Tool for FreeBSD which extracts, curve-fits a logistic function and transposes JHU-CSSE's Covid-19 data by country

That is why I like this place. Someone solves a problem he has, makes that public, and the sure-as-heck to come probing (otherwise known as bickering) of the methods is both civil and on a level most people out there won't get. Great.

W.r.t. Russia, my grapevine sings about them stamping almost all deaths of this bug as pneumonia. That may be technically correct.

One lesson learned from this is to check and heavily control the use of antibiotics in livestock management. The areas with high rates of people dying correlates with high-density animal farming. MRSA is a high cause factor here.
 
my uncle in Sao Paulo swears by the Albert Einstein hospital, which is clearly very good and private.

The two best private hospitals in Brazil are Albert Einstein and Sírio-Libanês, hard to know which one is better (and more luxurious), and a large part (likely the majority) of the doctors in those hospitals, specially the most specialized ones, are also the ones you will meet in the top public ones.

The public ones is where they make their name and the private ones is where they make their wealth. :)
 
I don't have any idea of how many ICU beds are available in the private sector but my father was in an ICU in Rio a few years ago, and they had about a hundred of beds in there, and that wasn't really one of those top private hospitals. Also, in Rio the 'Riocentro' (huge convention and event center) is already set to be converted in a field hospital if necessary.

We still have the military hospitals which are often well equipped, not crowd and the staff is well prepared.
I also tend to believe, that in extremis a lot of resources can be mobilized by joined efforts. I am still optimistic in respect to the outcome of the situation in Brazil.
 
I had the bad luck of spending several weeks doing daily visits to a patient in the ICU of a rural Brasilian hospital (my father). Both times he survived. The care in the ICU was excellent, the doctors well trained, hygiene good, nursing staff plentiful, helpful, and skilled. But in rural Brasil, the number of available ICU beds is low. As I said: Campo Grande has 14 at the Santa Casa (the SUS hospital), and at most another 10 or a dozen at the other three hospitals in town, for a city of 3/4 million people (which is also the supercenter for a state of 2.8 million people (I wrote 4 above, wrong). Of those, 1 (!) is an isolation bed in a separate room. That's WAY below european standards. I just saw in the news that the Uniklinik (university hospital) in Leipzig alone has 14 beds in the isolation ward of the ICU alone, and Leipzig is a medium-size town!

Except Hamburg, Berlin and Munich, all towns in Germany are medium sized, compared to world standards.

Where things fall apart in Brasil is in the non-intensive non-specialty part of the public health system. The regular parts of the government hospital there has serious problems with staffing, medication, supplies (even alcohol for disinfecting and toilet paper is chronically in short supply, we ended up bringing it). Nutrition for the patients is not good, and the expectation is that families station people in the hospital to be with the patient, to supplant the minimal nursing staff. Been there, done that. Not fun.

Hospitals in Dresden and now hospitals in Nordrhein Westfalen were opened for and received already the first patients from northern Italy in order to help out with their capacities. Per Google Maps, Bergamo to Dresden is 951 km and a 10 h drive. Of course this can be done in Brazil and elsewhere as well.

But then, you can't average rural brasil, not even a provincial capital, with Sao Paulo and Rio, which are big, somewhat wealthy, and international cities. With the wealth, they also have way more private health care, whereas in rural Brasil, the public system takes care of much more.

I do not have much experience with hospitals neither in Brazil nor in Germany. My son was born in a private hospital in São Bernardo do Campo and my daughter was born in the pseudo public IAMSP hospital in São Paulo. The private hospital had a nicer designed interior, but I tend to believe that the professionalism of IAMSP is in front. In both cases everything went well and quick, so we neither did appreciate much the design nor were in need of any seriously critical professional skill.

Well, "mentira" (which means lies) may be a bit of an exaggeration. A lot depends on how you define "ICU". In Campo Grande, the moment you need a ventilator, you will be in the ICU, or else you will be dead if all 14 beds are occupied. I know that in Germany, they use ventilators in intermediate rooms (better equipped than regular rooms, but not ICU). So comparing the numbers isn't all that easy. So counting what is an ICU bed or not probably depends on local definitions.

