Tribalism comes for pandemic science

Ok, so I spent some time digging into their source code. This is a model, and like many models it's output is fully dependent on the input. In this case we're dealing with significant inputs and assumptions tied to factors such as onset, delay, testing correctness and a lot of things we just don't have data for.

I'm going to share some quotes from the code.

> Our goal is to translate positive test counts to the dates where they likely occured. Since we have the distribution, we can distribute case counts back in time according to that distribution. To accomplish this, we reverse the case time series, and convolve it using the distribution of delay from onset to confirmation. Then we reverse the series again to obtain the onset curve.

> We assume a poisson likelihood function and feed it what we believe is the onset curve based on reported data. We model this onset curve based on the same math in the previous notebook

> We calculate this by figuring out how many cases we'd expect there to be yesterday total when adjusted for bias and plugging it into the first equation above. We then have to re-bias this number back down to get the expected amount of onset cases observed that day.

I don't want to invalidate efforts to build models because models are important, but it's also important to frame models as being statistical models and in this attempting to resolve R0 values from self-reported data, across districts with dramatically altered reporting methodology is extraordinarily fraught with peril. Even the model authors indicate this and have made several improvements, and models will naturally evolve over time.

Then let's touch on some of the stats we're looking at. Remember we are dealing with a sampled phenomenon - as our sampling rate changes (the amount of testing we're doing), our case rate will naturally rise. We want more testing because it helps us determine more accurate numbers, but it also means we need to understand we may see spikes in our daily reported cases. We therefore want to be careful with using small samples (3 or 4 days).

You'll also notice we have natural declines that align with the weekends - it's not because the disease takes the weekends off. One of the dangers of reading the worldometers graphs is that they're inclusive of all the data, including data from March/April which is probably underestimate.

Texas started lessening quarantine restrictions on May 16th, and indeed until about the 29th we see a pretty consistent decline (well past any incubation time). From the 30th to today's data (and always take the current day with a grain of salt because reporting isn't always instant) we do see a growth, primarily due to several abnormal spikes in the last week. When we compare that against deaths we don't see those same spikes represented - and in the last two days of the series (excluding today) we see a declining pattern again (but two days a pattern does not make).

So it's wrong to say "Texas has about 1500 new cases per day now. They've had a steady increase in new cases per day since mid April, when they were about 750 new pos cases/day. They are really growing.". The 7 day average keeps us at 1200 or less, with the exception of the last few days.

I pulled the hospitalization data from [^2] (xlsx format unfortunately) and while hospitalizations are technically increasing as a whole, that is almost entirely in the TSA of Houston (though Dallas/Ft Worth and Longview/Tyler show some increase as well) with 19 other TSAs declining or remaining steady. So, I wouldn't be overly concerned for your family, the data indicates things are generally going quite well.

I would be careful in how we read data.

[^2]: https://dshs.texas.gov/coronavirus/additionaldata/

How many kids die of the flu each and every year? How many kids die of malaria? Of tuberculoses? Where were you during the last strong flu season? Get off your high horse and actually contribute ideas instead of fear.

I am honestly and hearteningly surprised to even hear these sentiments from someone else here, which I have now observed for years. It's one reason why I love HN.

I'm not in the science field, but I absolutely am a student of human nature (and mindfulness). It's unbelievable how much the mechanism of science is dirtied, distorted, and abused by our emotional pride, greed, tribalism, fear, and all other aspects of human nature, which show that - for all our intelligence - we humans are wired for survival, and only wired for truth if and when it helps that survival. If truth comes at odds with it, we will lie - even if to ourselves, and at very deep subconscious levels - as much as we need to.

Rationalisation: https://en.wikipedia.org/wiki/Rationalization_(psychology)

How this biological reality plays out is complex. It's not just in our lizard brain, but the entire realm of our secondary limbic brain, with its 'tribal' swirl of social emotions and resulting behaviours. 'Survival' means a great of things, not just literal survival right in front of you right now. It's a powerful and primary goal coursing through our blood and all behaviour at the deepest and most subtle levels. We would do well to educate about this fact, and practice mindfulness to be able to see it in ourselves.

It's not like there was one unified message about this, ever. Yes, it was commonly understood that preventing "avoidable" deaths from stressed hospitals was a sufficient reason to lock down, but that doesn't mean that it was unanimous that it was the only reason. From the beginning, if you ask ten people what "people are saying", you'll get ten different answers. My own impression of what the public was initially told does not match up to yours.

What you say is not true - it could be true potentially but the data shows that's not the case. Tests counts relatively stable recently but pos counts went up and percent of tests that were positive also increased.

See https://www.covidexitstrategy.org/. As you see on this page, many states are increasing absolute number of tests - Texas is actually steady for a week at around 18k tests per day. But the % of people testing positive is increasing (see that same website).

On that page scroll to 'how is my state doing on testing' and look at Texas, it's interesting to compare states. Notice last week number of daily tests was around 18k/day. Test positivity went from 5.5% to 7.7%.

This isn't a political issue, it's just the actual numbers. Compare to say Wyoming (increased tests unlike Texas from 472 to 690 per day, test positivity went down there). Oregon is getting worse (2.4k tests/day, in the last week 1.5% to 3.4% of tests are pos).

Texas is having increasing covid-19 positive tests, it's not from increasing the number of tests and icu bed space is going down. This website gives the data source so you can look at it yourself.