If you’ve been following this blog, you’ll know that I am tracking the daily number of cases of COVID-19 in Santa Clara county and showing the results graphically. Now that I’ve tracked the number of cases of COVID-19 for about three weeks, I am going to base my projections of future cases on the trend of the rate of increase rather than on the most recent daily rate of increase. This is more realistic than assuming that rate of increase will continue unchanged over time.

Here is a graph of the rates of change of the number of daily cases of COVID-19 that shows the trends in these data:

The blue line in the graph tracks how the rates of new COVID-19 cases changes daily, while the gold line is the 5-day moving averages of the daily rates. One of the reasons for computing the moving average is that it “smooths” out the data; in this case it makes it easier to see a *downward trend in the rate*!

If, and this is a *big*

, we assume that the rate changes approximately linearly (i.e. follows a line), we can use a linear regression to estimate what that line is. The line for the moving average is shown in green, while the daily rate’s line is shown in red.*if*

In the graph below I use the trends and the assumption of approximately linear change of the rates to project the number of future cases:

Notice that if we can keep up the trend in the 5-day moving average, the curve is noticeably flattening! While this model depends on the assumption of approximately linear change in the daily rates of change and *does not* *predict* future results, it does show one possible future outcome.

If we can only continue this trend…