Created by Dan Goodspeed

using data from New York Times and

visualization help from Flourish.

For each given date, the number is the total of new normalized* cases that week (that date, plus the three before and three after). So a '1000' means 0.1% of the state's population received a positive COVID diagnosis that week.

* "Normalization" (perhaps better called "smoothing") means the abnormalities in the data were evened out. For example, if there were 10 days in a row of a few cases/deaths a day and then one day of 1000... that looks awful and frenetic on a chart like this, even when framed in a per-week display. In reality, that 1000 is just a backlog catch-up, so I normalized it by spreading the thousand over previous dates for a more even / more realistic data. It works similarly when the total number of cases/deaths drops one day. Likely a correction from a previous report, I just subtracted the difference over previous dates to numbers that are probably closer to reality.