Category: COVID-19

Here’s a quick Christmas Eve update of the Arizona Data by Zip code. This is a pretty interesting dataset because it allows me to look into much smaller areas than counties and see more granular trends. I can sort this data a number of ways and generally compare one or two weeks of data by zip code to see where the trends are. The below table is sorted by the percent growth in the zip code, but I also like to look at the data sorted by Normalized Growth (growth per 1000 persons in the zip code area).

As you can see, Pima county is topping the list for percent growth. This represents a surge in cases that is independent of the population in the region. It really surprises me that zip codes in Pima county are at the top of this list because Pima probably has the most restrictive anti-COVID rules in the state (including an evening curfew). The 85710 zip code is interesting because it is an older suburban area East of Tucson and the Median Age is over 40. This is kind of unique for a zip code in the top 10 by percent growth. Note that their normalized growth as a percentage of the population is very low, so what this tells me is that this zip code has had very little impact from COVID until the last week and then they saw a 23% surge. Note that the zip code in Douglas, AZ, (on the border with Agua Prieta, MX) has very different statistics. They saw almost 19% growth last week but have had a much bigger impact from COVID (their normalized growth is 4x larger than 85710). That means that their population was 4x more saturated by COVID last week than 85710. The next four zip codes by % growth are Pima county zip codes. The one with the highest normalized growth is 85706, which has had higher overall case numbers and also experienced higher normalized growth. Incidentally, it is also the zip code with the lowest median age and median years of education. Not sure what that means, but if I were to guess, I’d say this zip code has more people that can’t work at home.

Comparison of Normalized Case Growth with Population Density

Earlier I posted this interesting visualization showing how the zip codes with the lowest population density seemed to be experiencing higher than normal cases per 1000 persons. Below, I re-ran the data for last week and we see that at least for last week this interesting trend continues. There may be a multitude of reasons for this, but one interesting possibility is that the areas with higher population densities have more effective COVID restrictions in place and the areas with lower population densities may have less or no restrictions in place. This could be over-simplification, but it does seem to be the case that the counties with the most restrictive COVID measures are the largest by population.

Is the Arizona Surge Slowing?

Quickly, below is the cumulative case curve for Arizona as of 12/24. The blue dashed line is my polynomial curve fit, which as you can see has fit the red daily cumulative case counts very nicely until about a week ago when we saw the case acceleration rates start to slow. As I’ve noted in the past, the slowing acceleration rates are generally an indicator of the slowing of a COVID surge. So keep your fingers crossed. I also have another post that I recently put up that is looking at decreasing hospitalization as another leading indicator of the slowing of a surge.

Here are a couple of visualizations that give me hope that the hospitalization surge is easing… The below is from the AZ DHS Dashboard. Red bars represent the percent of ICU beds being used by a COVID Patient, Dark Grey are ICU beds in use by non-COVID patients, and Light Grey is available beds. Note how the last few days have seen a flattening of the COVID percentage. This is the behavior we noted as the first Arizona ICU bed surge happened in July.

See below for my own metric, percent hospitalized today compared to cases from one week ago. If you scroll down to a previous post, you’ll see that the over65 group was still trending up on this metric. About a week ago (when I first noticed the AZ DHS metric flattening) the over65 trend on my metric flattened and then started decreasing.

None of this is certain, of course, but maybe these are leading indicators that the virus is starting to run its course. Looking at the AZ Cumulative Case curve below, you can see that the current outbreak (which started in late October) is getting close to two months in duration. Since I’ve noticed in other states that non-linear case surges last about 2 months in states that enforce COVID protocols, perhaps we’re nearing the end of our winter surge?

Since the Thanksgiving Holiday season I’ve seen a number of major media outlets leading with stories about how Thanksgiving led to an increase in cases. Examples from WebMD, NPR, and others. So is this real, or is it an example of Confirmation Bias? It’s hard to know and even harder when one looks at just short-term trends.

