Today’s report uses the data from the 2:50PM Report from the GA Department of Public Health.
Today we saw 999 new confirmed cases (our record is 10379) and 570 new probable cases (our record is 3989), for at total of 1569 new cases (our record is 13296). That brings us to 12343 in the past 7 days (1.1% of total cases so far). We had 8 new confirmed deaths (our record for new deaths 184) and -1 probable deaths (our record is 67), for a total of 7 deaths (our record is 246). That brings our total to to 101 in the past 7 days (0.5% of total deaths so far). We saw 71 new hospitalizations (our record is 611), bringing our 7-day count to 712 (1.1% of total hospitalizations so far). Lastly, we had 21 new ICU admissions (97 is the record), bringing our 7-day count to 152 (1.4% 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 7.22 Cruise Ships full of infections, 1.09 747 Crashes in deaths, 6.66 movie theaters worth of hospitalizations, and 3.77 hotel fulls of ICU patients.
For testing, we saw 13057 new COVID19 tests, bringing us to 132049 in the past 7 days (1.4% of total COVID19 tests so far). We also saw 374 new antibody tests, bringing us to 5452 in the past 7 days (1% of total antibody tests so far).
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.
Beginning July 2021, GA DPH is nolonger reporting new data on weekends or holidays. As a result, these days will show as having zero cases.
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.
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 237060 cases not included in the total count, which would increase the total by about 25.9% increase. These visualizations show how the total case count would look if we incorporated that data.
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 2939 deaths not included in the total count, which would increase the total by about 15.8% increase. These visualizations show how the total case count would look if we incorporated that data.