As always, I’m capturing the state of the COVID pandemic through data. See below for the latest data across the US on the “Delta Surge”.
Current US State Status
Above is the standard Data Table that I build from the Johns Hopkins COVID data. You might note that the Case Rates (IROC_confirmed) and Case Accelerations (dIROC_confirmed) are increased over the previous two posts here and here. The rate that Lousiana’s case rate is increasing is surprisingly high… perhaps the highest acceleration I’ve seen yet for a whole state. This may be another data point demonstrating how quickly this delta variant spreads.
Hot Spot Counties
Above we can see a number of interesting things about the current Delta outbreak. First, the Louisiana Parishes at the top have really high rates and accelerations. This is one of the big reasons the whole state of Louisiana is surging. The top three parishes are all medium sized parishes that sit in between Baton Rouge and the New Orleans area, so perhaps their outbreaks are related.
The case rates and accelerations continue to inch upwards in the previous hotspot areas (Missouri/Arkansas border and Jacksonville, FL, area) but they’re not racing up anywhere near as quickly as Louisiana.
Finally, despite all these new cases, death rates are still extremely low… about 5 to 10 times lower rates of deaths per 1000 persons per day than back in January during the winter outbreak. For instance, Apache County, AZ, had the highest case rate in the state at this time (.728) but had a death rate of .033. Compare to any of the counties in the table above. They all have higher case rates than Apache County during January of 2021 and the highest death rate I see is .0082 in Phelps County, MO.
All I can take away from this is that 1) the Delta Variant is less deadly than the variant spreading in January, 2) our medical system has gotten much better at treating COVID, or 3) the deaths are lagging and we’ll start to see them showing up later. Of course we have the variable of vaccinations present now which could be impacting 1) above by making the virus less deadly in a society of a mix of vaccinated and unvaccinated victims.
Hospitalization Status in AZ due to COVID
Above is the current status from the state of Arizona of hospital beds. The Arizona case numbers are creeping up but are still relatively low (see below). Hospitalization (ICU) due to COVID is increasing, but it hasn’t yet hit the rates that were seen even in April of 2020. The trend here will be a good indicator of how serious this Delta outbreak is.
Here are the latest updates for those of you who want to see the data.
COVID by State
The most interesting thing to note from above is that the acceleration column (dIROC_confirmed) is getting larger in the top 15-20 states ranked by their Case Rates (IROC_confirmed). See my post from July 15 to see the difference. You’ll also note that the case rate is increasing pretty much across the board, but for most of the lower-ranked states, it’s a small increase. So where (which counties) are driving these increases?
COVID by County
So we’re continuing to see a large case rate in some rural Missouri and Arkansas counties. Nassau and Duval Counties in Florida have jumped onto the list. These two counties are both in the Jacksonville metro area. If you add Camden County, Georgia, (just north of Nassau county) into the mix, it looks like some sort of local spread event, perhaps. The outbreak might have begin in Camden County and worked it’s way down… This article from mid July indicates that only 28% of eligible people in Camden County had been vaccinated. This Jacksonville, multi-state metro area has an overall case rate and acceleration that might be driving much of the overall Florida numbers.
Therefore, I see basically three major local events in the top 20 or so counties: 1) Arkansas, Missouri, Oklahoma border area 2) Jacksonville, FL, metro area, and 3) Midland, TX (why?). This leads me to believe that this variant IS extremely transmissable — it has spread pretty quickly in these areas, but I believe these areas have relatively low vaccination rates.
Arizona COVID by County
Above is the data for Arizona as of 7/24. Here we see the bottom four counties in case rate (and all with pretty low accelerations too) along the border. Note in the NYT visualization below that Pima, Santa Cruz, and Coconino Counties all have pretty dark colors, i.e., high vaccination rates. Mohave, Pinal, Maricopa, Greenlee, and Yavapi Counties all have the lowest vaccination rates. This is similar to what we see above… the Delta variant seems to be growing fastest in low-vaccination areas. I’m not sure if this trend holds… things may change. But for now it does seem like Delta is very transmissable, but very localized (and possibly highly correlated with low-vaccination areas). And fortunately, as you can see, deaths remain very low as of this date.