Parts of the justifications of these publications for their assertion that Thanksgiving led to an increase in cases is that contact tracing has discovered a number of cases that can be traced back to Thanksgiving gatherings. The NPR article reported that:

“We are seeing a tremendous surge in cases in many locations around the United States that are associated with the Thanksgiving dinners, family get-togethers and social events,” says Michael Osterholm, an epidemiologist and director of the Center for Infectious Disease Research and Policy at the University of Minnesota. Much of the evidence comes from health departments that are tracing clusters of cases, but Osterholm suspects that hospitalizations and deaths — “lagging indicators” — will reveal the full impact in a few more weeks.

https://www.npr.org/sections/health-shots/2020/12/21/948809129/epidemiologists-urge-a-cautious-christmas-after-thanksgiving-surge-in-some-state

So, can we determine that the Thanksgiving Holiday gatherings were causal for increased case counts?

Lets start by looking at the data. Below, I picked a few different states to compare their case rates in one chart. Since I’m normalizing the raw case counts by the population of the state (actually, per 1000 persons), I’m able to compare cases in a relatively “apples to apples” way. Therefore, we see a number of things in the chart below…

1. The Dakotas ran up to the highest numbers of cases per 1000 residents in the country. Their surge started around mid- to late-August where it appears that they transitioned from linear case growth to non-linear (3rd-degree polynomial) levels of case growth. During this latter stage, the growth rate increased every day (it accelerated, actually) until somewhere around mid-November when the cases began decelerating. You can see when this happens by looking at where the upward curve switches to a downward curve. Nebraska seems to have started decelerating around the same time. The Dakotas’ period of case acceleration appears to run from mid-August to mid-November (3 months) whereas in Illinois it ran from late-September to late-November (2 months). We see a similar outbreak range of 01 October to the end of November for New Mexico, which has had some of the strictest COVID policies in the nation. I’m curious if this is a sign of an effect of the stronger government COVID policies in Illinois and New Mexico, but this would take much more analysis to prove.
2. While the Dakotas were surging, California (the light blue line) was maintaining linear case growth. However, sometime around mid-November, California’s linear growth began accelerating and you can see that their rate of acceleration (the highest in the country right now) is starting to approach that of the Dakotas from mid-November.
3. We can also see a handful of other places where states transitioned from linear case growth to non-linear case growth. I’ve tried to eyeball these and place a blue diamond where I think the transition occurred. After the transition, as a reminder, every day the case growth rate increases. I did a quick peek over at Accuweather.com to see what the high and low temperatures were in the largest city in each “blue diamond” state during the timeframe the transition from linear to non-linear growth occurred. In most to all of these cases, the non-linear transition occurred during a notable weather shift where the night-time temperatures went from somewhere in the 60’s or above to 50 or below (degrees F). In some cases, the low temperatures dropped more than 5 degrees in a day or two.
4. I’ve marked the Thanksgiving holidays with a blue rectangle. At least of the states represented here, none of them had a linear to non-linear transition after Thanksgiving.

Since the NPR article mentioned a surge in the Southeast, I re-ran the code that generates the above chart using different states, mixing southeastern states with other warm states as well as NY and Mass. See below. Tennessee has a very high rate of acceleration right now (almost as high as California), so you can see that it is curving strongly upward. It seems like it’s inflection point between linear and non-linear happened sometime in early November. Looking at the other SE states, I see inflections in similar timeframes. I don’t really see any states here that were linear until Thanksgiving and then go non-linear (signal of a major outbreak). Since I live in Arizona, I paid special attention to the Arizona curve. You can eyeball on the green line below that all was fairly linear until mid- to late- October. Guess what, accuweather (see image below the curves) tells us that Phoenix had it’s daily low temperatures crash from 69 degrees to 54 degrees on October 26th.

The Rate of Deaths per 1000 – the Lagging Metric

Below is a different metric that might give us an insight. These are the top 8 states by Cumulative Deaths per 1000 persons. The initial states that were hit hard by COVID back in May experienced much more than their shares of deaths for reasons that are probably fairly obvious… the virus was new and these states were first up to bat. They made mistakes as well as breakthroughs in how a community would respond to this virus and that resulted in higher death rates. But note that after June their death rates flattened off or at least became linear. The Dakotas are a very interesting comparison, however. They experienced very few deaths during the first six or so months of the COVID pandemic but then saw pretty high death rates (which are still increasing at a fairly high rate) ever since. In just the last month or so, though, the northeast states have seen a transition from flat or linear death rates to non-linear. But the slope of the current increase is pretty low. So what might all this tell us?

