So Omicron was discovered and it was only a matter of when the number of cases in my good condition started to increase. It looks like 12/8 is when it started here. Similar to Greg Abbott in Texas, our Governor, Ron DeSantis, didn’t say anything or anything stupid. Any mitigation in this state will be voluntary by people and therefore lag behind in terms of effectiveness.

I have come to choose 12/8 as a starting point not only with the naked eye, but also by looking at the rate of change of this 7-day moving average curve. 8/12 is a day when the direction of change has changed …

Like any good exponential function – or in this case * CLOSE* exponential (that’s actually the Logistics function), we see rapid growth after modestly slow increase.

_{The logistics function, is the solution to Verhulst’s equation, which is often used to model population growth that slows down as a “saturation point” is approached. To be really geeky, the curve of new daily cases is the first derivative of the logistics function – really called logistics distribution …}

Now I am lost in estimating these logistic curves in a * Finite difference* world – it’s in a spreadsheet. So the “rate” parameter that I use and put in the following figure is influenced by 2 choices I make in my model – I use 300 “days” and I model the time value in the actual calculations of – 5 to +5. By playing with this spreadsheet for a year and a half, I realized that the interaction of these 2 variables can lead to the exact same curve …

So I don’t really know how to put k = 8.25 in pure mathematical terms … but

- This is a value that I have NEVER had to tackle before in my math games.
- It is about 5 times higher than the K factor I was getting during the July-August surge at the start of the surge

So I think we can conclude that this is spreading quickly. How fast?

Well, let’s look at the Delta surge from 7/7 to 7/22 of this year versus the “Omicron” surge of 12/15 *:

By my calculations, this increase is * 160% faster,* until now. A few days before, I calculated 77% faster … oh my God.

Now, polishing a shit – er, my forecasting tools, I started trying to fit the data from 12/8 to 12/23 on Christmas Eve. The following model was the closest to the data received for Friday – but had a modest number of infections expected for the state – around 600K (recalling that these models are for 7-day moving averages, the actual numbers could be from about 25% higher). It seems small compared to the numbers I’ve seen from IHME and our local university (USF). They say 40,000 or more a day here in Florida from mid-January to the end of January, I think. I get a peak on 12/30 in this model:

_{Note – Tc (= 300) is the number of “days” in my model, Ts1 is the first time step of these 300 which is used in the curve fit.}

One thing I noticed over the year and a half of using these mathematical methods to model case peaks is that in reality the factor K (roughly the growth rate) and A (the expected number of people infected in the peak) do not remain constant. K tends to drift down, A tends to get bigger, resulting in surges that last longer than one would expect by doing simple calculations.

So that got me to think of another way to look at the estimate of A. In the discussion of the PRA, something prompted me to think of basing A on the rate of testing done in the state. . For Florida, that equates to about 1 test per person in the state per year, so a 6 week period (sort of an arbitrary and wild guess). Would give about 2 ½ million tests. I suspect that we 1) might see more, and 2) are going to see a ridiculous positivity rate in these tests in January … so I decided to use ~ 2/3 of that number (1,750,000) for A in the next model, using a factor of K divided by two:

This one peaks a little higher than the estimates I have seen / heard … but the timing is closer to those estimates. This peaks on 12/1/2022 at around 58,000 new cases per day (again, 7-day moving averages are modeled here) …

Now another scary guess that I have heard (I think from Peter Hotez, not from a jamoke) is __1 million cases per day__ for the country. If Florida contributes, it is the percentage of the population that is 60 to 70,000 per day in this state. I decided to do another arbitrary and crazy calculation to squash the two models together … 5%. So in 20 days (1/12/2022) this model adds the 2 separate models, but by the time we get to 100% model 2 the first model is quite small and gets smaller and smaller. faster…

This gives me a 2 week plateau from 1/1 to 1/15 2022, between 60 and 70,000 per day … which is close to one million per day increased by the population of the United States. It still seems to level off too quickly …

Now since at the start of the Delta push I was calculating K factors a little less than 2, maybe the 2nd model should slow down even more (take K less than 4), maybe adding some MAGA / additional anti-unlucky. vaxxers to stats (bump A up to 2 million)?

Without actually plugging in the numbers and looking at the results, I suspect it could make a shorter plateau further into January, perhaps at a slightly lower peak. So this will be the signal I’m wary of in the data, but it will be a week before the Christmas crash subsides, and then we’ll have a New Years crash. So we may not be able to really get a good read on the situation for a week and a half. If I remember correctly, Hotez recently commented on the data joke in this country. A very fast virus exploits this defect …

So be careful and be well.

_{* originally 12/8, but this figure shows when growth took off quickly}