Above you can see in my map of case rates and accelerations by counties there are a couple of large regions of outbreak. One hovers over the Arkansas, Missouri, and Oklahoma border areas and the other hovers over Jacksonville and S. Georgia. This is a pretty good picture of how non-uniform the current COVID Delta Variant outbreak is. The outbreaks also appear to correlate strongly with the low vaccination (light green) areas on the NYT visualization.
I’ve been seeing lots of articles alleging that the rate of infection is shooting up across the country. LA County is re-ordering the wearing of masks indoors, even by people who have been vaccinated. Does any of this make sense?
Above you can see the current rates. Anyone who has read this blog for a while is likely to notice that the case rates for each state (IROC_Confirmed) are still quite low (see the table for April 28th here for a comparison). If you look around at my older reports you’ll find that Arkansas’ rate of .229 cases per thousand persons is a pretty low rate compared to previous leaders which were 3 or more times higher. But are the rates growing each day (accelerating)? In some states we see non-trivial accelerations. Nevada’s acceleration (dIROC_Confirmed) is causing the case rate to increase by .0171 cases per thousand every day. Missouri is at .0182. However, most states’ accelerations (while they are non-zero) are fairly small. Texas is pretty close to zero. My guess is that their case rate is falling. California doesn’t even show up on this list (their case rate is .031 and their acceleration is .0021).
I suspect that some of the panic amongst our journalists is the fear of the case rates doubling or tripling again like they did last summer. Or perhaps there’s just not enough to write about? If you look below, you can see that there ARE some counties that have really high rates. Most of these are in Arkansas and Missouri. As these states share a border, this appears to be a local situation more than a US national trend. You can see that the case rates in Baxter County, Arkansas (in the north of the state near the Missouri border), are about 4 to 5 times higher than the overall state rate. The second highest case rate is in Taney County, Missouri, which is quite close to Baxter County. I can’t figure out why the case rate is high in Midland County, Texas. There’s nothing about an outbreak on their County COVID website, so who knows.
Do take note however, that the rate of deaths is extremely small. This is likely to do with the better resistance that vaccinated people’s immune systems make to an infection.
Los Angeles County Case Rates over Time
Below you can see the Confirmed Case curve for Los Angeles. An increase in slope is barely perceptible today, but you can see that cases have essentially been flat since about February.
As COVID numbers slow in my state (Arizona) and across the US, it’s difficult to see much in the way of trends. Here’s a quick update since the news outlets aren’t talking about the data much anymore.
Things to note. 1) The case rate is very low, even in the highest county (Mohave). I do believe this is a strong indicator of “herd immunity” through vaccination and natural immunity. 2) The counties at the top of the list are fairly rugged, individualist counties. I’m not sure their vaccination rate, but I could imagine that it might be lower. 3) Maricopa (more permissive) and Pima (more strict) had very different approaches to governmental restrictions about COVID. But at this point, their numbers are pretty much identical when normalized by population. There are a lot of papers coming out evaluating the effectiveness of governmental action during COVID. They’re not being highlighted much, but in general there’s not much confidence that the governmental actions accomplished much. Here’s a small sign that might demonstrate that point. 4) Yuma and Santa Cruz are both border counties that have the highest cases and deaths per 1000 persons. They appear to have been most affected by unconstrained outbreaks in Sonora, Mexico. This may point to the outcomes experienced with little to no government action (on the part of Sonora). Combined with the point from 3) above, this might demonstrate that there is an effect from some level — even small — of government measures, but that at some point, government action becomes ineffective.
This table shows us that the case rate is very, very low for the majority of US regions. The only two regions with any rate growth (acceleration) are New Jersey and Puerto Rico. The rest of the states have essentially zero change in their case rates (which as stated before, are already very low). New Jersey is very interesting, as they’ve had the most consistent rate growth of any state through the whole COVID pandemic. When other states’ rates would flatten out, New Jersey’s would keep creeping upward. They also have the highest death count per 1000 persons of any other state. No idea why this might be.