1. I suspect many of the people who died in the Northeast during May and June contracted the disease before anti-COVID policies (Masks, Lockdowns, Improved Retail cleanliness policy, etc.) went into place.
2. I also imagine that ND and SD didn’t have a whole lot of COVID floating around early on. The weather was nice and people likely were outdoors, where evidence is showing that transmission is less likely.
3. I hear anecdotally that ND and SD had no official policy about Government COVID intervention. I haven’t checked this, but it is what I heard and that seems to make sense as those states have a more independent streak to them. So what we see on their death rates is what happens absent a defined policy. My suspicion is that like most other states, their first death wave is in the susceptible community of people who have susceptible immune systems.
4. Right now the death increases in the Northeastern states appear like they will be much less severe than their earlier deaths.
5. As the Dakotas’ case rates have already slowed down and are decelerating further, I presume that their surge is over for a while. At some point I’d imagine that their deaths would flatten off too.

Takeaways

1. Though the articles state that contact tracing data indicates that a high percentage of current cases stems to Thanksgiving gatherings, I can’t see any evidence of a surge of cases in any state that started after these holidays. What might this mean? First, as with any subjective human measurement and data collection system, I don’t think contact tracing is anywhere near 100% accurate. COVID is everywhere these days and there may real difficulty determining if new cases were acquired during a holiday meal (or if they were acquired at the grocery store, or the office, or the Starbucks that one stopped in at on their way to the gathering). Second, if Thanksgiving led to a surge and the existing transmission rates just before Thanksgiving held constant, then we would see it in an increase in the existing case acceleration. I think that would be a hard case to make looking at these curves.
2. COVID is very complex because it is interacting with a highly complex society. As such, attempting to find one causal reason for anything to do with COVID is probably going to be frustrating. That said, there does seem to be a strong correlation with temperature and COVID transitions to non-linear growth. I haven’t checked each one of these states (feel free to go off and check the others and report back!), but in the cases where I did, it seemed to be where there was a sharp fall in the nighttime temperatures.
3. The concept of seasonal outbreaks of influenza has been investigated for years, but recently there is consensus around the causality of temperature and humidity for influenza outbreaks (see paper from the Journal of Virology). The temperature number that the linked paper references as being ineffective for influenza transfer is 30 degrees Celsius (86 degrees F). The paper also states that influenza transmission is highly efficient at 5 degrees C (41 degrees F). I’m not aware of any top-notch papers on the effect of temperature or humidity on COVID, but the NIH has a nice summary of around 20 primarily non-peer-reviewed papers on the subject, most of which found that COVID has higher transmissability in colder weather and less humid conditions. One of the papers they summarize indicates that COVID survives and transmits most effectively between 13-19 degrees C (55 to 66 degrees F) and 50 to 80 percent humidity. This seems to line up nicely with the weather during the times where states transitioned from linear case rates to non-linear case rates. It would make sense that a healthier, happier virus would be more effective at infecting its targets (us!).
4. The Oxford Dictionary defines Confirmation Bias as “the tendency to interpret new evidence as confirmation of one’s existing beliefs or theories.” As such, the observations that most states were already in non-linear growth regions well before Thanksgiving and lack of any real evidence that any change in these acceleration rates occurred after Thanksgiving makes me qualify most of these articles about the Thanksgiving outbreaks as likely colored by confirmation bias (I’m sure we all saw lots of articles before Thanksgiving on how it would result in significant case surges). No one can prove that Thanksgiving DIDN’T create any increase in case growth, but there’s really no good evidence to indicate that it did.

Bonus – Top States by Case Acceleration and by Case Deceleration

Note that California has the highest case acceleration rate in the country. This means their IROC_Confirmed Case Slope (New Cases per 1000 residents per Day) will increase by .1211 or higher tomorrow. Note that North Dakota’s case acceleration is still decreasing and appears to be near to the point where they have just a handful of new cases per day.

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:

1. 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).
2. 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:

1. 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.
2. 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:

1. The case growth for Arizona (the orange curve) continues upwards unabated. You can see this in the tables above too, of course.
2. 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.

Above is an interesting way to look at the two outbreaks we’ve had in Arizona and the cumulative number of cases (useful because it shows us the case trends).

