GA COVID-19 Report July 12, 2020

Daily Summary & Notes

Today’s report uses the data from the 2:50PM Report from the GA Department of Public Health.

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.

Cumulative Confirmed Cases

Cumulative Hospitalizations

Cumulative Deaths

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.

New Cases

For today’s cases, the 30-day mean is 2038.1 and the standard deviation is 984.42.

Hospitalizations

For today’s hospitalizations, the 30-day mean is 135.93 and the standard deviation is 86.42.

Deaths

For today’s deaths, the 30-day mean is 19.43 and the standard deviation is 14.6.

ICU Admissions

For today’s ICU Admissions, the 30-day mean is 20 and the standard deviation is 11.49.

Testing

These graphs contain several markers that reflect the changing nature of the testing data that has been provided over time.

Cumulative Testing

Positive Tests by Source

Total Testing Trends

COVID19 Molecular Testing Trends

COVID19 Antibody Testing Trends

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.

Commentaries

Is Herd Immunity A Viable Solution to COVID-19?

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.

Documentation

Code and data available here. Analysis conducted using R.