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?
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%.
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] Preparing everything to respond to the question: "Under our isolation epidemic model. Is it possible to correct government policy mismanagement starting at the end of July in 90 days for Madrid, Catalunya and a few other cities?" What do you think? Answers in an hour or so.
[2/n] For those that are new to this thread, you can prepare and hone your skills in modeling with the Harmless Virus Game:
The arrival of a vaccine in the context of COVID can be modeled using game theory as a gamble over the expectation of the final death toll. Most countries will have negative payoff after August 2020. Change my mind. cc @LDjaparidze
Let's make it more interesting... Do you agree?
Context is king. For a gamble to exist we need to define clearly the parameters to observe the likely expected result. Let's start with vaccine efficacy (VE). What do you think is the range most manufacturers are looking for?
Say you have a completly harmless virus (IFR=0) that can spread at R0=3.3 and you can find via PCR for 19 days. How many deaths per million would you find if you test all deaths in an average european city? cc @LDjaparidze
So now that I got your attention. Let's narrow it down. Our harmless virus would be found during it's spread frenzy at a rate of
OK. It seems I have a few epidemiologists playing. Here is a curve ball. Would change the results if we "Do nothing" (let it spread unmitigated) or if we mitigate it ('lockdown, masks, etc')? I know it is harmless!! Play along.
[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.
[1/n] Well @gummibear737 pushed me into a position to have to explain our theory of how universal mitigations (for ex. lockdowns) kill over time more people than what Sweden did.
[2/n] Disclaimer ahead, this is early work and not even the central point of our paper with @LDjaparidze. So don't read too much into the details yet.
[3/n] The gist of it is: Like many things in physics and biology, the death toll is path-dependent. One way or another, you always eventually get to community immunity and accrue unavoidable deaths. The question is always HOW you got there.
[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.
[1/17] Let's talk about herd immunity, EVIDENCE only. That 70% usually quoted assumes 100% of people are susceptible to infection based on a very restricted model (SIR) that has served us well over time.
[2/17] That 70% can be composed of several immunity traits. From barrier immunity, humoral immunity (antibodies), or cellular immunity. They can be acquired via infection, vaccination, or cross-infection.
[3/17] We know that estimating when we reached that 70% is hard, because... cross-reactivity between pathogens could diminish the susceptible types, barrier immunity is difficult to quantify, ...
Intriguing method. As a note, it is of particular importance the Non-Exponential Growth section to shed some light on serology anomalies like found in Barcelona's study and no seroconversion on intrafamilial exposure settings. [1/3]
In Barcelona's study 29 RT-PCR positive cases (8.53%) do not seroconvert. This may be signaling that serology studies are underestimating (probably by a lot) the underlying infection rate. [2/3] medrxiv.org/content/10.110…
Or worse, when positive confirmed cases infect secondary subjects and those even though potentially having compatible symptoms, never become positive at RT-PCR and IgG but show specific cellular immunity (confirming the infection). [3/3] medrxiv.org/content/10.110…