Red dots show grids with over 250 new cases per day per million population. Orange dots, over 100. Blue dots, over 10. Green dots, less than 10. Grid boxes are 2.5° longitude wide, 2° latitude tall.
Data from Johns Hopkins Univ.
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Interesting map. It does certainly does provide a general qualitative picture of where COVID-19 is rising fast (or not). But I wonder about the utility of the grid placement used here. Note the small blue dot about 90 miles off the coast of Maine. Probably not many people there. And the large red dot that appears centered just south of the Texas/Mexico border. This is the approximate location of Big Bend National Park — probably not heavily populated, and probably not a COVID-19 hotspot. Nevertheless, the map does tell a story.
[Response: The dots show the locations of the centers of the grid boxes. Even those whose center is mid-ocean, still include land areas.]
” But I wonder about the utility of the grid placement used here. ”
When reading that, I’m no longer sure you really understood what Tamino computed and displayed here above.
And I get even less sure when I read:
” This is the approximate location of Big Bend National Park — probably not heavily populated, and probably not a COVID-19 hotspot. ”
Tamino’s graph is about daily cases per million.
Thus, if a corner is ” probably not heavily populated ” but has a bright red spot, it then certainly IS a COVID-19 hotspot.
Imagine now that Tamino would have overlaid a 0.25 degree land mask over the grid, allowing him to precisely compute the grid cells’ land part.
Then all these cells whose spot is centred outside the land part would show a much higher ratio of daily cases per million inhabs per e.g. 1,000 km², making their red spots even bigger.
There are probably a number of ways of looking at the data to identify “hot spots”, depending on your perspective of what constitutes hot and how you are hoping to use the data. For example, Brewster County, Texas (the Big Bend area highlighted in Tamino’s map) showed 11 new cases per day as of July 1 (7 day average) vs 3 two weeks ago. So an increased rate of 8. Population is 9267, so an increase of 863 per million. I guess that would justify a large red dot on Tamino’s map. On the other hand, Brewster county has a population density of about 1.5 per square mile. Contrast that with Middlesex County, Massachusetts (where I live): an increase of 43 cases per day as of July 1 (7 day average) vs 59 two weeks ago. So there is a decreased rate here. Population is 1.612 million, in 847 square miles. Population density is about 1900 per square mile, nearly 1300 times the density of Brewster county. The Middlesex County increase of 43 comes out to 0.05 per day per square mile. For Brewster County, using the new average of 11 per day, I calculate a rate of increase of 0.0012 per day per square mile. Looking at it that way, the Middlesex County rate is about 43 times that of Brewster County. This may relate to your relative risk of exposure in these two areas. And the closest dot to Middlesex County on Tamino’s map is a small blue dot. So how you present the data depends on the intended use of the presentation and on what story you are trying to tell. I would suggest that the risk to the country from the currently rising rate of disease comes mainly from areas of highest disease count and highest population density as well as rate of increase, and not from Brewster County, Texas. Tamino’s map, taken on its own, tends to suggest otherwise, at least as I see it. Hence, my (fairly modest) questioning of the value of Tamino’s map.
My data was taken from the July 2 edition of the New York Times (https://www.nytimes.com/interactive/2020/us/coronavirus-us-cases.html), which credits state and local health agencies and hospitals. Qualitatively, the New York Times map of hot spots in the U.S. is similar to Tamino’s map, with the hottest area in the Southeast U.S.
I don’t wish to engage in a lot of argument here. I’m not suggesting that Tamino’s map is wrong. He doesn’t usually get these things wrong. But it tells a particular story which may not be the only, or most, relevant story regarding the spread of this pandemic in the U.S.
Reblogged this on Don't look now.
Well, that puts paid to the idea (if anyone was still harbouring it) that it’s a cold season virus.
Thanks Tamino for this very informative, grid-based evaluation.