The above two charts represent 1) the top 8 states by deaths per 1000 persons and 2) Cases per 1000 for a selection of “interesting” states. I include the deaths chart just to show the crazy effect of the big outbreak in the Northeast during the first few months of the pandemic. It took most of the others in the top 8 until November 2020 to catch up to the death rates that New York and New Jersey had in July. The other chart shows that high cases and high deaths are not correlated. Note that the top three on this list don’t appear in the top 8 deaths chart. New Jersey and New York are the only two states that appear in both charts. Of interest is New York’s and New Jersey’s unique case slope. They is mostly linear between November of 2020 and May of 2021 where all the other states here experience steep surges offset by plateaus. No idea why this might be.
These two tables sum up the two stories around countries around the world. The first shows the ones with overwhelming numbers (India, Brazil, Columbia, etc.) that make the news. The second shows countries that are disproportionately affected. In many cases, small countries like the Seychelle and Maldive Islands top the list, but you can see that Sweden, Czechia, and Chile are crowding them. These all have pretty high case counts for their populations. Finally, below you will note the countries that are experiencing high death rates normalized by their population sizes. These are places where deaths are very disproportionate. Note that Brazil and their near neighbors are high on this list and India is missing. The large numbers of deaths in India are just as tragic as deaths anywhere, but the ratio of deaths to people in Peru and Brazil are likely more overwhelming to those countries.
Here’s a quick update on what is happening in the US and around the world. See analysis below the images.
A bit over a week ago in my last post, Michigan was leading the US in case rates. Since then, their cases have collapsed and Michigan was replaced at the top by Colorado, then Oregon, then Alabama… This makes me suspect that these outbreaks (they’re all very small compared with the peak in January) are somewhat isolated. Above, the data tells us that the highest acceleration of cases is happening in Alabama but the largest slope (cases per 1000 persons per day) is in Oregon. It’s not surprising to see lots of cases in Oregon as that state has been very lightly touched to date. A similar effect is happening just north of Oregon in Washington. The sixteen states above are the only ones that show a daily increase in the case rate (dIROC_confirmed). The rest of the states are seeing case rates slowing.
The above chart shows something interesting that I have highlighted. The Arizona ratio of deaths in the over65 demographic to deaths in all other demographics is slowing significantly. What this might mean:
The data is small (deaths are down significantly) and this is a statistical anomaly.
The over 65 demographic — which seems to be getting close to “fully” vaccinated per the AZ DHS data — is being protected by the vaccine from severe responses to COVID. We all suspect that this is the case, but here’s some data showing a pronounced shift since about mid-March.
Above you can see the new cases and deaths on 5/8 from the top 17 countries. There’s lots of news about India and the terrible things happening over there as their hospital system gets stressed, but in actuality, on a per capita basis, India, Brazil, and the US are in much better shape than the leading countries in the chart below. India’s 400K cases yesterday represents about .3 cases per 1000 people, which is about half the number of Peru from 5/8.
At some point in early 2021 the more mediocre articles about COVID in the popular media started to slow down and memes in social media began to disappear. Much of the focus in these places turned to vaccines and the new US administration’s approach. It seemed like a good time to slow down my COVID updates and instead spend time watching. For a while I have been concerned that overwhelming people with data and analytics about COVID was contributing to the overall problem of fear and distrust (although it seemed necessary in light of the poor communication from government and media). However, I’ve continued to collect data and run analytics. Here’s the latest data for those who are interested.
United States Situation
The Latest on Interesting Analytics
The above is the table that I have shown throughout the Pandemic. You can scroll back a few pages and see this table during the different outbreaks for comparison sake. Right now there is one big outlier, Michigan, interestingly one of the states with the most restrictive (or at least publicized) COVID policies. The good news is that Michigan’s current case rate is still only about half of what the highest state was during the winter outbreak. The next highest regions have significantly lower slopes. This is an unusual trend and may be indicative of the success of the vaccination policies that the states have put in place.
Arizona has significantly lower Case Growth than other states right now. The highest counties in the state are those who were spared a bit more during the recent outbreak. This has been a pattern throughout… the virus finds regions that haven’t been hard hit and then runs for a while until it runs out of targets or temperatures move outside the virus’ comfort zone.
