Daily Summary & Notes
Today’s report uses the data from the 2:50PM Report from the GA Department of Public Health.
Today we saw 3406 new cases (our record for new cases is 3472), which brings us to 19179 in the past 7 days (19.1% of total cases so far). We also had 21 new deaths (our record for new deaths 100), which brings us to 94 in the past 7 days (3.2% of total deaths so far). We saw 307 new hospitalizations (our record is 442), bringing our 7-day count to 1175 (9.6% of total hospitalizations so far). Lastly, we had 30 new ICU admissions (54 is the record), bringing our 7-day count to 148 (6% of total ICU admissions cases so far).
For testing, we saw 20499 new COVID19 tests, bringing us to 145574 in the past 7 days (14.9% of total COVID19 tests so far). We also saw 224 new antibody tests, bringing us to 13397 in the past 7 days (7.8% of total antibody tests so far).
Today we cross 100,000 confirmed cases of COVID19 in Georgia. Given that we have a population of about 10 million people in Georgia, this means that 1 in every 100 Georgians has tested positive for COVID19, and half of those are within the past 31 days. Today sees our second largest 1 day increase in cases, and 4th largest 1 day increase in hospitalizations. It’s also worth noting that the Georgia Hospital Resource Report is showing that our hospitals are currently slammed, with major saturation and multiple hospitals in diversion mode.
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).
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 1619.07 and the standard deviation is 846.4.
For today’s hospitalizations, the 30-day mean is 118.03 and the standard deviation is 67.92.
For today’s deaths, the 30-day mean is 23.97 and the standard deviation is 18.65.
For today’s ICU Admissions, the 30-day mean is 18.73 and the standard deviation is 9.62.
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 17099.1 and the standard deviation is 6438.78.
COVID19 Molecular Testing Trends
For today’s new tests, the 30-day mean is 14819.7 and the standard deviation is 6199.24.
COVID19 Antibody Testing Trends
For today’s new tests, the 30-day mean is 2279.4 and the standard deviation is 2403.19.
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.
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 0.98! 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 0.97. 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 0.579839. 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 0.61. By the observational nature of our data, we can’t infer causation, and we can’t remove eliminate extraneous factors, but we can observe that the association between these two variables is limited, and that the increases in cases cannot be attributed purely or even primarily to the quantity of testing occurring.
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.