Data Obsessions while in Self-Quarantine

I sit in my home office looking into a garden which explodes in yellow from the forsythia with splashes of pink from the camellias. Both flourish after a large shading cherry tree fell down a few years ago. The tree stump is covered by moss and provides a natural border. My native American Flame azaleas (Rhododendron calendulaceum) now stand 8 feet tall in front after I planted them in 2001 as 3 inch sticks. They are the pride of my garden along with Piedmont, Sweet, Okonee, and Plum azaleas all purchased from Callaway Gardens in Georgia. They grow well, because I correctly predicted that the warmer climate zones of Georgia would move northward towards Delaware. Here are the azaleas in blooms in early May or four weeks from now:

These are distractions, because I need to process and analyze ocean velocity data off Greenland. My student from South Korea rightfully expects numbers that she can work with for her Masters degree. We plan to meet via Zoom video call every Friday and Wednesday. She is ordered to stay at home in Maryland while I am ordered to stay at home in Delaware. We also meet Monday and Wednesday evenings when I teach “Waves” via Zoom to eight University of Delaware graduate students from China, South Korea, Thailand, and the USA. Our topic yesterday was the waves in the wakes of a ship or a duck or an island. To me physics are as beautiful as are the flowers in my garden:

Now these are the things that I should work on during my self-quarantine, but I am obsessed and distracted with new data. The Johns Hopkins University in Baltimore, MD distributes data on the number of people who were diagnosed with Covid-19, who died of it, and who have recovered. While it is easy to access their excellent data displays as global health authorities report them, the actual raw digital data files are accessible at

https://github.com/CSSEGISandData/COVID-19

These data require computer programming and data handling skills that a well trained physical ocean, climate, or data scientist masters. The raw data, however, do not tell a story, because it just looks like gibberish,

but there is a most orderly system to this madness. With 143 lines of computer code (one C-shell and two awk scripts) I convert these data into a single graph to tell a story:

First, I focus only on the number of people who have died, because I consider this the most reliable (albeit morbid and depressing) estimate of how the virus is spreading.

Second, I present the number of people who died relative to the population. It hardly seems fair to compare the numbers from the USA with 327 million people to those of Malta with only 0.5 million people. The technical term is “normalization,” that is, all numbers are relative to 1 Million people. So, 5 dead in Malta give 10 dead per million. The same 10 dead per million correspond to 3270 dead Americans. This way I am comparing apples to apples as opposed to Americans to Maltese.

Third, I want to compare the spread of the pandemic over time on different continents, different countries, different states, and different cities. This requires to time-shift countries hit by the virus earlier than others. In the above graph, for example, I moved the curve for Italy 14-days forward and that of Spain 6-days forward relative to all other places listed.

Fourth, I am most interested in New York State (population 20 million), because it contains New York City (population 8 million) and, I believe, it gives Americans a good idea what is coming. Furthermore, I believe, that the Government of New York State is a little more efficient, smart, and forward-thinking than many other government entities. It also has resources not necessarily available to less affluent communities.

The curve for New York State initially (until Mar.-25) followed the trajectory of Italy 14 days earlier, but then it switched over to the steeper trajectory of Spain 6 days earlier. Notice that Italy’s curve has a flatter trajectory than the steep curve of Spain and New York State. From Mar.-28 to Mar.-31 the New York curve was almost exactly that of Spain 6 days ago, but yesterday, the number of people dying in New York grew even faster than those in Spain or Italy ever did. This is scary stuff.

Yesterday, New York State had about 111 dead per million people. While this is still less than the 180 dead per million people that both Italy and Spain had yesterday, it may take only 4-5 additional days for New York State to reach those numbers also, but I still do not know what these numbers mean. I do not “feel” them. So I try to compare them to other causes of death such as people getting killed every month in (a) car accidents (9 per million) or (b) gun violence (8 per million) or (c) cancer (126 per million). These references help me to visualize the scale and impact of this pandemic.

So, while Covid-19 has killed about as many people in the US the last 4 weeks as people died in car accidents, in New York State the number of Covid-19 dead is about to exceed those who died of cancer in this same period. The hardest hit place in the US, however, is not New York City (160 dead per million), but New Orleans (295 dead per million). The County or Parish of New Orleans, Louisiana has about 400,000 people or a little less than New Castle County in Delaware where I live, but New Orleans has 115 dead compared to 5 in New Castle County (9 dead per million).

There are a few bright spots and I want to close on those. Los Angeles (7 dead per million) and California (5 dead per million) are doing remarkable well as does Germany (11 dead per million). Despite physical separations from others, I feel closer to friends, family, and neighbors both overseas and across the street. With more than 10 feet distance we have impromptu get-togethers between the door and the end of the driveway of 4 different households. I am happy to know that my neighbor Joyce from Kenya is safe back home living quarantined across the street with her African friends from Mali. She runs Water for Life which is a small non-profit that provides clean drinking water for rural communities in Kenya. It makes me happy to know her as a neighbor across the street.

And then there are the true warriors who fight this virus while endangering themselves to help others. Here is a nurse from Spain whose photo at work I took from her Twitter feed. We are all surrounded by wonderful and beautiful people.

One response to “Data Obsessions while in Self-Quarantine

  1. Hi Andreas, wonderful post, as always; but cannot resist that you have failed to add a notation to account for a very clear delineation between the waves outside the wake, (particularly clear in your image of the duck), and those inside of the wake. With the duck image, there is even a line created. So may I be so bold as to suggest that that distinct line requires a notation, E, so that the calculations provide an explanation. (Yes, indeed, my “eye” again 🙂 ).

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