1. Note that the deaths seem to be higher during the summer outbreak than during the current one considering the rate of case growth. During this current outbreak the deaths are so far staying under 50 per day, but back even in the earlier phases of the summer outbreak they were inching up to 100 per day.
2. Also, the deaths are just the raw number of deaths and aren’t normalized by the respective populations. What this means is that the red lines represent the total number of deaths over 65 years old (about 13% of the AZ population) and the blue represent everyone else.
3. Deaths during the current outbreak have a ratio of 2.95 deaths over 65 to 1 death under 65. During the summer outbreak the death ratio of over 65 to under 65 was 2.31. This is a pretty big difference and indicates to me that the virus might be getting less deadly for society as a whole. If I knew exactly how old the people dying were it would help (if they average 85 that’s much more informative than just knowing they’re over 65). This may indicate that the “Years of Life Lost” due to COVID is decreasing.
4. In the chart above, the state had lockdown restrictions in place until May 15, then most counties put mask requirements in place on June 9th. Early October is when most of the second set of restrictions on bars, gyms, and movie theaters were lifted. It doesn’t seem like any of these dates are correlated with anything the virus did. Seems like it has it’s own mind…

I decided to evaluate the new cases from the current outbreak differently. Previously I was interested in where case growth as a percentage of previous cases. This may be a useful metric, because it signifies anomalous case growth in a specific location. Presumably that info could be used by a public health organization to target localized outbreaks.

However, perhaps much more interesting would be Zip Codes where high case growth per 1000 residents is happening. The chart below shows this metric. Both the cumulative number of cases (blue) and the last month’s case growth (orange) are normalized by the population of the Zip Code.

What does this chart tell us?

1. On the left of the chart, we see the zip codes (see table below for better visibility into this portion of the chart) that have had the largest number of cases per 1000 residents cumulatively (since the beginning of COVID). A couple of these regions have seen very high growth in the last month. But as your eyes move rightward, you can see some regions that had experienced high COVID cases in the past that had lower numbers of outbreaks in the last month. And of course, other areas (the peaky orange lines) have experienced very high numbers of cases in the last month. It would be good to understand why some regions have had worse outcomes over the last month than other regions. We’ll evaluate some of this below looking at the table.
2. The general trend does seem consistent, though. Regions that experienced higher numbers of cases during the summer outbreak are in general experiencing higher numbers of cases during the current outbreak. I was hoping to see a different trend (that might have indicated immunity in some regions) but will keep watching for that trend to emerge.

Details of the Normalized Case Growth

The below table is sorted by the Cumulative Cases per 1000 in a Zip Code. The Growth-Norm column represents growth in Cases per 1000 over the last month. Note that some regions that have experienced high case growth up to this point didn’t have nearly as large of Case Growth as other regions that had experienced similarly high cases in the past. These are circled in green. You can also see regions with larger than expected case growth circled in red. Are there any factors that might be correlated with this lower and higher amounts of growth?

The first thing that I rule out is Education and Median Age. These on the surface don’t seem to be related. Some regions with lower median age are right next to regions with 15 or so years higher in median age. The same applies for education. What I do see, however, is a trend with population density, where regions with higher population density seem to be seeing lower COVID case growth per 1000 people. This might make some sense if you think about how regions with high density generally always have high populations, and therefore, a larger denominator in the growth per 1000 person equation… However, this also means that the numerator (the change in case count over the last month) is disproportionately low. Which, I think is interesting. Why would there be less cases than expected in regions with higher density? Thoughts:

1. I wonder if this might be an indicator of the effectiveness of government interventions (mandatory masks, school restrictions, etc.)? Since all the data I’ve seen indicate that school restrictions aren’t resulting in large numbers of case reductions (regardless of whether they’re in school or not, every study seems to be showing that people under 15 don’t really transmit the virus), and most regions don’t have restaurant/gym/bar closures now, I’m assuming if it is anything it is the mandating (and compliance!) with the mask restrictions. Unless someone can chime in with a different idea…. Compliance is an interesting thought, because it seems like in a more populous area, there appears to be more social pressure to comply with COVID restrictions. Whereas, my observation is that in less dense areas, the social pressure is much less.
2. Also interesting to me is the resurgence of cases on the border. These regions were very quiet ever since the summer wave slowed down and generally went from having the highest case rates in the country down to the very lowest. But now we see Yuma and Santa Cruz counties experiencing case growth again. Also, the South Mountain region of Phoenix (85042) is also experiencing another surge in cases. But looking at the highest number of new cases per 1000 over the last month, we see some interesting places. Page (up near Lake Powell), Cottonwood (near Prescott), and Douglass (on the border, but only lightly affected during the summer border rash of cases) all are near the top of the list with 85350 in Yuma.

