Note: this page is being updated continuously throughout the day. Check back soon for additional states and countries.
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. 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.
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
Dr. Frank’s Commentary the “journey” of these models
In retrospect, one could argue that it might have been nice to have this entire process prepared in advance. But then, the thousands of you who have joined me on this journey would not know how to think about the really accurate results we are seeing.
Think about it. Say I showed up today with my models, and I said, “Ta dah! Here is the answer.” You would just chalk me up with the rest of the so-called “experts” you hear elsewhere every day. Especially since they don’t align with most of the noise that is out there.
But because you have traveled with me on this journey, you understand. You know my heart, you know my intentions, and you *understand* why the results are so valuable. They are credible because you went through the process with me.
The world renowned mathematician from Oxford, John Lennox, says something similar about Jesus Christ. Imagine that Jesus suddenly came down from heaven and said, “Here I am. I am God. Serve me.” And then left. We’d chalk it up to another wacko. I would.
But that isn’t what Jesus did. This is what He did:
“Christ Jesus, who, being in very nature God, did not consider equality with God something to be held onto; rather, he made himself nothing by taking the very form of a servant, being made in human likeness.
And being found in appearance as a man, he humbled himself by becoming obedient to death—even death on a cross!
Therefore God exalted him to the highest place and gave him the name that is above every name, that at the name of Jesus every knee should bow, in heaven and on earth and under the earth, and every tongue acknowledge that Jesus Christ is Lord, to the glory of God the Father.” (from Philippians chapter 2)
Dr. Frank’s Commentary on “Modeling Good Controls”
Covid-19 “Scientific Controls”
A key principle in designing a scientific experiment is the use of controls. The idea is to hold everything constant except for one or two variables, then explore the effect of those variables.
One of the controls I’ve place on myself in this process is reporting. I give myself some lattitude on stream of thought posts, but once something is up, I try to leave it that way (typos and computer glitches excepted). It keeps me accountable.
Another control is access to news and media. I’ve mentioned it several times… I am doing by best not to look at anything other than data, in order to remain objective. No radio, not politics, no television for over a week. It’s been easy… you folks keep me busy.
One of our friends here just private messaged me to let me know that Fauci at the national press conference is starting to sound like me. That is awesome. It means that now you have at least TWO sources, that are independent of one another, converging on the same conclusions.
A few days ago a health Nobel prize winner came out with a prediction that matched mine. That is a THIRD independent source. I’m looking forward to reading that article, but I’m not allowed until after this is over. I don’t want to poison my creativity and objectivity.
I have deliberately have not listened to him, but I’ve noticed in your comments that Trump’s timeline is matching mine. That is a fourth independent source (only 3 1/2 because folks in press conference are on his team).
When things begin syncing up like this… just like our state models… we have increasing confidence that we are on the right track. If we did not use scientific controls we would not have this opportunity.
It ain’t science without good controls.
Latest World and USA Model:
(Note: Click on the heading to get a page of past charts for that heading)
Not all the states are updated, but the trend looks great, America! Every state listed has been updated within the last 24 hours at least once.
Just added the data.
“But Dr Frank. How do you know the curve is going to bend over?”
I know a fork when I see it, even when it is partially covered by a napkin.
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.
Dr. Frank answers the question, “Why will the cases peak rather than continue exponentially?”
Covid-19 “Quick Look at Australia”
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.
Covid-19 “Italy Model Update”
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.
Covid-19 “Model Update for the UK”
Just added data, forecast is working. Notice that the deaths curve is tracking well too… a good sign our British friends have this under control. Very re-assuring. When I smooth the data, it fits the forecast really well. Hopefully, UK is at its worst.
Covid-19 Model Spain
It looks like the “Quick Look at Spain” intial assumptions are not going to pan out. The first graph is just adding the data, showing how the initial assumptions have failed.
The second graph is the adjusted model. When I adjust the number of cases up to match the reported values, the number of deaths reconciled on the nose. A good sign that most of the early assumptions were on the mark.
So it is looking like our Spanish family had a rampant Covid-19 infection that no one knew about. And now, that will have to run its course, it can’t be stopped, and the numbers are not good. Of course, they must try to contain it from spreading further.
Pray hard for Spain, they are in for some very rough days.
Covid-19 Model for Cambodia
Just added data. Looking good. Even though I want to adjust this model down, I am going to leave it, because I suspect this is going to be what it looks like when a country is quickly finding and containing spot infections.