Nothing much going on in the tropics (couple of suspect areas off the west coast of Mexico), no recent earthquakes, so let’s take a closer look at Georgia COVID-19 fatality numbers, and how to to use them to get a look into the future. Yes, there will be math! And an equation!!
I’m really tired of hearing people say “the spike is because of testing” or “the fatality rates are dropping!” implying that things are not headed the wrong way. Well, the rates are dropping are a little, but that’s not helping as much as you might think. Lets look at the ratio of people dying to new positives. That’s OK in concept, but most people doing those graphs are failing to account for how the disease progresses. On average, it is three weeks between someone testing positive and the date they expire (and exposure to expiration time lag is probably more like 4 weeks). If you don’t take in to account that 21 day time lag, you can reach a wrong and dangerous conclusion. Here is what the wrong calculation (just taking the deaths and dividing by positives) ratio, and a much better calculation (taking in to account the three week delay) looks like if we plot it. Orange is the 21 day lag ratio, and blue is the “wrong” way that is duping people …
So, yes, better treatment and more younger people catching this is probably improving survival rates. But it’s not as dramatic as the proponents of that view suggest. The proof, of course, is in the prediction. Let’s go back in time to the days of yore, May 30th. At that time the lag21 Positive Fatality Ratio was 6.111%. It had been slowly dropping, so we compute the trend and end up with an equation that looks like this:
Forecast = (0.0611-days_since_may_30th*(delPFR – K*days_since_may_30th)*L21P
delPFR: the change in Positive Fatality Ratio as of May 30th (2.8e-4)
K: constant to adjust for improving survival rates (calculated over 1 to 30 May, 9.2e-7)
L21P: the number of positive tests 21 days before the date you are trying to forecast
so with this equation, we can forecast up to 21 days in the future. It’s a very simple model (we have more sophisticated ones), but is easy to explain and only uses reported information. How did it work, and what does it show for the next three weeks? Here’s that plot …
Yesterday the reported deaths were 2849. On June 11th, three weeks ago, the prediction for yesterday was 2913, only a 2.25% error. Properly using simple tools, we can do a pretty good forecast out three weeks into the future. It has worked reliably over the last month, so we can expect it will continue to work well unless something dramatic changes, which is pretty amazing, isn’t it? I mean, except for the fact it is showing that because a bunch of morons aren’t taking all this seriously, and doing very simple things like wearing a mask in congested places, etc., it is very possible that almost as many people will die over the next three weeks than died over the last three months. And we can’t do much about it because it depends on what we did in the past, and even if everybody changes their behavior today, it will take weeks for it to show up in the statistics. But aside from that, isn’t math great? Sigh.