The chart below looks complicated, but don’t let the looks deceive you. Here’s Pima and Maricopa Counties cumulative Cases per 1000 people (and the trend lines) compared to the number of daily tests in the state (it’s trend line is the orange, dotted U-shaped line.

1. You can see the weird bump around 9/20 or so on the Pima County line. That is the first few days U of A tested out their homegrown antigen test on lots of students. The bump represents the excessive false positives in the test (they fixed it, I think). Remember, just because you test positive for something doesn’t mean you have it!
2. The U trend on the tests is really interesting to me. Even as the summer wave was accelerating (far left) we see the trend in tests decreasing. Then when the cases are largely flat we see the trend reverse and start increasing. This could be some sort of psychological effect or maybe the number of tests is some sort of a leading indicator of case rates? This seems like an informative chart, so I’ll post it every week or two.
3. Maricopa’s normalized case rate is around 7 cases per 1000 persons higher than Pima County. This has been sustained since mid-May. Not sure what it reflects, but it could be the greater adherence to government mandates (mask, distancing). Or it could have some demographic cause? It does seem that activity/going to work results in infections, because the normalized infection rates (per 1000 persons) are identical across the whole “working-aged” 20 to 64 age range. The 65+ population has just over 1/2 the rate per 1000 of the working-age group and the under 20 population has just over 1/3 of the rate per 1000 of the working age group (see second chart below).

Here’s the latest update on the current COVID-19 outbreak with backing data. I’ll show a number of different views of the data, including some extra focus on the Arizona data since I have my best data from my own state. Always happy to take requests from folks from other states.

Arizona COVID-19 case growth by Zip Code – 11/7 through 11/15

Note below that we’re still not seeing a whole lot of repeats in the top 30. I think that some geographic areas are surging and then slow down. This week a new zip code from Pinal County is now at the top with 20 percent growth, but since their numbers were already very small, it’s probably not as relevant as 85756 in Pima County, which is the first county that has shown up recently in the top 30 this fall that had significant cases in the summer. It is a relatively large zip code in population, as evidenced by the large orange bubble below. If you look closely, it does appear that the majority of the top zip codes have a relatively young median age. The main exception is 85614, which is a Green Valley zip code and has a median age of 68.

Case Growth Across Arizona over the Last 2 months

Note that 85719 (home of the U of Arizona) stands out over the last 2 months. Fortunately, due to the low median age in this zip code, there appear to have been no deaths and few hospitalizations in this zip code for all these cases. It’s also fairly certain that many of the cases in this region were false positives due to this zip code being the first to use the U of A’s antigen tests, which have been demonstrated to have high false positives. 86001 in Flagstaff also has a major university, and thus, higher case growth over the last 2 months. Initially, during September, 85009 from SW Phoenix had high case growth, but it slowed down and this zip code is no longer in the top zip codes for case growth.

Arizona Cumulative Case and Death Curves

I like looking at the data this way because it becomes clear if growth rates are linear or if they are increasing non-linearly (the upward curve). Right now, we’re seeing nonlinear growth in Arizona cases, but probably more like linear growth of deaths. Deaths seem to be increasing at a rate of about 23 per day, which seems to be just slightly above the average since 9/11. Cases however are increasing non-linearly and the instantaneous slope today is around 2100 new cases per day. It’s hard to tell at this stage because things can change quickly, but it’s possible that the slope of this phase of the outbreak is lower than the slope from the start of the summer outbreak (around June 14th).

Arizona Hospitalization Status

I have been hearing word that the hospitals are heavily burdened by COVID cases again. This is likely very true and may be different in different localities. However, at a state level, there is still no need to be afraid. Here is the ICU Bed status for the State from the state Dept of Health Services dashboard. The increase of COVID patients has been nonlinear since October, but the numbers are still around 20% of the state’s ICU bed capacity. I expect that the grey bars will get squeezed by COVID before this outbreak is over (much like it did in early July).

US State Case Growth Rates

These tables allow you to see case growth rates as well as cumulative case and death numbers per 1000 people. Note that North and South Dakota are still right in the middle of the fight and their case acceleration rate is still quite high (Montana is catching up).

