An April Look At Covid

Covid has continued to ravage the planet in recent weeks, but the collective appetite of Americans to indulge in public health measures is quickly approaching zero. On my personal Facebook page, over the course of the pandemic, I have periodically shared some stats and analysis about the current state of the pandemic. Today, I will do so here.

The model I have been using for some months now to predict deaths from Covid is much simpler than some of the high-end statisticians use, but it has still proven effective for the most part.

First, I used to track and record the daily cases and deaths in the United States. I started with June 1, 2020, and here’s my reasoning: Before June 1 (in the early days of the pandemic), accurate case and death counts were sketchy, and I wanted accurate data for my predictions. Granted, by May and June of 2020, the data were largely good, but I decided on June 1 to be totally sure the data were accurate.

Here’s the key assumption I make in the model: After N days of X people becoming infected with Covid-19, a certain percentage P of those X people will die. The main statistics I had to figure out was the N and the P.

One of the first thing I did, since this time-series data, is run the Cross-Correlation Function (CCF) to test for what lags are most prominent between cases and deaths. The best lag was -22, ie, case count preceded deaths by about 22 days. Over the past 6 months I have been running this model, the -22 day lag has remained remarkably consistent. The message is clear: Someone that will die from Covid typically, on average, dies about 22 days after diagnosis.

Figuring out the percentage P was a little trickier, but I programmed a loop that iterated the sum of squared differences between 22-day-old cases and current-day deaths to find the one that minimized the sum of the squared errors the most to within one-hundredth of one percent. This number has NOT be stable over the 6 months I have ran the model. Actually, it has been monotonic, but decreasing. (This makes sense – as a virus propagates, it is advantageous to the virus to get less lethal over time.)

For example, in late November (near Thanksgiving), the virus had about a 1.65% death rate, in that, Covid Case count on Day 1 turned into 1.65% of those cases dying by Day 23. Today, on April 18, that number is down to 1.41%. This also makes sense because vaccines are taking effect, and many of the most vulnerable have already gotten vaccinated. Still, it is a perilously low drop – only about .25% drop in the past 6 months. And the overall death rate still stands at 1.46% percent, according to my model, going back to June 1. There is some hope here that eventually the death rate will make the virus become a nonfactor, but its rate of decline has plateaued (it was 1.45% for the month of January).

Here is the plot of the two time series:

Death rates of Covid19 victims since June 1, ending April 17, 2021

There are so many interesting tidbits about this graph. First, notice the big dips – these mostly correspond to holidays, when the reporting went down due to facilities closed for the holiday. The slight dip in blue/red around day 90 is Labor Day, the larger dip around day 160 (blue) and day 182 (red) is Thanksgiving, and the huge massive dip around day 190 (blue) and day 212 (red) is the Christmas/New Years holiday. They are “fake” dips, in that it is unlikely true numbers actually dipped during these times – it is likely just lack of human recording during the holidays.

Also notice overall how well these two plots match overall: There is only a cumulative error of 5.8% in the red graph (cases) to predict the blue graph (deaths), and the correlation between them is 0.97.

What does this tell us about the coming weeks? Well, the upward trend in Cases (red line) definitely will likely produce an upward trend in deaths soon (the blue line). If the prediction (the dotted line) is correct, we could see over 1,000 deaths per day again over the next 3-4 weeks.

No doubt vaccines will make a difference, but we are reaching a limit very soon; the number of people willing to get vaccinated will soon be reached. We likely will not reach herd immunity. This gives the pandemic air and room to keep going and keep mutating. I fully suspect the pandemic, in some form or fashion, will be with us most or all of 2021. Why? The tail of the Cases graph above was already sliding down at an increasingly slow speed, and now it’s heading back up again. I think if this pandemic does end anytime soon, it will be a slow, agonizing end, not a definitive stopping point.

We, as Americans and as a planet, should take heed at the alarming signs. Will we? Doubt it. We are just too sick and tired of it all. I feel like Americans as a whole just want to let it ride and take our chances, and if that’s the case, chances we will most certainly take.

Until next time.

%d bloggers like this: