Gas station without pumps

2020 July 16

Updated plot for COVID-19

Filed under: Uncategorized — gasstationwithoutpumps @ 19:36
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My previous COVID plot showed New York having reached its peak and California doing really well, but things have changed a lot for California in the past month, and even more dramatically for Southern states:

Santa Cruz has shot up in the past month, but is still doing better than much of California.

I added Santa Cruz (county) as a possible location to highlight, but I’m having to manually copy data from the Santa Cruz website, which is a pain, as they update it daily with corrections extending back a week or more. I probably should try to find where the data exists in downloadable form.  Santa Cruz is still a month or two behind California as a whole, but seems to be catching up.  We’ll probably hit a peak just as school starts.

Florida has now reached the top of the leaderboard in terms of cases/million each day, as Arizona seems to have moved past its own peak.  Louisiana is probably the only state that is seeing a second wave (rather than a delayed first wave), having brought the new cases down for quite a while.  It probably won’t be long before Arizona and Louisiana surpass New York in total cases per capita.

Bay Tree Bookstore at UCSC has come out with “Fiat Face Mask”:

It isn’t a very creative design, but it has a certain appeal to it. I’m getting one to add to my rotation of cloth masks.

2020 June 14

New plot for COVID-19

Filed under: Uncategorized — gasstationwithoutpumps @ 22:29
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It has been a while since the post I did on exponential and logistic growth models not working.  I’ve continued to scrape data from websites and plot the curves with gnuplot, but they have been very uninteresting—I was seeing almost linear growth in both US and CA curves, both for confirmed cases and for deaths.

I was getting a little bored with the manual data entry, and I did not have a good set of California data, because I had been too lazy to enter it daily.  So today I decided to waste a little time cloning the JHU github repository of data, and write a Python program to extract data from it.  This turned out to be messier than I thought, as JHU has changed the format of the files and data a couple of times,

I started by parsing the US-only files, because they seemed to be pretty clean and uniform, but they only go back 63 days (since 2020 April 12), so miss the early days of the pandemic.  I then started parsing the world-wide data files, which have a lot more rows (more than one per county for California) but fewer columns.  I needed to write routines that would merge data from multiple rows if I wanted state-wide numbers, and the format changed at least once, so that I had to recognize “San Diego County, CA” in “Province/State” as being the same state as “California” in “Province_State”.

It has also been a while since I’ve used matplotlib, so it took me some time to figure out how to do such simple things as requesting that logarithmic axes use plain numbers rather than 10^2 and 10^3.

Anyway, I think I’ve finally gotten the files parsed and been able to extract and plot some data.  I chose for my first plot just to plot the new cases/day vs total cases for each state, which I could not do with gnuplot (because it doesn’t provide an easy way to take the differences between adjacent days nor to do rolling-window averages.

I highlighted two states here: California, because that is the one I live in, and New York, because it has been hit the hardest with COVID-19.

New York has clearly peaked and has a declining new-case rate, while California is still slowly growing. I don’t think that the numbers, even with the per-capita scaling, are really comparable between California and New York, because the California fraction of tests that are positive has remained relatively small, and the new-case rate has tracked with the number of tests fairly well. I think that a lot of the growth in California has been due to increased testing and confirming a larger fraction of the cases, rather than an increase in the actual rate of new infections. (The hospitalization reports plotted by the LA Times indicate a slow decrease in California hospitalizations lately.)

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