One metric that I have been tracking is the ratio of deaths in groups over age 65 to deaths in groups younger than 65. In Arizona, only around 13% of the population is over 65 but this ratio is still somewhere about 3:1. This is a sign of how overwhelmingly COVID has impacted the over 65 age group. During the first large summer outbreak in AZ we saw a ratio of about 2.3:1. During the most recent winter outbreak, we saw the ratio peak up over 3. This increase might mean that right now there are less susceptible people under 65 than there was during the summer of 2020. The green line on the chart above is the moving average of this ratio. The recent peaks in this line are primarily due to the small numbers of deaths being recorded now.
Here we see the case growth curves for both Maricopa and Pima counties with the number of COVID tests per day superimposed. This metric (number of tests) is not a perfect metric but it appears to be a solid indicator of upcoming acceleration or deceleration in the COVID case curve. You can see for yourself that when it starts trending in a direction, a change in the case curves comes 4-5 weeks later. Currently the number of tests is approaching the low water mark but this remains a good number to keep watching to give us an idea of whether there will be summer outbreaks again this year.
This chart shows the percent of tests conducted yesterday with positive test results. The blue dots are the daily results and the gray curve is the best-fit line that describes the trend. The tests positive number hovered near 50% for a while and now is stable around 8-10%. It is concerning to observe that the curve shows signs of turning up again. If true, this may be an indicator of another summer outbreak in Arizona.
India has been in the news recently for their large number of new cases and deaths. 323K new cases in a day would be enough to intimidate anyone. The raw numbers seem scary, but note that the case slope (IROC_c_n) is very low for India (.2724). What this means is that right now, as a percentage of their population, the rate of new cases is quite a bit lower than other countries (see below). If one considers case acceleration (dIROC_c_n), we still see that India’s number of an increase in .008 cases per 1000 persons every day is small compared to countries like Turkey, Columbia, and Argentina.
We can see that India isn’t even in the top 10 in the world with regard to normalized case slope (IROC_c_n). Turkey, however, is very interesting with both a high raw count of cases and deaths, but also a very high normalized Case Slope. Turkey also has a very large population, but not nearly as many as India, so their numbers of new cases each day will be shocking (43K yesterday), but not as difficult to comprehend as the 323K new cases yesterday in India. So even though India isn’t as fully-saturated with COVID as Turkey or some of the South American countries, it’s still a problem for the world. India is one of the largest manufacturers (maybe the largest) of pharmaceuticals in the world. COVID vaccine manufacturing has dropped significantly since the start of India’s second wave. This is an example of how COVID can drive non-linear effects.
Updates on Interesting Analytics
I have showed the above a number of times during the pandemic. The trend still holds where the latitudes between -10 and 20 have experienced very few cases and deaths due to COVID.
I have also shown multiple revisions of my correlation studies over the last year. In the above we are demonstrating the levels of correlations of various measurable features with cases per 1000 for countries across the world. The top factors associated with COVID cases continue to be smoking and BMI metrics. What this says is that in regions where smoking rates and Body-Mass Indicies are high, we have seen larger numbers of COVID cases. It may not be surprising to see this. Population over 65 is also another feature highly correlated with Cases per 1000 and the inverse correlation of COVID cases with the prevalence of Tuberculosis in a country is still quite interesting. I know there were some studies into whether TB innoculations were somehow providing protection against COVID, but I lost track of those.
The above correlation is for death counts per 1000 persons. As with the Case correlation project, we see that smoking and BMI measures are strongly correlated with COVID deaths. Population size over age 65 is also (unsurprisingly) correlated with COVID deaths as well. For some reason, the growth rate of the country is inversely correlated with COVID deaths. Perhaps this is because a region that is growing is adding infrastructure like hospitals? This might seem to contradict the observation above that the number of hospital beds is correlated with COVID deaths, but my suspicion all along has been that countries with large hospital bed counts are probably recording more COVID deaths that go unrecorded elsewhere.
It’s pretty interesting to see that the COVID case/death trends by latitude have continued. I suspect some of this could be attributed to a lower population in some latitude bands combined with a focused COVID outbreak, but since this is population-normalized, it probably only applies at the 50 degrees and northwards latitudes…
Equatorial regions still have a significantly lower case and death count. Poorer reporting could be partially involved, but can’t describe this big of a difference. Surprised this isn’t discussed more.
