2/ We extended the SEIRS model to support stratified isolation levels for healthy <60 and vulnerable individuals.
3/ We forced the model to predict daily deaths curves and the reported age serology ratio for key metropolitan areas in Europe. The immunity level estimations obtained were: Madrid 43%; Catalonia 24%; Brussels 73%; and Stockholm 65%.
4/ We predict that, with the exception of Brussels, no location can return to normal life without having a second wave (albeit in Stockholm a smaller one). Those predictions are more sensitive to population, population over 60, and the reported deaths than to the IFR.
5/ For locations far from the herd immunity threshold (HIT) we searched what isolation values allow to return to normal life in 90 days while minimizing final deaths. Shockingly all isolation values for healthy <60 were negative. (i.e. coronavirus parties)
6/ Assuming an ideal 1-day long vaccination campaign with a 77% efficacy vaccine, we compared predicted final deaths of those 90-day strategies for all possible vaccination dates against a 180-day long vaccine waiting strategy with mandatory isolations.
7/ We found that mandatory isolations (i.e. schools and workplaces closed) produces more final deaths if the vaccination date is later than (Madrid: March 7 2021; Catalonia: Dec 26 2020; Paris: Jan 12 2021; London: Jan 25 2021)
8/ Secondary findings suggested by the model include: 15% of SARS-CoV-2 deaths are 'with' the virus; Very low infectiousness period may be the cause of deaths curves valleys after lockdowns; An early wave of a competing variant could explain the low death rates in Asia.
9/ Finally, we propose that the probability of covid-19 disease-related damages suffered and caused by a single individual choosing NOT to completely isolate should be used to cover those risks.
10/ If some virus is so contagious and so damaging to a large proportion of the population that the disease-related damages risks can not be covered, then that virus pretty much will be the end of normal life. SARS-CoV-2 is not.
12/ If the Brussels prediction looks wrong in retrospect is because Belgium does something others don't. They include possible cases in their death series... Are you serious Belgium?😡
13/ As you can see, when we remove the suspected cases (as we do in every other country) this is the proper behavior to expect. A clear second wave for Brussels too.
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1/ After almost 1.5 years of studying cancer research for personal reasons, I arrived at a realization that prompted me to write this tweet. I will lay out the hypothesis in this thread.
2/ Disclaimer: I am not a formally trained health researcher. More like a very curious and tenacious guy with a 15+ year background in research, development, & reproducibility in computer science (computer science).
3/ I am putting the hypothesis out there because it may make sense to others doing field work. Feel free to dissect this hypothesis, find holes in it, and play devil's advocate. We will all come out smarter from it.
1/ There is a very perverse dynamic on how Chavism (aka "the communist socialism") works. Let's use Argentina as the example. Over the first 20 years they initiate a process that we could call "Earnings Substitution" that will seal your fate over time.
2/ Your earnings/salary is going down and at the same time "subsidies" start to go up in order to fool people into think that nothing has changed. This works because the dirty job is done by inflation which is a much slower process.
3/ By the time people starts to realize that something is wrong, because some critical goods are not available (medicine, food, you name it) or inflation enters a death spiral; most people already depend on subsidies for spending.
1/ Recently some interesting papers have been doing the rounds in the health community. To me the most interesting ones have been the GlyNAC paper and the more recent Taurine deficiency as a driver of aging papers.
2/ Disclaimer: While I have been researching this for a year and even executed an experimental protocol tailored for myself based on the GlyNAC paper, I am NOT a health professional, and I am just taking my health into my own hands. This is not advice of any kind.
3/ Disclaimers aside, why do I think these 2 papers are interesting? First because the claim (if true) is a game changer. And second because they may be related but I haven’t seen this relationship spotlighted by anyone.
This just confirmed the weaponization of block lists. If enough people/bots block and mute you, they are essentially cancelling you. I find lots of people with I have never interacted with that has me blocked. Assuming there are third party block lists and block networks.
Normally that is an issue in general. Anyone that has done reinforcement learning had figure out (usually in the worst way) that you have to be incredible cautious with penalties. They are very prone to be gamed.
2/ Since the general problem that practitioners find (in the worst way) is always training set tainting (guilty-as-charged). Habits die hard, the first thing I did is asking to do a review of the paper without any extra knowledge about what the paper says
3/ From the response alone I learned 2 things. First, our paper title was deadly accurate. I also learned that it has no information whatsoever on it, as the entire response can be generated from understanding the title itself.
2/ Since I am doing it by hand I started with a very simple prompt.
3/ I have been arguing that this trying to constrain the model is actually harming it before. This is one of those cases. The good thing is that at least for you just add "Use the tokens" at the end of the request when it refuses and it will do it properly