Here’s a bunch of Arizona (and some US State data) that touches on case growth as well as death and hospitalization trends.
Above is an interesting chart showing the case growth trends for the 10 zip codes in the state with the highest case counts. You can see a few things here:
- Yuma again has the fastest growing zip codes (the dark blue and orange lines at the top). The next 8 highest are a mix of Phoenix and Tucson (and a couple of suburbs of Phoenix).
- The aqua line that looks weird is likely a data error that happened a while back. Note how this line doesn’t follow any of the trends that the others do. It appears like whoever was collecting data messed that zip code up on 9/12 and kept messing it up a little bit undil 12/12 when they “fixed” the data suddenly. I point this out because this is pretty common. I presume that the state DHS collects this data and manages it (it does come from their site) and it does seem to be their habit to suddenly “fix” data. That’s probably better for them than backdating it due to their unwillingness to share historical data (their dashboard only shows the current day, so to build these plots I have to scrape the data manually every single day). One outcome of this habit is that much of their data is not very trustworthy (Hospitalization is a good example. They have messed that up multiple times).
Showing the same data as the previous chart above, except this time it’s normalized by population. This shows that the Somerton zip code of Yuma County is far outpacing the others per 1000 residents.
Above are the Zip Codes in Arizona with the largest “surges” in COVID cases over the last two weeks. This is a percentage of their previous case count, which isn’t the metric to end all other metrics, but it is interesting to note that for whatever reason, that region experienced an unusually large surge. In this case, we see Yuma County with the highest surge in 85349, followed by two zip codes in Coconino County. After that it is a mix of the two largest counties, Maricopa and Pima. Interesting things to notice:
- The median age of all of these is pretty low, as is the median income. One exception to this stands out, 85383 in Peoria, AZ, which has the highest median age and median income (the two do tend to go together, of course). So it would be interesting to study this zip code to see what happened.
- The two Flagstaff zip codes have very low density unlike the other zip codes which (other than Peoria above) are very high density. These two zip codes are north and east of the city of Flagstaff, so they are more rural. Both have a relatively sizable population of Native Americans. These represent areas that someone ought to investigate.
Above we can see US States sorted by Case Growth Rates (IROC_confirmed). Note that some of these states case rate numbers are rising (Red Up Arrow) and some are falling (Green Down Arrow). What I have noticed is that once the dIROC_confirmed column goes down to near zero (or negative), the case growth flattens out shortly after. I saw this recently with North and South Dakota, and that did prove to be the leading indicator that their case rates were flattening out. I suspect that Indiana, New Mexico, and Utah are now through the worst of their winter outbreak (they all started before Arizona and California, who are now both on the rise). This is a good field to watch to understand when a state will stop accelerating in growth.
Above we can see that two Arizona border regions have risen to the top of the list of Counties with the highest case growth and acceleration rates again. Santa Cruz and Yuma counties both had large outbreaks during the Summer and I had hoped to see them be relatively unaffected during the winter, but that seems to not be the case at all. Val Verde County in Texas is another border county, which makes me wonder if there’s another big outbreak in Mexico (I haven’t been looking).
I’ve showed this chart once before, so here it is updated. You can see a few things here:
- The case growth for Arizona (the orange curve) continues upwards unabated. You can see this in the tables above too, of course.
- Over-65 deaths continue to be the large majority of deaths. Even when the data isn’t normalized by population, the over-65 group (only 13% of the state’s population) dominates the death numbers. You can’t see this easily in this chart, but the ratio of over-65 deaths to under-65 deaths has risen from 2.8 during the first outbreak during June-August up to 3.7 since late October. This seems to indicate that the disease is either more dangerous for the over-65 group this time around or that it is less dangerous for the under-65 group. I’d lean towards the latter since the overall death numbers are still lower during this winter outbreak than they were during the summer outbreak by quite a bit.
The above chart is more experimental than anything. I was curious about what the ratio was of hospitalizations per day divided by the number of Cases from one week earlier. Then I calculated this ratio as a percentage for each age demographic. In theory, this represents the percentage of people that have a COVID case confirmed and then enter the hospital one week later. This isn’t a perfect metric (what if they enter 2 weeks later?), but it seems interesting and the trend has been pretty consistent for a while. Note the what I have done to see the trends is to fit a trendline to the data for each age group. The over-65 trend line slopes upward (maroon-ish color), which may indicate that the hospitalization is increasing for over-65 people as a percentage of over-65 people getting confirmed cases one week previous. For some reason, though, this ratio is decreasing for all other demographics. This may be meaningless (there’s not a whole lot of data yet), or it may indicate that the likelihood of going to the hospital due to COVID is decreasing for everyone but over 65 age people. I’ll keep building and tracking this.