Daily Summary & Notes
Today’s report uses the data from the 2:50PM Report from the GA Department of Public Health.
Today we saw 958 new cases (our record for new cases is 4813), which brings us to 10571 in the past 7 days (3% of total cases so far). We also had 18 new deaths (our record for new deaths 122), which brings us to 170 in the past 7 days (2.2% of total deaths so far). We saw 19 new hospitalizations (our record is 447), bringing our 7-day count to 699 (2.2% of total hospitalizations so far). Lastly, we had 4 new ICU admissions (97 is the record), bringing our 7-day count to 164 (2.8% of total ICU admissions cases so far).
It is important to note that the case count presented here only represents cases confirmed with a PCR test. Patients who only receive a rapid antigen test are considered “probable” cases in Georgia and are not reported on a daily basis; instead these are reported in the weekly County Indicator Reports. Adding these to the count would add 27,736 new cases as of 10/26. It is unclear why Georgia does not include these in the total case count, as this is a common practice in other states and the antigen tests are seeing increased use across the state.
I realize these numbers can be hard to put into context, so here’s an alternative metric. In the past 30 days we’ve had equivalent of 12.67 Cruise Ships full of infections, 2.49 747 Crashes in deaths, 11.74 movie theaters worth of hospitalizations, and 25.51 hotel fulls of ICU patients.
For testing, we saw 16356 new COVID19 tests, bringing us to 147109 in the past 7 days (4.3% of total COVID19 tests so far). We also saw 509 new antibody tests, bringing us to 6760 in the past 7 days (2.1% of total antibody tests so far).
You can access an interactive version of these graphs, including embedded data here.
Prior to 5/11, all data is taken from the noonish update from the GA Department of Public Health to present even time intervals between data points which is important for graph interpretation. On 5/11, reporting schedule shifts to being at 9AM, 1PM, and 7PM, so this report will capture to the 1PM reporting time. On June 2nd, reporting was reduced to once a day at 3PM. Data does reflect multiple inefficiencies and inaccuracies in the current reporting system, including showing tests before their results are returned, delays in reporting on weekends that create artificial spikes and valleys in change data. In general, interpretation should examine the general trends, and not focus exclusively on endpoint trajectories, which are highly influenceable by these data variations.
To help visualize the effects of State actions on the outbreak, I’ve added a few sets of lines to several of the graphs. The first — the vertical blue lines — show when the state of emergency went into effect (3/15; solid line) and when we might expect to see first effects from it (dotted line). The second — vertical red lines — is the Friday Shelter in Place was instituted (4/3; solid line) and the date we might expect to see first effects (dotted line). The third — vertical pink lines — show when the shelter in place was lifted (4/30; solid line) and the date we might expect to see first effects (dotted line).
In addition, to help visualize change in graphs using cumulative data that spans large counts, both linear and algorithmic scales are offered. You can read more on interpreting graphs using log scales here.
Where point data is presented, a LOESS regression with 95% confidence intervals is shown to help the viewer interpret overall trends in the data. This is preferred over a line graph connecting all points, which tends to over-emphasize outliers in report.
Cumulative Confirmed Cases
Cumulative ICU Use
Count Level Tracking
Z Score Fluctuations
Because percentage growth becomes misleading over time, I’ve added a floating 4-week Z-score visualization for each measure to help put into perspective the magnitude of daily variation in numbers.
For those who don’t spend a lot of time in the world of statistics, a Z score is a measure that describes the relationship of an observation (in this case, a particular day’s number) to the average across the entire group. It is calculated by taking the difference between the observation and the mean, and dividing by standard deviation.
Z = (Observed Score — Mean) / Standard Deviation
For example, if the mean score for a group is 50, and the standard deviation is 10, then a score of 60 woud have a Z score of (60–50) / 10 = 1, and a score of 20 would have a Z score of (20/50) / 10 = -3.
This can be useful in identifying patterns in data reporting, and help put daily fluctuations in perspective. Because the data is more localized, it doesn’t fall victim to the diminishing returns effect. These visualizations are limited to the data from the last 30 days, which further helps illustrate trends and fluctuations.
For today’s cases, the 30-day mean is 1266.97 and the standard deviation is 376.64.
For today’s hospitalizations, the 30-day mean is 97.8 and the standard deviation is 57.82.
For today’s deaths, the 30-day mean is 30.43 and the standard deviation is 13.33.
For today’s ICU Admissions, the 30-day mean is 21.77 and the standard deviation is 13.77.
These graphs contain several markers that reflect the changing nature of the testing data that has been provided over time.
As of 4/28 specific counts of the number of tests administered by the government and commercial providers stopped being reported. Additionally, on this date we began to track data on the number of positive tests conducted by the CDC.
