I thought folks might find it interesting to see the cumulative case rate charts from a selection of states and counties. There are interesting things to note in all of these. Remember that these are all just measuring cases and that cases are a strange measure due to the wildly different response different people have to the COVID-19 virus. Still, case growth is a leading indicator for hospital overload and deaths…
Note: in all of these charts, I am choosing to show “non-normalized” data. This means, when you see an “IROC-Confirmed-Case” number, that refers to the instantaneous slope of the curve. This slope will represent the number of new cases per day for the curve, and is often a better indicator of the case growth than moving averages or other such approach. Therefore, when comparing charts, keep in mind that the visual slope of the line is the better comparison than the numbers (because larger places like LA county will most likely have more cases due to their population). Also, I start each chart at the point they hit 5000 cases.
Also, the light blue line might be confusing to people. I am modeling the entire outbreak in a region with a fourth-order (quartic) polynomial equation and this equation is plotted in blue. You can see how the red “actual” datapoints often align strongly with the quartic equation for the region. I’m not sure if the fact that I can fit the whole outbreak for a region with a quartic is interesting or not, but I do know that the quartic emerges often in fields like optics and the propagation of waves through a real-life transmission line (like a copper wire). I wonder if a virus propagating through a real society is a similar application?
I’ll lead off with the chart of my home county. Pima is the location of Tucson and has over 1M in population. Mask orders have been in place since early June, but my observation was that a lot of residents were already observing mask orders well before that. The whole county has a curfew currently in place (I assume it is to reduce the numbers of people at bars?). You might note that the curve was solidly accelerating from about Halloween until Thanksgiving and then it started into a linear phase comprised of a bunch of positive and negative oscillations. This is to say, that the case rates have slowed for a few days, then sped up, and so on. Note that the last data point is a big jump over previous days. My guess is that this is an anomaly due to state DHS people taking holidays and accumulating numbers differently than in the past. Today’s data point (not shown here yet) is much lower, so I’m curious about whether the deceleration trend continues or not.
Maricopa county is the largest in the state with over 4M residents. This is the location of Phoenix, Mesa, Tempe, etc. Note that the latter part of the chart looks similar to that of Pima County. You can see more of the Maricopa curve since they hit 5000 cases earlier than Pima. Right now Maricopa communities have individual mask ordinances in place (some cities cancelled theirs then brought them back as cases surged. I’m unaware of any curfews in Maricopa, but that may be from lack of looking. Maricopa County is also the location of a number of large kids soccer tournaments back in late November and early December that were notable due to media attention (lots of teams from California were participating since they can’t practice or play in California). I don’t see any evidence of case surges due to these tournaments… rather, it appears like cases decelerated all the way from Thanksgiving until about Christmas day.
Currently, California has the highest case acceleration of any state and it does seem like LA county is a big driver. Note that the curved formed by the red datapoints is steeper than the blue line would model. This is very surprising to me in light of California’s significant COVID-19 restrictions. One might speculate that high density plus low evening temperatures (in my previous entry or two I point out that most of these surges started when night temperatures fell to 50 degrees) could be leading to the really steep slope in California. However, density might not explain it, as I’ve noted in Arizona that the most dense zip codes tend to have lower case growth than the less-dense zip codes. Regardless, the situation in CA is a puzzle.
Orange County’s case curve looks similar to LA’s except it seems to fit the quartic model better regarding acceleration.
Now we’ll switch to another large county, Harris County in Texas. This is the location of Houston. Note that it’s case slope is much flatter than any of the other regions. It appears to be accelerating slightly. One thing I’d note about Harris County that is absent in the previous charts, and that is humidity. Whereas Arizona and California are very low in humidity this time of the year, Houston is somewhere around 70-80% humidity. It has been observed that the virus transmits most effectively in lower temperatures and low humidity.
Above is the chart for the state of New York. It looks similar to California, just with a bit lower slope. The state also seems to have the same oscillation pattern over the last two weeks that AZ and CA regions have.
Now to change the pace again, here’s the third largest county in the state of AZ, Pinal County. This is a mix of rural and suburban communities, probably leaning more towards the rural. To my knowledge they don’t have any county mandates in place for COVID-19 and their characterization through 2020 regarding COVID leaned more to the individualistic rather than the collectivist. Pinal saw some notable tapering off of the visual slope of cases around 12/15, but some of this is probably anomalous due to the large jump that happened around 12/13. This has the appearance of being a data collection glitch. Note though, that this county’s trend is to fall below the blue quartic model line.
Finally, here’s a picture of a much smaller county in AZ, Cochise County. This is a primarily rural county with a medium-sized retiree population. Cochise had very few cases for the longest time but they’re getting close to doubling their case number since mid December. This is another area that has hit lows of 50 degrees and has low humidity. It is a bit higher in altitude than Pima County.
Normalized Data for Selected States
Here’s a chart where I have normalized the data by population to give better comparisons. I’ve selected a handful of states, including a number from the section above. What do we see across the board? General slowing of cases across the southeast, a bit of acceleration remaining in California and New York, and a bit of “uncertainty” in places like Georgia and Arizona.