I think until this current outbreak slows that I’ll continue to do weekly data dumps for people who need to see the latest data in a unvarnished, non-manipulated form. Again, I’ll have better data for Arizona since I live in that state and have collected data from the state Dept. of Health Services for much of 2020. Of course, they don’t make it easy to collect the data in any form except the current day, so I have to go back every day and capture the latest. However, by doing so, I feel like I have insights that many don’t have. One of my reasons showing the Arizona data in such detail is that I feel that the behavior of the virus is similar in all regions and perhaps the Arizona results can provide insight into COVID activity in other states.

Zip Code Data

I feel that the Zip Code case data (wish I had deaths/hospitalizations by zip code too, but that is not provided) is valuable at understanding how the outbreaks are trending. For instance, we continue to see the largest case growth for this current Arizona winter outbreak in areas that weren’t hit very hard by COVID during the spring or the summer. This raises a couple of questions… 1) Why is it just hitting these regions now? Some of them are places that people from Tucson or Phoenix travel for vacation. I would have expected the case growth to have occurred along with the big outbreak in Arizona over the summer. The second question this raises is if this is an indicator that we’re seeing the effect of immunity in the areas that were hit hard over the summer (Yuma, SW Phoenix, S. Tucson, Nogales). See the latest charts below on the case growth in the last week across Arizona. Note in the table that there aren’t any obvious patterns in this wave (see my zip code correlation study from July here which demonstrated a number of patterns in the summer outbreaks)

Deaths Per Day

I think a lot of people are fairly aware that deaths have decreased in count since the big COVID waves in the Northeast this spring. I was curious how the “Daily Death” count in Arizona compared between the over 65 and the under 65 age demographics through the big summer outbreak and now in the winter outbreak. The plots below that perform this comparison are stacked bar plots. You can see three things in each bar, the under 65 deaths that day (the height of the blue bar on the Y-axis), the over 65 deaths that day (the difference between the height of the red bar and the blue bar), and the total deaths (the height of the stacked blue-red bar for the day). Hopefully that’s clear enough. But it’s a pretty useful chart, especially for visualizing differences between 2 or 3 groups.

The first plot shows raw numbers of deaths (blue is under 65, red is over 65). Therefore you can see that on the highest day for deaths during July we saw somewhere over 50 deaths in the under 65 demographic, around 120 deaths in the over 65 group, and about 170 deaths total. This is a good way to view the data and it reveals that on most days, there are many more deaths in people over 65. However, this isn’t that informative of a visualization, because the blue bars represent 87% of the state’s population. Therefore the second graph shows the death data normalized by the population of the group. I’m representing it as deaths per 100,000 people in the age grouping so the numbers aren’t too small to be meaningful. Therefore, now you can see that on the same day that we saw the 170 deaths, on the chart with normalized data, this represented about 13 deaths per 100,000 persons over age 65 and just under 1 death per 100,000 persons under age 65. This is a good way to visualize the true impact across age groups. If I separated the age groups under 65 it would be evident that the deaths are far more rare under age 45.

Cases

Now that I’ve demonstrated normalizing by population, here is how the cumulative case curve looks when normalized by the population of each age grouping. You can read this as that the 55-64, the 20-44, and the 45-54 groups all currently have cumulatively reached 45 cases per 1000 persons in their group. Note that this is just the cumulative count, not the number of cases currently active! What this shows us is that case growth for the three groups above has tracked almost identically since June. The interesting points to note, however, is that the over 65 case count when normalized by the over 65 population numbers is much lower (even though their deaths are much higher) and the under 20 normalized case counts is even lower. This tells us a few things. Cases are rarer in the 65+ population and even rarer in the under 20 population. The fact that 65+ deaths have been so much higher on a smaller number of cases shows that getting COVID is much more deadly proposition for this age group.

Hospitalization

A while back, the Arizona DHS improved their hospitalization status chart by adding the COVID cases in. Here’s an example of the ICU bed usage across the state. The other types of hospital bed usage charts look basically the same, but you can find them by following the link above. We see the peak from the summer hitting and the non-COVID ICU patients were squeezed out. Utilization never really went over 90% because of the hospitals’ ability to manage their beds. Then as the hospitalizations from COVID crashed in late July, new patients flooded into the ICU beds to keep the overall utilization around 80%. Now it’s creeping up again due to an uptick of COVID patients. I’m curious (and hopeful!) if the increase in COVID hospitalizations will be more gradual during this outbreak. It seems likely to me, but we’ll have to watch.

