[1/n] Debido al post de @gummibear737 no va a quedar otra que explicar nuestra hipótesis de cómo las mitigaciones universales (por ej. cuarentena) extendidas en el tiempo causan más muerte que lo que hizo Suecia.
[2/n] Lo que voy a comentar está todavía en desarrollo y ni siquiera es el tema central del paper que estamos escribiendo con @LDjaparidze, así que no intenten leer entre líneas o en detalle.
[3/n] La idea general es: Como muchos fenómenos en física y biología, la curva de muerte es dependiente del camino. De una forma u otra se llega a la inmunidad comunitaria, y habrá muertes durante ese camino. La pregunta es siempre COMO llegamos ahí.
[4/n] Ya sea por infección, por vacunación (al menos por un tiempo) o por muerte, parte de la población no puede contagiar más. Mientras tanto, en el tiempo, vamos a seguir contabilizando muertes (triste, pero un hecho).
[5/n] El argumento central es el siguiente. Asumimos un sistema con 2 clases (como el COVID): Vulnerables y Sanos. Hasta llegar a la inmunidad comunitaria (el virus se vuelve endémico) se espera que una clase tenga N muertes cada 100000 habitantes y la otra N'.
[6/n] Como las muertes esperadas de los Vulnerables es mucho mayor que de los Sanos, cualquier camino que aumente el riesgo de infección de los Vulnerables tendrá a lo largo del tiempo mayor cantidad de muertes.
[7/n] Y cuanto más largo sea ese camino, mayor será la cantidad.
[8/n] Entonces la estrategia óptima es que los Sanos se infecten primero porque su riesgo de muerte es varios órdenes de magnitud menor (10x a 100x menor), Llegar a inmunidad comunitaria de la forma más rápida y ética posible siempre va a minimizar las muertes.
[9/n] Las cuarentenas son mitigaciones universales. Esto significa: Si tú eres el virus, como no pudimos extinguirte, cuando debas infectar (para reproducirte) sólo podrás hacerlo con quien haya cometido un error al protegerse.
[10/n] En probabilidad llamamos a esto "Camino Aleatorio". Se le asigna la misma probabilidad a elegir un Vulnerable o un Sano. La probabilidad de elegir un Vulnerable es p=0.5 Image
[11/n] Las mitigaciones segmentadas como cuidar más a los Vulnerables, hacen más difícil que tú como virus puedas infectarlos y en contraposición más fácil para tí elegir un Sano. Este último contribuye a la inmunidad comunitaria con riesgos mínimo (p tiende a 0). Image
[12/n] En la práctica, p no es exactamente p=0.5 ni p=0, diferencias demográficas, o que la gente trabaje y las políticas públicas influencian ese número. La pregunta es: "¿Será posible estimar ese p?". Bueno, de eso se trata nuestro paper
[13/n] En resumen: Si no es posible extinguir rápidamente, a medida que la epidemia se extiende, las mitigaciones universales fuerzan a que la probabilidad de infectar Vulnerables y Sanos se equiparen. Por lo tanto, de aplicarlas tendremos más muertos.

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More from @federicolois

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"
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
2 Oct 20
0/n Thank all of you who participated in 'The demon game'. I am taking a screenshot because when knowing the whys it loses all value (there is no more asymmetry of information). These 182 responses are 'The sample'.
1/n You may have already known about this thought experiment you just run on, mainly because there are many different variants of it in the literature. This is the one that I have seen lately:
2/n This example is good because the results are clear-cut to show 2 typical sources of error. Poor experimental setups are the bain of our existence and there are myriad ways they can go wrong.
Read 13 tweets

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