[1/n] While radical my current working hypothesis is that HIT is probably higher than what Oxford models suggest, even though we arrive at the same seroprevalence for cases curve concavity change. cc @MLevitt_NP2013
[2/n] I postulate that the observation of 15%-25% range for curve concavity is actually a measurement artifact that is not reflecting the actual seroprevalence at the population level.
[3/n] Rationale: Antibody level studies have 2 sources of error, one is calibration over clinical cases which affects the actual on the field sensitivity. Even the best assays will underestimate prevalence.
[4/n] But, the most relevant source of error is the dynamics of the immune response. Nature paper suggests that antibody levels fade quickly. nature.com/articles/s4159…
[5/n] The introduction of the time variable after the initial fast growth stage and the delay caused by interventions like using masks and hygiene would push the equation toward a steady-state
[6/n] where new population enters into the potentially measurable positive universe, while at the same time other fraction of the population gets out becoming seronegative.
[7/n] So you may ask, why we have seen then Argentinian slums with 55% seroprevalence medrxiv.org/content/10.110…, or Brooklyn at 45% medrxiv.org/content/10.110… .
[8/n] The reason is that in those places, it happened fast (unchecked), and we measured at the proper time before antibody fading would have a measurable impact.
[9/n] I also postulate that after you arrive at the 'real' concavity change on a geographical location (some countries were too successful in containing or delaying) what we are watching is a phase shift into seasonal behavior.
[10/n] Again, this is a working hypothesis that still has a few data holes (because it is too early) but so far seroprevalence data points are in agreement. The canary in the coal mine for me is the Geneva longitudinal study.
[11/n] Another source of strengthening of this hypothesis would be to rerun that Argentinian slums and Brooklyn where the prediction is that you will go way below 20% in between 4-6 months.

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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"
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
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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