Around the end of January I posted this table and about half or less of the states were showing deceleration of their COVID case growth rates. This could be seen in the dIROC_confirmed column where negative numbers are deceleration and positive numbers are acceleration. Now, about 12 days later, every state has negative growth acceleration and the overall growth rate numbers are much lower. This happened very quickly. You can see that Arizona — who had the largest growth rate in the country as of 1/31 — now is much lower and has the largest deceleration number in the US.
As winter weather patterns still differ across the US, I’m curious if this reflects growing numbers of COVID vaccinations
Below are the Arizona numbers by county. One interesting point to note is that Maricopa County has a higher deceleration number and a lower overall case growth rate than Pima County. This is counter-intuitive because Pima County has enforced much more restrictive COVID policies during the entire outbreak. I have heard (but haven’t researched) that vaccines are easier to get in Maricopa County due to a more efficient rollout by their County Medical Office. Perhaps this is reflected in these numbers?
Here’s the current state of the primary analytics that I maintain on a daily basis. The data for these comes from the Johns Hopkins University and from the AZ DHS. Occasionally the data diverges between these two organizations, but not wildly.
I thought it might be interesting to share what I look for in these tables and graphs, especially now that the case rates are slowing down in Arizona and across much of the US.
The above table is familiar to anyone who has read one or two of these pages. It sorts each state by their overall case growth rate. I call this the Instantaneous Rate of Change (IROC) because what I’m measuring is the current slope of the COVID Case curve. The states can be compared because the COVID cases are normalized by each state’s population. This table can help one to quickly identify where the hotspots in the US are. The Confirmed Case IROC tells us how many Cases per 1000 persons per day each state is experiencing. I sometimes refer to this as the case growth rate. The dIROC_Confirmed column tells us how fast the case growth rate itself is increasing. Above, we see that Arizona has the largest case growth rate in the country but that this growth rate is decreasing by 0.0275 cases per 1000 per day — every day. When a state’s dIROC_Confirmed starts slowing, it is an indicator that the case growth rate peak is near. When the dIROC_Confirmed goes negative, it is a sign that the peak is in the past and the case growth rate is now decreasing. I captured this table all the way down to California, who about 2 weeks ago had the highest case growth rate in the country by a pretty good margin. As you can see their case growth rate continues to decrease every day. It looks like Arizona will be seeing similar numbers in about a week since it’s peak was about a week and a half ago.
This table also tells us interesting things about cumulative numbers. You can see that Arizona, Rhode Island, and Utah all have very high cumulative cases per 1000 population. This probably reflects the overall populations of these states being smaller or more confined to larger population areas. Much of Rhode Island’s population is near Providence, Utah’s population is primarily in the Salt Lake area, and a majority of Arizona’s population is in two metro areas, Phoenix and Tucson. Also, most of these regions have seen recent case surges while states like NY, MA, and NJ have had a long lull since their primary surge back in March. One other metric on this chart that’s useful to understand the state’s situation is Deaths per 1000. The Northeastern states still have the largest number of deaths per 1000 persons, but other states like Louisiana, Mississippi, and Arizona are catching up.
Arizona Case, Death, and Hospitalization Data
Now I’m switching primarily to Arizona numbers. These may or may not be similar to what is seen in other states, but I have pretty good access to Arizona data as well as the motivation (being an Arizona resident) to tracking them more closely.
The above chart is the visualization of the Case per 1000 curves for five different age demographics in Arizona. During the initial summer case surge, it was clear that all the age groups between 20 and 54 tracked together and had the same case rates when normalized by their population numbers in the state. The 65+ group has tended to have lower cases per 1000 and the under 20 group has far lower cases per 1000. This chart tells us that this trend continued until about mid-November, when the 55-64 group really separated from the herd (this is the green line above). It also appears to the naked eye that the 65+ line (maroon) also had a slightly increased slope over the under 55 groups starting around mid-November. As I’ve mentioned in other postings, this likely represents contribution to cases from winter visitors to Arizona, none of whom are represented in the Arizona population for their age group. Therefore, they’re contributing to the numerator (cases) without contributing to the denominator. This may just be a curiosity, or maybe it has a hand in Arizona’s particular COVID case and hospitalization challenges since the beginning of October. It appears to me too that while the age groups under 54 (red, yellow, and blue) have started significantly flattening out — meaning that their growth rates are slowing) — the over 55 groups seem to be slowing less.