COVID-19 US State Table

The below is sorted by the “acceleration” of cases per day. Therefore, North Dakota is seeing an increase of 0.0388 cases per 1000 persons every single day. Therefore their case velocity (IROC_confirmed) of .9640 cases per 1000 persons will likely increase to around 1.03 cases per 1000 per day tomorrow and 1.0688 the following day. This acceleration metric (dIROC_confirmed) is a useful indicator to determine when an outbreak is slowing in a state. When Arizona was number one on this list last summer this metric is exactly where we first noticed the change.
As you can see, the midwestern states are currently seeing the largest case growth, but right behind them are the Northeastern states. I’m hoping and praying that the daily Delta_Deaths metric in all of these regions remains lower than it tended to be during the spring.

Since the trend is once again toward increasing case rates, I’ll just put out there a bunch of graphics and tables so you can see what’s happening.

Arizona Zip Code Case Counts

Since I live in Arizona and have followed it more closely than any other state, it might be interesting to see the trends in Arizona. My suspicion is that other states are seeing similar trends. Our first trend turns out to have been relatively low case rates and high deaths due to infections in communities at higher risk, such as our nursing homes and reservations. During this time period the whole state went into lockdown. Case rates were relatively low and constant throughout this lockdown period and a few weeks after. The second AZ trend was the summer outbreak, which I’m quite confident occurred in conjunction with a large outbreak at the same time in Mexico. During this time, we saw cases increase polynomially and mask rules were implemented across the board but did not slow down infections. If you look back in time on this site, you’ll see that the most heavily hit communities during this timeframe were the ones that had significant ties to Mexico (SW Phoenix, S. Tucson, Border Region). Now we’re in the third outbreak and what we’re seeing is the virus sweeping through the communities not touched during the first two outbreaks. Though case rates are increasing, death rates remain relatively low.

In the above two diagrams, note that none of the heavily-infected zip codes from the summer are present. These are generally areas that haven’t been hit hard yet. This makes me suspect that in this wave there is some element of immunity to COVID being expressed by the harder hit zipcodes from the summer. Perhaps this is a normal balancing we should expect.

The above table confirms the above. The top 5 counties all have had lower case rates to date (see their cases per 1000 numbers). You can also see how the death rates range from the high in Apache and Navajo Counties, both of which were hit hard back in the early days of the outbreak when deaths were higher, all the way down to tiny Greenlee County. Maricopa and Pima Counties, which have the large populations, are somewhere in the middle.

Other US States

The above two tables show the states that currently have the highest accelerations of their case and death rates. Acceleration means that the slope of cases (# cases per day) is getting larger or smaller over time. As you can see, North Dakota is in the unfortunate position of having the largest case acceleration (an increase of 0.038 cases per day, every day) and the largest death acceleration (.0035). Interestingly, though South Dakota has tracked right with North Dakota on cases, the death rates are much lower in South Dakota. This makes me suspect that in ND the virus got into a community somewhere that was highly susceptible but in SD it didn’t. Note that ND’s death acceleration is almost 3x the next highest (Iowa). Fortunately, in this latest outbreak, deaths continue to be rare.

Additionally, we can see the Northeastern states creeping back up to the top of the case and death rate lists. In other regions, it seems like when COVID comes back a 2nd or 3rd time the death rates are much smaller, so what’s happening in the Northeast is puzzling. I’d have to look closer at those states (i.e., by zip code) to figure out exactly what is happening.

World Data

I’ve shown the below diagram a few times throughout the COVID outbreak and interestingly, the trend continues that the virus is unusually inactive or unmeasured between about 10 degrees South and 30 degrees north. The below shows the cases and deaths per 1000 since the start of the outbreak by latitude. Other than the growth in 20 to 10 degrees South (Brazil) and 20-30 degrees North (India) not much has changed. Note that despite, India’s large COVID numbers, the overall number of cases and deaths per 1000 people is still much lower than other regions.

And below shows current states for countries + US States sorted by normalized case growth rate (IROC_c_n). We see tiny Andorra at the top of the list, but a number of European countries are moving back up the list. Note that in Belgium (the country in Europe that had the highest death rate), the current death rate (IROC_d_n) is a good bit lower than Czechia, a country that was largely missed by the first round of COVID.