Yes they should. Dexamethasone, Bromhexine, Melatonine and a few others like Colchine had in-silico interesting results as early as 1st of March. With the exception of dexa still waiting for the large scale RCTs.
Haven't said this publicly before but early on with a group of MDs, biotech and others researchers we built a protocol coupling statistical exploration techniques from AI with low toxicity drugs for profilaxis use. Apparently it was too innovative and rejected by our government.
With the exception of dexamethasone, all the others were already identified to be used in the early treatment of at risk with high probability contract protocol. Probably ivermectin that appeared later would have made the cut too, given the profile.
It is still alien to me why in cases like a pandemic the medical professionals don't embrace nonparametric statistics in order to perform fast and efficient discovery of treatment. Instead they go with the standard parametric n-arm trials which are very inneficient.
Instead a RCT using multi-armed bandit can hone in into a treatment pretty fast (if any one -or several- of them is good) using exploration techniques widely used in AI. Funny thing, the mathematics required are more than 40 years old.
But to add insult to injury, every day that passes many of our hypothesis like for instance that there may have been some casuality to the correlation of mortality and Melatonine behavior in age and sex could have been investigated early on. scitechdaily.com/melatonin-prod…
Should have read "causality". Sorry to be blunt, but this is on the medical professionals (not all of them but many didn't know best either). It's not that the tools didn't exist, it's that they don't understand them. And lets not start with how epidemiology is doing.
How many more confirmations they need to see that ours is probably the most accurate and most importantly highly predictive model out there? The waves, the 'with' the virus, asymptomatic, presymptomatic and short infectious period. What else do they need to take us seriously?
Rant over. Thanks but I need to say it. This is beyond infuriating.

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More from @federicolois

31 Jan
1/ Very good thread about all the things that most modelers fail at, which is predicting outcome. As shown your assumptions are everything. For example, our model only estimate immunity through simplified cohort dynamics. Its good for that, it just happen it is also very useful.
2/ Language is a model that is useful to communicate ideas. In the same way as language, models shape reality. If your model simplifies an specific aspect, your reality also becomes less rich. You always have to be careful what model you accept as correct and why.
3/ but the most important realization you have to develop is that models are useful, until they arent. The more disconnected from reality, the more effects you are explaining without fully understanding them.
Read 10 tweets
18 Jan
1/n Language is powerful, because it gives hints on what is going on. I am in my home town, a 150k inhabitants city that has been isolated by government for a long time. Given my parents live here I have been tracking COVID here from early on.
2/n I even know the city infectious disease public official here and we exchanged notes on the early outbreak when there was just 2 deaths. Our estimation back then was between 120 to 150 deaths by the end of it.
3/n Fast forward to today, if we use the conservative method used by the WHO and CDC for correcting detected and actual infections it gives that 120k were infected. Remember 3rd world testing infrastructure.
Read 6 tweets
20 Dec 20
1/n It is our view with @LDjaparidze that lockdowns cause harm in subtle way. They do stop the virus, mind you, but when it eventually circulates again (and until vaccination it always does) vulnerable willpower to isolate is gone.
2/n Death minimizing is about virus circulation among healthy <60 while vulnerable *are still willing* to isolate at high levels. That is exactly what didn't happen in Argentina after the 5th month of lockdown.
3/n Oblivious to most (even the expert epidemiologists) after lockdowns death minimizing requires overshooting healthy <60 infections while vulnerable isolate at very high levels. None of that is happening.
Read 5 tweets
7 Nov 20
1/ The first rule of Lockdown Club is: You do not talk about deaths per million. The second rule of Lockdown Club is: You do not talk about deaths per million.
2/ Third rule of Lockdown Club: someone yells Sweden or herd immunity, you point out the other Nordics. Fourth rule: only two metrics to a discussion, cases and cases.
3/ Fifth rule: one lockdown per season, fellas. Sixth rule: no deaths, no herd. Seventh rule: lockdowns will go on as long as they have to.
Read 4 tweets
17 Oct 20
Controversial opinion: those that say its not possible to shield the vulnerable, also won't be able to prove if there is a difference (or lack of it) between the trajectory of the virus at Madrid and Stockholm. Who do you think has let it rip?
1/ There were many "Eureka" moments while working on our paper, but probably the most important of all happened pretty early. Non-linear models are highly sensitive to:
2/ We decided early on to eliminate as many parameters as possible. Location parameters are simple to fix, they are location parameters. Viral parameters also, you can go and say R0=3.3 and you made a choice. How many parameters are left if you do that?
Read 32 tweets
13 Oct 20
1/ Our preprint with @LDjaparidze is online at @medrxivpreprint
"SARS-CoV-2 waves in Europe: A 2-stratum SEIRS model solution"
medrxiv.org/content/10.110…
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%.
Read 13 tweets

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