I believe that dedication of the medical staff is as least if not more important than the technical resources. Once there was a joke about a German engineer. "He goes into the jungle having a knife and a tin can, he comes out with a compass on a bicycle."
I can tell you, this time has gone in Germany, nothing, really nothing is on any level at any scale creatively provisional. I think, a lot of people in Brazil are what German engineers were believed to be long time ago. At least, I don't regret it living in Brazil and not in Germany in the moment.
 
Next time you update, if you happen to have graphs for other countries, posting then would be appreciated.

Which countries are you interested in?

Perhaps, in my initial post the Quick Start section was too brief. Now I will show step-by-step how to achieve the data extraction and curve fitting for example for Italy:
  1. Checkout the xcssecovid sources from my GitHub repository:
    svnlite co https://github.com/cyclaero/xcssecovid.git/trunk xcssecovid

  2. Compile the tool:
    cd xcssecovid
    clang -g0 -O3 -march=native xcssecovid.c -Wno-parentheses -lm -o xcssecovid

  3. Fetch the Covid-19 data from the GitHub repository of CSSE at the Johns Hopkins University (repeat this daily):
    fetch https://github.com/CSSEGISandData/COVID-19/raw/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv

  4. Extract, curve-fit to the logistic function and transpose the data of Italy into a .tsv file:
    ./xcssecovid Italy time_series_covid19_confirmed_global.csv Italy.tsv

  5. Look at the produced TSV file. The parameters which resulted from the curve fitting are at the head:
    head Italy.tsv
    Code:
    # Model: a/(1 + exp(-b·(x - c)))# a = 112942 ± 2.7875 %
    # b = 0.199317 ± 1.5662 %
    # c = 60.6516 ± 0.4692 %
    # ChiSqr = 200158.7
    t/d C L
    -20 * 0.011789
    -19 * 0.014390
    -18 * 0.017564
    -17 * 0.021437

  6. Open the TSV file in your favorite graphing application and look at the curves:

    Italy-2020-03-24.png


    In a semilogarithmic plot, it becomes more apparent that we already left the exponential and entered the logistic regime:

    Italy-2020-03-24.log.png
The percentages behind the resulted parameters are estimated standard deviations. This means that the probability that the predicted result within the range of ±std.-dev. will actually occur is 68.3 %. In a range of ±3·std.-dev. the probability would be 99.7 %. Currently the 99.7 % prediction of the outcome of the number of cases in Italy in 2 weeks would be from 109493 - 3·2.79% = 100328 to 109493 + 3·2.79% = 118658. (Actually it is a bit more complicated, because we would need to take the std.-dev.s of the other parameters into account as well, but presumably this would not change the said range significantly).

For other countries, you would only change the first argument on the xcssecovid command line.

We must not simply believe the curve fit results. It is important to examine the curve and verify that we really passed the turning point of the logistic function. For some countries, the course of the reported spread is everything but logistic, for example Iran. Other countries are still in a very slow exponential regime, for example Australia, and/or the course of the reported cases is somewhat erratic, for example Brazil. In yet other countries a second spread has started on top of the almost finished initial one, for example South Korea.
 
That is why I like this place. Someone solves a problem he has, makes that public, and the sure-as-heck to come probing (otherwise known as bickering) of the methods is both civil and on a level most people out there won't get. Great.

W.r.t. Russia, my grapevine sings about them stamping almost all deaths of this bug as pneumonia. That may be technically correct.

Well, it is correct. What we happen to have is a flu and a mass hysteria.

There were kinds of flu in the past which were highly contagious, and a flu could always lead to pneumonia, and that could always be fatal, specifically for the old and the weak. There is nothing out of the ordinary.

One lesson learned from this is to check and heavily control the use of antibiotics in livestock management. The areas with high rates of people dying correlates with high-density animal farming. MRSA is a high cause factor here.

This is not surprizing, but there is a lot more to it. We are used to have vaccination against flu, which lots of people get.
The effect is that the physis does never learn how to go thru a severe infection and survive, which leads to generally less stamina in that regard - because, just like everything in the physis (brain, muscles, etc.), this also has to be trained. And worse, at work we are not expected to be ill for a week or two and go thru it, we are expected to have the modern medicine just switch the illness off. That is against nature.
Furthermore, we have a high population of elderly people, many of these being still alive only because of the features of that modern medicine, and being trained to be dependent on these. Otoh, it was always the task of nature to weed out the old, weak and permanently ill, and that is true for as long as life exists. Over all, the regimen of "modern science" is against nature in almost all regards, and sooner or later that must backfire.
 