On 5/27, specific counts of serology tests (antibody tests) became available, which had previously been aggregated into the total test count. This date has been marked with a vertical gold line on the graphs. This distinction is important, as positive antibody tests do not result in new cases in the overall count, and thus both suppress the positive test rate and artificially inflate estimates of test prevalence. The daily data for daily COVID19 tests and serology tests is tracked starting on this date.
Positive Tests by Source
Total Testing Trends
For today’s new tests, the 30-day mean is 20529.53 and the standard deviation is 7280.44.
COVID19 Molecular Testing Trends
For today’s new tests, the 30-day mean is 19634.43 and the standard deviation is 6963.42.
COVID19 Antibody Testing Trends
For today’s new tests, the 30-day mean is 895.1 and the standard deviation is 511.4.
Is Increased Testing Causing Increased Cases?
A popular talking point recently is that the increase in cases that are being detected is not reflective of increased spread, but rather a result of increased testing. There is a certain logic to this — the more tests that are run the more potential cases we can identify. However, this can lead us to significant logical errors, and these in turn can lead to dangerous behaviors. While our data does not allow a perfect causal analysis, we can examine what associations between testing and cases exist in our data.
Correlations Between Testing and Cases
If we run a simple correlation between total number of tests and total number of cases, we get an initially persuasive graph. Note that this graph includes both antibody and molecular tests.
This gives a correlation of 0.9963085! This is inviting, but it mostly just shows that both of these numbers are increasing. This is potentially misleading because it looks at cumulative data. In fact, if we run a correlation between the total number of tests administered and a simple series of ascending numbers (1, 2, 3, etc.) we get a correlation of 0.981721. Because our hypothesis (increased testing causes increases in reported cases) is more about fluctuations in these two variables than cumulative growth, we need a different analysis.
If we look at the daily increase in cases against the daily increase in tests, we get a different picture:
This gives us a correlation of 0.7568782. But this number is also misleading, because there are significant time lags in reporting of tests and new cases within the data.
To better assess the relationship, let’s look at 10-day moving averages for both new tests and new cases, and see what correlation exists between them. This will help balance out the issues of delayed results.
This gives us a correlation of 0.84. By the observational nature of our data, we can’t infer causation.
As I’ve watched this plot evolve over the past few weeks, I think it’s starting to become clear that we have two covid19data$different distributions happening. The first is the relatively flat group of cases you see across the bottom, which has characterized most of our COVID19 response. During this time, we had relatively stable case numbers regardless of whether we saw heavy testing days or light testing days. In this group, it’s pretty clear that testing and number of cases detected aren’t strongly related. However, we also see a second group of cases, which seem to veer upwards rather abruptly at around 12500 cases per day. These data points are more recent. If our data was limited to these cases, you could make a case that there’s a strong association between increased testing and increased case identification. As it is, our correlation estimate ends up sitting somewhere between the two lines.
Having watched the data evolve, my hypothesis is that the increase in testing is a response to the increase in cases. of particular relevance to this hypothesis is the gap between our two “groups” above the 15000 test per day level. I think if testing were the driver of new case identification, then we’d see greater variation at the higher testing levels, rather than two distinct low variation areas forks. When we consider the hypothesis that increased case reports spurred an aggressive increase in testing, the pattern here makes more sense. When the number of cases detected at ~12500 tests per day began increasing, testing itself was escalated to try to keep up. This hypothesis would also account for the differing curves between new tests per day and new cases per day.
To further explore the relationship between testing and outcomes, let’s consider the relationships between testing and both deaths and hospitalizations.
Correlations Between Testing and Deaths
This gives a correlation of 0.9924606! This again shows the issue with simply comparing linear additive functions.
If we look at the daily increase in deaths against the daily increase in tests, we get a different picture:
This gives us a correlation of 0.2696899. Of course, this number may also be significantly misleading — after all people probably aren’t dying the same day their test results come back! Since we don’t have an average time from infection to death, and because that’s probably a wide range, let’s explore a 14-day moving average.
This gives us a correlation of 0.54.
Correlations Between Testing and Hospitalizations
Considering hospitalizations, we get this:
This gives a correlation of 0.9950544! This again shows the issue with simply comparing linear additive functions.
This gives us a correlation of 0.2859727. Again, there’s probably some lag between hospitalization and when people get test results back — some are likely hospitalized before results are returned, and vice versa — so let’s look at a 10 day moving average.
This gives us a correlation of 0.59.
Correlations Between Cases and Hospitalization
Lastly, let’s look at the correlations between these indicators and cases themselves.
Considering hospitalizations, we get this:
This gives a correlation of 0.9944724! This again shows the issue with simply comparing linear additive functions.
This gives us a correlation of 0.6399275.
This gives us a correlation of 0.89.
Correlations Between Cases and Deaths
Considering deaths, we get this:
This gives a correlation of 0.9844673! This again shows the issue with simply comparing linear additive functions.