The above is a chart that I look at a lot and like because it compares the case curves for Maricopa County (Phoenix) and Pima County (Tucson). This is interesting as a natural experiment, because it seems provable that Pima County has imposed much more structure around COVID protocols than Maricopa County and has done so pretty consistently since the beginning. The Blue line is the polynomial equation fit to the Maricopa actual case data (light grey) and the Red line is the equation for Pima actual data (dark grey). The change in the current surge rates is revealed where the real data diverges from the polynomial. This started around the first week of January, but I could see signs of it beginning at the start of 2021. The other interesting signal in this chart is the moving average of tests per day in the state. I’m only showing the numbers of tests, not the positivity of these tests, in this chart because my theory is that the number of tests is a leading indicator for case growth (this likely measures how sick people think they are… few people get COVID tests for fun, I’ve observed). Looking above, you can see that the number of tests moving average hit a low around 9/08 when the case rates were increasing only a slight amount and appeared to be doing so linearly. The case rates started curving upward somewhere starting early October and the test numbers increased along with it. The test number peaked somewhere around the holidays and then gradually slowed. This may have anticipated the case rates starting to slow somewhere in early 2021. This may or may not really be a leading indicator, but the data I have makes it seem interesting at least.
Comparing Deaths over 65 with under 65
The over 65 population in Arizona makes up about 13% of everyone in the state, but has experienced most of the deaths. At some point, I started tracking the Over65 to Under65 ratio as an indicator. You can see the above as the green line. This is the 5 day moving average of this ratio. The light blue bars are raw deaths for the under 65 group (87% of the population) and the red is the raw deaths for the over65 group. The right way to show this data would be normalizing it by each group’s population, but if I did that, the blue wouldn’t be visible unless I plotted it on a logarithmic scale (very hard to explain to non-math majors). The green line is maybe the most interesting element of this because of the comparison of the ratio during the smaller summer outbreak (June/July) where the ratio hovered around 2.5 with the ratio during the current surge, where the ratio jumped quickly and has been ranging between 3.0 and 4.0. This is hard to analyze, but it appears like the virus is more deadly now for the older demographics than it was during the summer. Perhaps the cooler weather compounds the virus’ effect or perhaps there are less susceptible persons under 65 now than there were during the summer? If you’re curious about the spikiness of the ratio during the lull from August until October, I’m pretty sure it’s just due to the small numbers of deaths during that time frame. Small data’s statistics can behave oddly.
Above is what I call my “experimental” metric on a ratio of current hospitalizations to cases from one week earlier. As you can see above, I do this ratio for each age demographic. The basis for this analytic is that I was curious about the rate that people get hospitalized one week after they are confirmed with COVID. Perhaps 2 weeks might have been a better gap, but I chose to use one week because I felt that might be a more common time frame between a case and a hospital visit (any kind, ER, ICU, Outpatient, etc.). Each datapoint represents the daily ratio and since the trends are hard to see with the naked eye, I fit a regression line to each age group so I could see the trends. It doesn’t show it here, but though all the trendlines on this ratio show a decreas now, but back around November, the over 65 group’s trendline was increasing pretty steeply. This changed sometime around the holidays and all the age groups have been trending down ever since. This indicates to me (but doesn’t prove, of course) that hospitalization surged in the over65 group pretty strongly early in this outbreak. It was likely this over65 group that filled the hospitals (see below) very rapidly starting November 1. You can see above that the maroon and green (over 65 and 55-64) dots around that timeframe frequently ranged between 50 and 75%. This means that the number of people hospitalized during that timeframe over 55 was 50-75% of the Confirmed Case counts for those demographics one week earlier. Now the numbers for over65 are generally under 25% and the 55-64 are generally under 10%. This is not a perfect metric, but it does seem to be illustrating a trend and was possibly a good leading indicator for the COVID hospitalization easing that can be seen below.
