deaths curve has not been spot on. Looks like they have a secondary infection, especially with the recent rise in reported cases. In a couple days we will be able to forecast how bad it is. Unlike S Korea, these are large numbers. So it not likely to be merely a single rest home situation. The model said the death tally would be 2077. Reported is 2206. Might end up being a good example why we must be diligent until it is over. And good for us to see this in the USA. Be diligent. It ain’t over, ’til it’s over.

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Covid-19 “Model Update for the UK”

Sounding the alarm. Just added data. Cases up, in tandem with deaths. Time to pray for a small infection.

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Covid-19 Model Spain


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Covid-19 Model for Cambodia

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Covid-19 Model for the Philippines

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Covid-19 Model for the Netherlands

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