This gives us a correlation of 0.4316724.
This gives us a correlation of 0.5, and a really weird shape.
What do we make of the information from these new graphs? I think there are a few takeaways. First, it’s safe to say that while the increase in testing does create an increased ability to detect cases, it is not the reason that cases are increasing; after all we’re seeing similar escalations in hospitalizations and deaths which couldn’t be caused by increased testing. Second, like with the correlations between new tests and new cases, we can see that there seem to be multiple groupings within this data, which likely reflect periods of escalated testing in response to increased cases and changes in how we treat patients diagnosed with COVID19. Ultimately the story we see here is much richer and more complex than those who want to blame pandemic numbers on testing are willing to acknowledge.
Comorbidity (Written 7/15/2020)
I think today is a good time to remind people about comorbidity risks. I often see people insist that they have no risk because “only people with pre-existing conditions get COVID”. While pre-existing conditions are associated with increased risk, this misses both that healthy people with no prior conditions get COVID, and that what’s counted as pre-existing conditions is pretty broad. The GA DPH website indicates that the following are considered comorbid conditions in COVID19 data reporting: Chronic Lung Disease, Diabetes Mellitus, Cardiovascular Disease, Chronic Renal Disease, Chronic Liver Disease, Immunocompromised Condition, Neurologic/Neurodevelopmental Condition, and Pregnancy. These are very prevalent conditions here in Georgia — Over 6.9% of adults have COPD or other lung disease, more than 1 in 10 Georgians have diabetes, and more than 1 in 3 Georgians have some sort of cardiovascular disease. I could pull stats fo r the other conditions listed, but the implication is clear — a large proportion of our citizens are at elevated risk. Most people likely either have one of these comorbidities, or are close to someone who does, and don’t recognize the risk.
Physical Distancing (Written 7/31/2020)
Today I want to talk briefly about social distancing. The guideline that’s been shared is to maintain 6 feet distance between people. Unfortunately, many people struggle with this. The struggles tend to fall into two areas.
First, some people are not good at judging what 6 feet away is; most people I see are treating 3–4 feet as 6 feet. Often people also may start at 6 feet away, and slowly close that distance (sometimes unconsciously). With all this in mind, let me give you a few ways of thinking about what 6+ feet looks like:
- If you could shake hands without moving your feet from where they are, you’re too close.
- If you could fall face forward — just straight face planting into the ground — and the other person could catch you, you’re too close.
- If the person could hit you with a baseball bat without leaving where they’re standing, you’re too close.
The second issue is that many people interpret “stay at least 6 feet apart” badly. Much like how people interpret a speed limit of “55MPH” as “drive 55MPH, if not more”, people interpret social distancing stay “6 feet between persons, if not slightly less”. Aside from making the absolute minimum safe distance the norm, this also tends to ignore the reality of human beings as 3D creatures.
Consider the following situation: You have a line 30 feet long in front of a service counter. How many people can stand in that line and maintain social distancing? In the abstract, we might quickly calculate that 30/6 is 5, or even conceptualize 6 if we assume we can put a person at spot “zero”. Now let’s think about actual human beings. Let’s assume the first person in line stands 1 foot from the service counter. If we assume a personal space bubble of about 2 feet, then a minimum of 6 feet away for the next person is 9 feet from the window; subsequent spots are at 17 and 25 feet. Suddenly we’re down to 3 people in our socially distanced line. If some of our “spots” are filled by groups of people, like families or couples, then we have to build even more space.
With those two issues in mind, I encourage you to think about social distancing as 10 feet away rather than 6 feet. This accounts for our poor spatial judgment, tendency to drift closer, and the issues of humans being 3-dimensional. This also creates space to move in and around people if you’re working in a classroom or retail environment.
On Reduced Testing (Written 8/30/2020)
I’d like to spend a little time today talking about a big problem both here in Georgia and elsewhere regarding information and attitudes about COVID19. As you may have recently seen in the AJC demand for COVID19 testing is down in Georgia. This is alarming, as Georgia has never really tested at levels sufficient to contain the virus, and the decrease will only worsen our ability to monitor and intervene. Decreased testing means more cases will not be detected, which will increase community spread and further distort our understanding of the outbreak. So let’s talk about why testing is going down, and what can be done about it.
One major factor is poor communication about the utility of tests in recent weeks. Recently the press has become fascinated with stories of false positives. This is a real thing that happens, and happens with tests for most medical conditions; however it’s also pretty uncommon — studies of PCR tests suggest a false positive rate below 5%. In fact, you’re much, much more likely to get a false negative, and that problem of false negatives has very real human costs. Where clusters of false positives have occurred, they’ve generally been issues with the labs reporting tests and have been quickly identified. But unfortunately, this has led to considerable misinformation and conspiratorial theories spreading. Rather than recognizing that a national testing campaign may occasionally have errors, COVID deniers have chosen to interpret the existence of these false positives as proof that all positive tests are false, spread unsubstantiated claims that all tests come back positive, and insinuate that asymptomatic cases are all false positives. For people not versed in the nuances of testing, this can be dangerously persuasive.