PMc well, what we have is a flu that doubles the normal amount of dead people in a city per time. That is not normal.

And we can be really really happy this is something from the tribe of flu and not from f.e. measels. Then the world would be really deep in the popy. Without a depth gauge.
 
One lesson learned from this is to check and heavily control the use of antibiotics in livestock management. The areas with high rates of people dying correlates with high-density animal farming. MRSA is a high cause factor here.


There was a article about the concern of the USA with its military personal, specially abroad, because of the high use of prophylactic antibiotics used by the military to treat anything...
 
Hospitals in Dresden and now hospitals in Nordrhein Westfalen were opened for and received already the first patients from northern Italy in order to help out with their capacities. Per Google Maps, Bergamo to Dresden is 951 km and a 10 h drive. Of course this can be done in Brazil and elsewhere as well.
The first two patients from Italy went to Leipzig last night (saw it in the German newspaper). They were transported in an Italian airforce transport plane, a C130, in isolation intensive care litters, directly from Bergamo to Leipzig. There are impressive pictures in some newspapers ("Die Zeit" hat an article I read). The patients were so critical that they had to be re-stabilized after transport. I haven't had time today to read newspapers; right now is the first time I have 15 minutes of free time today, and it is nearly 11 at night!

I do not have much experience with hospitals neither in Brazil nor in Germany. My son was born in a private hospital in São Bernardo do Campo and my daughter was born in the pseudo public IAMSP hospital in São Paulo.
Small world! I was born in Campinas, but that was nearly 60 years ago. Unfortunately, I have had too much experience with hospitals in Brasil; that happens whenyou have elderly parents.

I believe that dedication of the medical staff is as least if not more important than the technical resources.
Exactly. And this is one of the reasons the SUS (the public health system in Brasil) is so good: Well trained and highly caring and dedicated staff.

Regarding your question what countries I'm interested in: Obviously the US. I just haven't had time to download and run your code, been super busy. But I will immediately add a word of warning: It is silly to speak about "the US", because that is an average over vastly different regions. The pandemic first hit in California (right here, in the bay area), and then in New York a few days later. In the meantime, it has grown much larger in New York. But looking at "the US" as a whole ends up averaging totally disparate regions. Bay Area and New York are similarly rich, and similarly well networked internationally, but the Bay Area has much more connection to Asia, in particular China, while New York has much more connection to Europe. The US is also very big, the distance from Seattle to Miami is about like Madrid to Moscow, not just in kilometers, but also in cultural and economic difference. And the US has subregions that are economically and geograpically very distinct; Kentucky and Montana are neither like Boston, or Chicago, or San Francisco. Taking infection statistics and averaging then over all of the US really makes a mess of the data.
 
I've been using this for reference - but working with the data directly has its own appeal.

https://www.worldometers.info/coronavirus/

Caution: above does have ads in the sidebar.

Looking at the John Hopkins data - the USA data is under "daily reports".

I'm comparing the numbers above to what the State of Texas reports.

https://txdshs.maps.arcgis.com/apps/opsdashboard/index.html#/ed483ecd702b4298ab01e8b9cafc8b83

If that link does not work ... go to the main page and click on the "COVID-19 Dashboard" link.

https://dshs.texas.gov/coronavirus/

The numbers aren't exact 1:1 but seem to agree.
 
Regarding your question what countries I'm interested in: Obviously the US. I just haven't had time to download and run your code, been super busy. But I will immediately add a word of warning: It is silly to speak about "the US", because ... Taking infection statistics and averaging then over all of the US really makes a mess of the data.

Unfortunately, this is what the CSSE@JHU did on March 23 (the day before I started this thread). Up to March 22, US time series were separated by state and the series of the total numbers was not given. Since then the state's series were removed from the data set, and replaced by the US total time series. The old files with updated series until 2020-03-22 has been archived.