Finally, I’ll re-show the latest from the AZ DHS on the breakdown of COVID, non-COVID, and empty ICU beds in the state. This is a stacked bar chart and the three states above add up to 100%. So for instance, at the hospitalization peak around Jan 5th we could see just over 60% of the ICU beds in the state occupied by a COVID patient, about 30% of the ICU beds occupied by a non-COVID patient, and about 10% of the ICU beds empty (my guess is that these were the broken ones??). Today, we see about 50% COVID, 40% non-COVID, and still 10% unoccupied. If this follows the trend from the summer, we’ll soon see the COVID hospitalization numbers start dropping even faster.
The Case data for the Arizona winter outbreak has shown some unusual characteristics that might be unique to the state. The chart below shows a few of these anomalies.
The cases began acceleration almost to the day that temperatures in Phoenix dropped from 15 to 20 degrees F overnight. The cooling trend only stayed for a few days (see the orange and blue bar chart below), but nighttime lows remained lower until November 8th when the temperatures plummeted about 20 degrees again. These drops plus the movement of the nighttime lows below 60 degrees seems to have triggered the case acceleration that has only recently began to slow.
The green line in the diagram below represents the 55-64 group. This plus the 65+ group makes up the bulk of Arizona’s annual winter visitor population. Arizona State University did a study a few years back that showed that the population in the Phoenix metro area swelled by 300,000 every winter. I have heard that Tucson’s population increases by over 100,000 every winter and areas in the western part of the state (such as Quartzite) are said to have over 2M visitors every winter. So why is the 55+ groups both accelerating faster than the 20-54 groups? My guess is that the winter visitors are bringing COVID cases with them, and since I’m normalizing by the population of these age groups that lives year round in Arizona, I’m showing a higher number of cases per 1000 “year round” residents. I suspect if I could correct the denominator of this ratio with the actual numbers of full-time plus winter residents each month, then the rates would equal the 20-54 groups. What does this tell us? Some decent percentage of our case counts during the winter outbreak are due to winter visitors. (edit: Using some back of the envelope math I arrived at about 10K cases in the 55 to 64 age group due to winter visitors. This would mean there have been around 90K winter visitors in this age group since November)
Just before Thanksgiving the case rate appears to have slowed a slight bit, but around the end of November, we start seeing strange case count disruptions and apparent acceleration. Looking closely (I’ve blown this range up in the graphic right after the one below), it seems like a short glitch that is probably due to cases being counted inconsistently during the holiday break. Some days had no cases reported and others had nearly as many positive cases as tests. Obviously that was the state catching up on counts they had accumulated when they were out of the office. I don’t see any strong evidence of a holiday surge for either Thanksgiving or Christmas. At best there could have been a slight acceleration, but it appears more likely that inconsistent data collection may have given the impression of a short uptick in cases.
The under 20 age group (blue curve) continues to show that the rate of infection in this group is significantly lower than the other groups. This may be due to our inability to measure these cases well (since so many are asymptomatic) or due to this group’s lower probability of getting infected. Or maybe it’s because this group is more sheltered than others because they stay at home, don’t need to go to work, aren’t buying groceries, etc. Or some combination of these. Remember from my recent excess death analysis that this age group had around 1/2 (on average) the deaths during 2020 than would be expected in a normal year.
For the last week or two, the cumulative curves for all the groups (less so the 55-64 group) show obvious deceleration. This coincides with the decrease in COVID hospitalization that I show below.
Below you can see the zoomed in area where the data glitches occurred. The overall case slope during these times appears to be constant, so the appearance of case acceleration during the holidays is probably due less to a holiday surge than to poor data collection.
Below is the hospitalization chart from the AZ DHS dashboard. It is a stacked bar chart, and the combination of the colored bar percentages will add up to 100%. The Red bars reflect COVID cases in the ICU (60% of all ICU beds today) whereas the dark grey reflect non-COVID ICU beds (about 30% of all ICU beds today). The light grey is the unoccupied beds (about 10%). As you can see the numbers of beds occupied by COVID patients has consistently been dropping for a week or two. This is consistent with how the Summer outbreak worked. Hopefully this means the hospitals are through the worst of it.