Meanwhile, significant efforts have been underway to blame individuals for becoming infected and attribute this as a personal fault rather than acknowledge that our public health response has forced people to risk unavoidable exposure to the virus or face financial ruin. This can be seen pretty much everywhere, but it’s particularly visible in Universities, where administrators have chosen to bring students to campus and then shame them for getting infected, ignoring the realities of elevated risks that occur in student housing and poorly ventilated lecture halls and instead blaming parties. While there is some truth to the concerns about parties, the risks inherent in university programming and the risks that students assume when they take on work with the public where they’re often unable to enforce safety guidelines is a much greater concern. This is essentially just a COVID variation of the millenial shaming that’s been a favorite past-time of university administrators for some years. Rather than making students take more precautions, these strategies mostly discourage students from testing and reporting, and makes them less likely to be honest with contact tracers if they do test positive.
On Underlying Conditions (Written 8/30/2020)
Yet further discouraging testing is people’s focus on the idea of underlying conditions. This has been particularly prevalent today, following news reports saying “94% of Covid-19 deaths had underlying medical conditions”. People are reading this and interpreting it to mean that only 6% of people who die from COVID would have survived if not for underlying conditions, and that the risk is wildly overblown.
That would be noteworthy, so let’s look into this a bit. The claim comes from Table 3 on the CDC Weekly Update, which is captioned:
Table 3 shows the types of health conditions and contributing causes mentioned in conjunction with deaths involving coronavirus disease 2019 (COVID-19). For 6% of the deaths, COVID-19 was the only cause mentioned. For deaths with conditions or causes in addition to COVID-19, on average, there were 2.6 additional conditions or causes per death. The number of deaths with each condition or cause is shown for all deaths and by age groups.
If we look at the table and associated data, however, we quickly see a that this is somewhat misleading. What this table is actually doing is reporting “Conditions Contributing to Deaths where COVID-19 was listed on the death certificate”. It’s important to understand that over two thirds of death certificates list multiple causes of death, and this is generally considered a good thing from a health standpoint — he inclusion of multiple factors associated with the death helps us better understand disease interaction and progression. This is different from reporting the “underlying cause of death”, which is the illness that is considered to have precipitated the death, which is often difficult to specify, and which the CDC table does not address. When we consider that when COVID is fatal, the death is usually a result of respiratory or organ failure resulting from damage done to the heart, lungs, liver, and kidneys, then it makes sense that most COVID19 death certificates would list things like pneumonia, adult respiratory distress syndrome, respiratory failure, respiratory arrest, ischemic heart disease, cardiac arrest, heart failure, renal failure, and sepsis as contributing factors. If we factor in that 6 in 10 US adults have at least one chronic disease, and 4 in 10 have at least 2, then it also becomes no surprise that these make an appearance on the table. And given the promotion of the Miracle Mineral Solution, AKA Bleach, it’s not surprising that around 5000 of these deaths seem to be poisoning related. Taken all together, it’s surprising that any COVID death certificates don’t list additional contributing factors, let alone 6%!
Because there are conspiracy theorists desperate to capitalize on any data inconseistency, I should mention that the reason that the table lists only 161,392 deaths is NOT because the CDC is quietly removing deaths from the count; it’s because the table relies on limited and delayed reporting. The national data shows 182,149 deaths and 5,934,824 cases.
As a final note, the recent focus on deaths as the only indicator of disease threat is a big problem. We are seeing that COVID19 has significant long term health effects on people who survive it, ranging from ongoing symptoms of fatigue, a racing heartbeat, shortness of breath, achy joints, foggy thinking, a persistent loss of sense of smell, and damage to the heart, lungs, kidneys, and brain. Hospitalized COVID patients are at elevated risk of blood clots that can cause strokes, heart attacks, lung blockages, and other complications, and this is particularly prevalent in young patients. Other residual symptoms include neurocognitive impairments, and elevated risk for psychaitric disorders. In children, we’re seeing Multisystem Inflammatory Syndrome. And because myths about children being immune continue to spread, we should note that we have evidence of cases and deaths in children as young as 1 year old. Lastly, we should remember that we’re only 9 months into this virus, and we don’t know what the true long term effects will be. Dismissing the health impacts on survivors is short-sighted and likely to exacerbate the human suffering and economic damage this virus will cause.
As always, I am not trained in epidemiology, and defer to recognized experts in the field on all issues. These analyses and commentary are solely designed to help lay persons approach the publicly available data and larger public health conversations.
Wash Your Hands.
Wear a Mask.
Code and data available here. Analysis conducted using R.