1/ When we wrote this paragraph back in October I wouldn't know how close to home it would hit. Back in October my mother-in-law (65+) fainted in the bathroom while having diarrhea. It was quite a scare as she got hit very hard.

Ref: medrxiv.org/content/10.110…
2/ Not long after my father-in-law (80+) was diagnosed with anorexia nervousa. Doctors presume that loneliness caused by the lockdown, plus the scare of the episode would have been more than enough to cause it.
3/ Back then I immediately told my wife that those were signs of COVID, being health personnel and working on the lab at the hospital knew very well the evidence. On her account, the evidence was definitely not anywhere near what they thought were signs of the disease.
4/ My rationale for suggesting it was statistics and our plain modeling experience on how outbreaks behave (see our paper with @LDjaparidze). On their location, they had a rampant still undiscovered outbreak (because of cases you know).
5/ Fast forward to mid-February she was thinking to get the Russian vaccine. I told my wife, that I would bet her they don't need it as they already got it. She said no. So we told them that I would pay for the spike quantitative AB study. And my mother-in-law did it.
6/ Who do you think won the bet?
7/ Obviously me, though that is no measure because I write also when I am wrong. I had been back in March with suggesting masks were useful, it's on the record. What did I win? Hanging this beauty exactly where I wanted to.
8/ Though winning the bet was great, the whole point is to show that everywhere health officials, epidemiologist and the general public is underestimating the capability of the SARS-Cov-2 virus to disguise into other maladies and make TTI the wrong approach to deal with it.
9/ Needless to say, those 2 cases + the whole household (which are likely old positives too) are not counted on the government statistics making decision-making without the proper epidemiological understanding a nightmare.

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More from @federicolois

23 Feb
1/ There is absolutely no discussion that if you have a disease that doubles its size every 3-4 days and 2% of those infected requires hospitalization you are going to have a quite difficult situation to deal with. Not even a newbie modeler would disagree with @neil_ferguson
2/ But as @gidmk told me once in one of our initial exchanges (and respectful disagreements): "Everyone comes into epidemiology for the uncertainty and stay for the nuance" (if I recall it correctly, corrections please). The entire response rest on those nuances, let's dissect.
3/ At least in my mind there is no doubt that the Ro of SARS-Cov-2 is very high (we estimated 3.3) and if we account for the UK variant we are probably 30% up from there. So, we can say we agree probably up to the decimals level.
Read 14 tweets
20 Feb
Let's play a game. I will ask a few things about SARS-Cov-2 and you respond in a poll. Let's see how many points do you get.

#1: Is there a non-human reservoir or vectors (or both)?
#2: Is the disease geographically restricted?
#3: Is there a 'sterilizing' vaccine or any other transmission-disrupting alternative?
Read 14 tweets
17 Feb
1/ By popular demand I was going to do a deep dive into the European CDC Face Mask recommendation study. Well, it may end but be a bit shallow. There is not much depth to be diving into. ecdc.europa.eu/en/publication…
2/ The study follows a usual form with clear inclusion and exclusion criteria (which is good). It uses the GRADE framework to ascertain the evidence and generate a recommendation. That is among the best we got in the evidence based land.
3/ The number of studies included is 'interesting'. With a n=118 we would expect to get a nice body of clear cut evidence to support the recommendation. Image
Read 17 tweets
11 Feb
1/ What does this mean for research? For example, while my twitter followed has increased absurdly since early last year because of my work on data analysis on SARS-Cov-2, I was mostly known for my work in performance analysis.
2/ What performance analysis teaches you is that you run experiments of the type 'If P then Q' every single day, several times a day.
For ex. "If I change this data structure then I will be able to obtain better performance by accessing memory contiguously"
3/ Now, when you enter into the realm of 'how the physical CPU is working' then becomes far more difficult. The reason behind that is that with that many moving parts noise is very difficult to separate from the signal.
Read 8 tweets
7 Feb
1/ Let's look at this paper: "Influenza Virus Aerosols in the Air and Their Infectiousness" from 2014 (we cannot claim this was unknown). We know now that we have a new kid on the block now, ready to challenge Influenza for supremacy in the transmissibility metric.
2/ So there were these guys that actually infected people with Influenza to measure how infectious it was. That is a 'challenge study', this is no 'model' this is actual humans. And they found, that with as much as 3000 copies you get it.
3/ Another study actually measured how many particles an adult would inhale in 1 hour given the concentrations found on a health center, a day-care center, and airplanes.
Read 19 tweets
6 Feb
1/ I was sent this paper. You know I have disregard it before because the filtering mechanic was really not significative for the type of airflow conditions imposed by masks. How wrong I was on not looking deeper.
2/ I have been told by @Kevin_McKernan that you always have to look for "Where is Waldo?" in this type of studies. The first interesting fact comes from Table 1. Each experiment has different experimental setups, that is good enough to disqualify in my book.
3/ But then I skipped to Table 4. Mind you, almost none were statistically significative. But remember Table 1. So you see a correlation there?
Read 6 tweets

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