With a tiny modifications, my tool can be used to extract and curve fit the data for example of New York and California - but only up to the reported date.
Change line 125 to:
C:
if (len != col && (line = strstr(line, country)) && line[cl] == ',')
Change line 128 to:
C:
for (i = 0; i < 4; i++)
Change line 168 to:
C:
            if (--n > m)
Finally, line 197 to:
C:
                  if (isfinite(c[i]) && i < n)

NY_x_Cal.png

NY_x_Cal.log.png


The red/blue course belongs to New York and the orange/green course to California. However, in those states the case reports start only on 10th of March and I do not consider the count of case numbers being sufficient for a valid forecast.
 
I've been using this for reference - but working with the data directly has its own appeal.

https://www.worldometers.info/coronavirus/

Caution: above does have ads in the sidebar.

Looking at the John Hopkins data - the USA data is under "daily reports".

I'm comparing the numbers above to what the State of Texas reports.

https://txdshs.maps.arcgis.com/apps/opsdashboard/index.html#/ed483ecd702b4298ab01e8b9cafc8b83

If that link does not work ... go to the main page and click on the "COVID-19 Dashboard" link.

https://dshs.texas.gov/coronavirus/

The numbers aren't exact 1:1 but seem to agree.
This thread is about analysis of time series and not about the competition on who is able to report the latest and greatest numbers. For analysis of time series, consistency of the data set is the most important factor. Differences of the numbers between data sources can be mostly attributed to different reporting periods, and this must be consistent along the series.

CSSE@JHU provides the whole Covid-19 data set for download. They even do maintain the data set by correcting numbers from previous days if erratas arrive. Can we download somewhere any sort of maintained Covid-19 time series from Worldometer's site? Or from anywhere else?
 
Can we download somewhere any sort of maintained Covid-19 time series from Worldometer's site? Or from anywhere else?
Unknown. If I find such a source, I'll post here. How I got here was compiling my own time series from local reported daily numbers which quickly became a pain.

It's a little odd that the JH data does include USA numbers but omits the data in their time series. Not a big deal to generate the series from the daily files.

As stated above, working directly with the data has its own appeal.
 
PMc well, what we have is a flu that doubles the normal amount of dead people in a city per time. That is not normal.

Okay. But then, who defines "normal"?
I prefer a different approach: there was a way people used to live, right from the beginning (say minus 100k years, or maybe a million) until about 100 years ago, and it did not change so very much during this long time. That way of living has proven to work, because otherwise we wouldn't be here.
And there is an other way how people live, that was established during the last 100 years. If you care to look, that way is very different - and it has not yet proven to work.
In the old way, it was normal to grow your own food, and to work mostly outside. It was also normal to get 10 children, where six of them would die before the age of 4.
I don't say the old way was better, I only say, the changes aren't yet verified to be ok (that takes a couple of generations).

In my youth I was told that good food and working mostly outside (or doing things like walking barefoot in the snow) strengthens the resistability against catching a cold and such. Few people nowadays do such things; they rather prefer to believe in antibiotics and medicine.
There are many such little changes, and all together they may explain the death toll.
 
Can we download somewhere any sort of maintained Covid-19 time series from Worldometer's site? Or from anywhere else?

Summary of alternatives.


None look as good as the JH dataset. Turns out the Texas link above derives from the JH dataset.
 
Summary of alternatives.


None look as good as the JH dataset. Turns out the Texas link above derives from the JH dataset.
OK, I will stay with the CSSE@JHU Covid-19 time series. I am back at the drawing board of modelling now. A group at the Humboldt University in Berlin set up modified SIR differential equations in order to explain why almost all the curves are not exponential ad infinitum: http://rocs.hu-berlin.de/corona/docs/forecast/model/
I know why, because according to Einstein only two things are infinite, the universe and human stupidity, and he continued, that he is even not sure about the former. Anyway, I will build an ODE integrator into my tool, so it can do curve fitting of a set of ordinary differential equations against said CSSE series. Don't tell me that it is not possible to use an ODE solver together with the LM algorithm, I did this in the past several times with success.
 
I prefer a different approach: there was a way people used to live, right from the beginning (say minus 100k years, or maybe a million) until about 100 years ago, and it did not change so very much during this long time. That way of living has proven to work, because otherwise we wouldn't be here.
It worked barely. Humanity was nearly wiped out several times by infectious diseases, such as the bubonic plague. At times, whole parts humanity (geographic, cultural or ethnic groups) were fully wiped out; if you look at the history of for example the pacific island nations or Greenland, quite a few were settled multiple times, because the previous settlers had become extinct. Not to mention wars; the 30 year war was way more brutal than anything in recent memory.

