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Michael Levitt Profile picture
@MLevitt_NP2013
May 7, 2020 • 4 tweets • 4 min read • Read on X
"Comparative Analysis of COVID-19". Stanford group of 5 is well-supported by @StanfordMed & @NIH. Three helped part-time: Andrea Scaiewicz @andreascaie, Frédéric Poitevin @fredericpoitev1 , João Rodrigues @jpglmrodrigues . Also Francesco Zonta @Cescoxonta (at @ShanghaiTechUni Image
The app was written in Python by Andrea Scaiewicz advised by João Rodrigues. It is research software allowing comparison of all locations with >50 deaths or >3000 cases (see examples). Response is slow as freely hosted by heroku @heroku. Tested too little, it is likely buggy. ImageImageImageImage
The table of locations is classified by Class score. Columns can be sorted or filtered by a string (eg. "====" with quotation selects worst locations). UNSM is raw data, SMO3 is moderate smoothing & SMO5 is more extreme smoothing.
Enjoy at levitt1.herokuapp.com and feed back to me. I intend to pay for better hosting as needed. Also please suggest alternative places we can host this.

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More from @MLevitt_NP2013

Michael Levitt Profile picture
Jan 16, 2022
Just so Europe does not feel left out, here are some of the larger countries.

All use the same 5.5W sheet
docs.google.com/spreadsheets/d…

Spain 133,000 cases/day at peak on 12-Jan.
docs.google.com/spreadsheets/d…
Italy 181,000 cases/day on peak on 13-Jan.

docs.google.com/spreadsheets/d…
Germany 72,000 cases/day on peak on 20-Jan.

This new Omicron outbreak comes on the heels of previous outbreak (Delta? that peaked 25-Nov-21). Set start to 25-Dec, later that with 13-Dec. start.

Anyone know if outbreaks in different places like Holland
Read 5 tweets
Michael Levitt Profile picture
Jan 16, 2022
USA Omicron has peaked. Daily new cases on the decline.

Open Sheet:
docs.google.com/spreadsheets/d…
Big states looking good too.

California 130K cases/day at peak on 19-Jan.

docs.google.com/spreadsheets/d…
Florida 59K cases/day at peak on 7-Jan.

docs.google.com/spreadsheets/d…
Read 5 tweets
Michael Levitt Profile picture
Aug 8, 2021
As someone who broke the news to Israeli leaders on Sweden’s handling of COVID-19 in Mar. 2020, I am so distressed to be reading this now m.ynet.co.il/articles/hj30k…

When will Homo Sapiens realize that we can never stop a tiny virus & re-engineer human biology by force not smarts?
Please note that the two translation are automatic. I give independent results from Microsoft and Google. The original is in Hebrew.

Taking this opportunity to rejoice in machine translation. It it so worthwhile to get used to its quirks.
Fortunately age-adjusted excess death in Israel for the 75 weeks from 1-Jan-20 to 6-Jun-21 is almost as small as that in Sweden (<2% of natural death in 75 weeks)

Economic, social, medical & educational cost to Israel likely higher than to Sweden.

Does anyone have good data?
Read 5 tweets
Michael Levitt Profile picture
Jul 11, 2021
1/n
 Growth of COVID-19 outbreaks may seems specialized, but is crucially important.

If growing by commonly used Logistic function, early growth is exponential & forecast of outcome is impossible.

For Gompertz function, growth is slowing from start allowing forecast of end.
2/n
 Described on YouTube year ago, this is still not widely understood


Then we focused on small outbreaks (1000's cases) well-contained in New Zealand and South Korea.

Stimulated by @Marco_Piani & @PienaarJm, we now analyze Lompardy, NYC, UK & Spain.
3/n
 Before releasing results & Excel fits, I describe the growth functions and then show raw data.

If you want to learn about growth functions, study the properties of the functions so that you can identify how the real data changes.

I got this wrong from 1-Feb-20 to 20-Mar-20. Image
Read 4 tweets
Michael Levitt Profile picture
May 30, 2021
I am sure that there will be something that correlates with COVID19 death rate better that the stringency of restrictions.

Finding it is important!

The entire world needs to know what to do the next time there is a serious viral threat.
1/8
 Interesting replies led me to carefully analyze data in @youyanggu GitHub table. Alway more objective when one collects data, another analyses it.

Looking at data so first tidy table in Excel.

Color formatting is red-yellow-green low value to high. Image
2/8
 Rather than make plots of one measure against another, we get the correlation coefficient of all pairs of measures.

Correlation coefficient, CC, of A to B is same as CC of B to A so table is symmetric. Correlation coefficient of A to A is always 1; it is whited out here. Image
Read 9 tweets
Michael Levitt Profile picture
Mar 21, 2021
1/7
 Excess death (E) in any period is the difference between the actual all-cause deaths and those that are expected. Expected deaths in the current year, c, can be calculated in many ways. Easiest is to use the data from a few recent years as a reference (we use, 2017 to 2019).
2/7
 Data can be used in 3 ways to calculate expected deaths.
(1) as average death in the reference years.
(2) as average corrected for the change in total population.
(3) as average for each age band corrected for its population, what we call age-adjusted.

We use 5 age bands.
3/7
 (1) If D(i) is death in reference years i, then expected death in year c is E(c)=average[D(i)].
(2) If P(i) is population; E(c)=P(c)*average[D(i)/P(i)].
(3) If (P(i,j) is population of age band j in year i, D(i,j) the corresponding death; E(c,j)=P(c,j)*average[D(i,j)/P(i,j)]
Read 7 tweets

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