A critical question when looking at the daily rate of new US COVID-19 cases is whether we are flattening the curve. I haven’t found an adequate visualization that accounts for a major confounder: Is the rate of new case growth slowing, or is our ability to test saturating?
To get a handle on the question the plot below shows the a plot of new deaths / new cases with the CDC assumption that the lag between diagnosis to death is 13 days. Then I plotted this ratio as a percent (red) against the cumulative confirmed cases. Cases confirmed less than 13 days ago are assumed active (blue) and plotted by extrapolating out the most recent deaths / cases percent value. (Edit: I changed to a 10 day lag given that most testing results are probably still not same day).
To interpret this chart, we need to consider what would happen under a couple of different scenarios:
- New infection rates are actually decreasing; the red deaths/cases ratio would trend downward to some expected case fatality rate probably and the remaining active cases in blue would decrease. This would indicate a decreasing span between the current number of cumulative cases now and what that number was 13 days ago, and that testing is catching up so that you get a realistic rather than inflated case fatality rate.
- New infection rates are not decreasing but testing can’t keep up; the blue span of active cases would decrease but the deaths/cases ratio would go up. The increase in the deaths/cases ratio would be the tip off that testing is falling behind artificially shrinking the blue span.
- New infection rates are not decreasing but testing is catching up; the deaths/cases ratio would decrease but the blue span would continue to increase. The decrease in deaths/cases would be the clue that testing is catching up despite the widening span of active cases.
In effect the blue span of active cases tells us how far we still have to go, while the red deaths/cases ratio tells us whether this distance is over or under representative.
Scenario #1 is what we hope for an would allow better forecasting of the likely deaths. Scenario #2 would indicate that deaths are likely to be worse than might be expected from the data at face value. Scenario #3 is not good but at least a more realistic forecasts of the likely deaths could be made.