Compared to that, the last 100 years were not particularly worse. Except from an ethical point of views.

In the old way, it was normal to grow your own food, and to work mostly outside.
That has not been the norm since about roman times in the west, so this is old news.
 
PMc you may be partly right, but almost exactly 100 years ago the spanish flu was out and about. And it killed more people than WW1 did. So the lifestyle does not really serve as an argument. We have factors in both ways. I for one do not know enough of these factors to have a conclusion. But I know whom to ask...
 
Unfortunately, this is what the CSSE@JHU did on March 23 (the day before I started this thread). Up to March 22, US time series were separated by state and the series of the total numbers was not given. Since then the state's series were removed from the data set, and replaced by the US total time series. The old files with updated series until 2020-03-22 has been archived. ...

Only a quick note. On April 1, CSSE@JHU put online consolidated time series for the states of the U.S. in separate files. The total count of U.S. cases is still present together with the ROW in the global files - see: https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data/csse_covid_19_time_series
 
OK, I will stay with the CSSE@JHU Covid-19 time series. I am back at the drawing board of modelling now. A group at the Humboldt University in Berlin set up modified SIR differential equations in order to explain why almost all the curves are not exponential ad infinitum: http://rocs.hu-berlin.de/corona/docs/forecast/model/
I know why, because according to Einstein only two things are infinite, the universe and human stupidity, and he continued, that he is even not sure about the former. Anyway, I will build an ODE integrator into my tool, so it can do curve fitting of a set of ordinary differential equations against said CSSE series. Don't tell me that it is not possible to use an ODE solver together with the LM algorithm, I did this in the past several times with success.

Done, I updated my xcssecovid tool for FreeBSD or macOS on my Cyclaero GitHub repository. It includes an ODE integrator, and a bunch of options can be set on the command line, and the options are explained in the Usage instructions:
./xcssecovid -h
Code:
Extract, curve fit an epidemiological model and transpose CSSE@JHU's Covid-19 cases data per country - Copyright Dr. Rolf Jansen (c) 2020 - Version 1.0.1
Usage: xcssecovid [-a<0-9> value] [-f (0-9)+] [-m model] [-e] [-r] [-s] [-o day#] [-z day#] [-h|-?|?] <Country> <CSV Input file> <TSV Output File>

       -a<0..9> value      Optionally set initial values for model's parameters the Differential Equation Solver and Curve Fitting.
                           The models deduce its initial parameters from the boundaries of the imported time series and by common
                           knowledge/best educated guesses. Example: -a0 0.57 -a1 125000

       -f (0-9)+           Overrides the default selection of a model's parameters which take part in curve fitting, and which usually is a0, a1, a2.
                           Example: -f 1245 would lead to curve fitting against the paramters a1, a2, a4 and a5, while a0 and a would be left untouched.
                           Different models got different number of parameters, which is currently 3 to 6.

       -m model            Select the model for curve fitting and simulation:
                           - LF   Logistic Function -- https://en.wikipedia.org/wiki/Logistic_function
                           - LDE  Logistic Differential Equation -- https://en.wikipedia.org/wiki/Logistic_function#Logistic_differential_equation
                           - SI   Epidemiological SI-Model (basically another form of the LDE) -- https://de.wikipedia.org/wiki/SI-Modell
                           - SIR  Epidemiological SIR-Model [default] -- https://en.wikipedia.org/wiki/Mathematical_modelling_of_infectious_disease#The_SIR_model

       -e                  Only export the extracted and transposed time series without curve fitting and simulation of the model.

       -r                  Only report the model description and the values of its parameters with or without fitting, but without exporting any curve data.

       -s                  Simulate the model without curve fitting before.

       -o day#             The day# of the first data point in the imported time series to be included for curve fitting.
                           [default: first day with more than 17 cases].

       -z day#             The day# of the last data point in the imported time series to be included for curve fitting.
                           [default: last day of the imported eries].

       -h|-?|?             Show these usage instructions.

       <Country>           Select the country for which the time series shall be processed.

       <CSV Input file>    Path to the CSSE@JHU's Covid-19 time series CSV file.

