GA COVID-19 Report February 21, 2021

Daily Summary & Notes

Today’s report uses the data from the 2:50PM .

Today we saw 1463 new confirmed cases (our record is 10379) and 295 new probable cases (our record is 3597), for at total of 1758 new cases (our record is 13296). That brings us to 20748 in the past 7 days (2.1% of total cases so far). We had 4 new confirmed deaths (our record for new deaths 184) and -2 probable deaths (our record is 58), for a total of 2 deaths (our record is 246). That brings our total to to 873 in the past 7 days (5.2% of total deaths so far). We saw 106 new hospitalizations (our record is 611), bringing our 7-day count to 1374 (2.5% of total hospitalizations so far). Lastly, we had 3 new ICU admissions (97 is the record), bringing our 7-day count to 167 (1.9% of total ICU admissions cases so far).

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 44.11 Cruise Ships full of infections, 10.07 747 Crashes in deaths, 27.21 movie theaters worth of hospitalizations, and 6.88 hotel fulls of ICU patients.

For testing, we saw 22719 new COVID19 tests, bringing us to 189168 in the past 7 days (2.7% of total COVID19 tests so far). We also saw 1478 new antibody tests, bringing us to 7296 in the past 7 days (1.6% of total antibody tests so far).

You can access an interactive version of these graphs, including embedded data .

Data

Data Notes

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 .

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

Probable Cases

Georgia counts cases that are reported using rapid antigen tests as “probable” cases rather than “confirmed” cases is they are not subsequently confirmed by a PCR test. These data have only become available as of 11/3. As of today, this represents 180693 cases not included in the total count, which would increase the total by about 22.5% increase. These visualizations show how the total case count would look if we incorporated that data.

Cumulative Hospitalizations

Cumulative Deaths

Probable Deaths

Georgia counts deaths that occur when a patient only has an antigen test, or when a patient has clear symptoms of COVID but no PCR is test is applied before death, as “probable deaths” rather than “confirmed deaths”. These data have only become available as of 11/3. As of today, this represents 2111 deaths not included in the total count, which would increase the total by about 14.4% increase. These visualizations show how the total case count would look if we incorporated that data.

Cumulative ICU Use

Change Patterns

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.

New Cases

For today’s cases, the 30-day mean is 3235.4 and the standard deviation is 1259.62.

Hospitalizations

For today’s hospitalizations, the 30-day mean is 226.77 and the standard deviation is 104.23.

Deaths

For today’s deaths, the 30-day mean is 98.77 and the standard deviation is 60.08.

ICU Admissions

For today’s ICU Admissions, the 30-day mean is 26.37 and the standard deviation is 15.73.

Testing

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.

Cumulative Testing

Positive Tests by Source

Total Testing Trends

For today’s new tests, the 30-day mean is 31943.17 and the standard deviation is 9400.77.

COVID19 Molecular Testing Trends

For today’s new tests, the 30-day mean is 30672.1 and the standard deviation is 9079.74.

COVID19 Antibody Testing Trends

For today’s new tests, the 30-day mean is 1271.07 and the standard deviation is 582.45.

Final Thoughts

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.

Commentaries

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: . These are very prevalent conditions here in Georgia — , , and . 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 . 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 . This is a real thing that happens, and happens with tests for most medical conditions; however it’s also pretty uncommon — . In fact, you’re much, much more likely to get a , and that problem of false negatives has . 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 , 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 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 . 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 , 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 , and this is generally considered a good thing from a health standpoint — he inclusion of multiple factors associated with the . 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 , 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 , then it also becomes no surprise that these make an appearance on the table. And given the promotion of the , 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 . Hospitalized COVID patients are at elevated risk of , and this is particularly prevalent in young patients. Other residual symptoms include neurocognitive impairments, and . In children, we’re seeing . And because myths about children being immune continue to spread, we should note that . 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.

Final Thoughts

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.

Stay Home.

Wash Your Hands.

Wear a Mask.

Documentation

Code and data available . Analysis conducted using R.