       <TSV Output File>   Path to the TSV output file containing the extracted and transposed time series for the given <Country>, including
                           a column for a simulated time series by the given model, using the parameter as resulted from curve fitting.

Now we may choose among 4 models for post processing the imported CSSE Covid-19 data. The Logistic Function (LF) is still present, which is the analytical solution of the Logistic Differential Equation (LDE). The epidemiological SI Model is just another form of writing down the LDE and got another name, so Epidemiologists feel comfortable with the math behind. Now the SIR model is a set of differential equations based on the SI model and amended by one term, the Removal of infectious individuals from the infection chain. This is the default model of the updated tool.

At the beginning, the LF model resulted in considerably good curve fits. However, on a day to day bases, the resulting parameters were moving, and more and more systematic deviations became evident. This usually means that the model needs a refinement. The LDE and the SI model are only included into the tool for proving that these are essentially the LF model, and would give neither different nor better results. Actually the SIR model is the refinement which does a very good job to the present stage. The day to day curve fittings for Italy, Germany and the U.S. do converge, while the results of the LF = LDE = SI model do diverge.

Code:
svnlite co https://github.com/cyclaero/xcssecovid.git/trunk xcssecovid
cd xcssecovid
clang -g0 -O3 -march=native xcssecovid.c -Wno-parentheses -lm -o xcssecovid
fetch https://github.com/CSSEGISandData/COVID-19/raw/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv
./xcssecovid US time_series_covid19_confirmed_global.csv US.tsv
head -15 US.tsv
Code:
# US
#
# Model: SIR Differential Equations
#   dy0/dt = -a0/a1·y0·y1         || y0(a5) = a1
#   dy1/dt =  a0/a1·y0·y1 - a3·y1 || y1(a5) = a2
#   dy2/dt =  a3·y1               || y2(a5) = a4
#
#      a0 =   0.623407 ± 0.88468 %
#      a1 =     529773 ± 3.5909 %
#      a2 =    12.6712 ± 13.753 %
#      a3 =   0.333333
#      a4 =         20
#      a5 =         34
#
#  ChiSqr =    1527959

Day #71 is April 1. In case a strong containment regime is maintained or even improved, we would see much less new infections per day by the end of April (day #100) in Germany, Italy and the U.S. Here are the curves for the U.S.:

US-SIR-2020-04-01.png


US-SIR-2020-04-01.log.png


One crucial parameter in the SIR model is a3. This is the rate at which infectious individuals are removed from the infection chain, i.e. by any means won't infect others. 0.333... means that it would take 3 days for an infectious person to be identified and put under effective quarantine, this is roughly 5 days to 1 week after the person was infected him-/herself.
 
PMc you may be partly right, but almost exactly 100 years ago the spanish flu was out and about. And it killed more people than WW1 did.

I am most times only partial right - that's human nature. But this is basically what I am saying: such things have happened before - there is nothing out of the ordinary. And yes, it kills people. That is also normal, it fact there is only one thing we know for certain: that we will die.
I travelled quite a bit thru Africa and Asia, and one thing that impressed me very much, is, that death is much more conscious there, as something that is normal and can always happen. Consequentially, life is also more vivid and colourful there, because that relates, and you can't have one without the other. And I for my part enjoyed that freedom much, not being protected 24-hours by buerocrats, but actually being allowed to take care for myself. Only that way I learned that actually Nature -or God, which equates- takes care for me, and I found many things that way, even water in the desert.
In contrast, our super-protected and super-insured, super-sterile western life makes us being full of fear and neurosis that something bad might happen to that life which we most of the time don't really live. But probably, now, we can make a great step forward and reduce ourselves further to mere online-creatures, cage-kept roboters existing only for work and consume.
 
Unfortunately, this is what the CSSE@JHU did on March 23 (the day before I started this thread). Up to March 22, US time series were separated by state and the series of the total numbers was not given. Since then the state's series were removed from the data set, and replaced by the US total time series. The old files with updated series until 2020-03-22 has been archived. ...

Only a quick note. On April 1, CSSE@JHU put online consolidated time series for the states of the U.S. in separate files. The total count of U.S. cases is still present together with the ROW in the global files - see: https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data/csse_covid_19_time_series
 
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