Right. Deep Breath. RT-PCR "false positives" and Ct numbers (again). tl;dr it is complex, but the RT-PCR testing systems deployed across the world are sound and the people who run them report positives are positives and little can be improved obviously.
Context: I am a genomics/genetics + computational biology expert. I know a large number of infectious disease testing experts. I have a COI in that I am a long established consultant for a company (ONT) that makes a new test here; this gives me additional insight
There a number of classes of false positives which don't concern the current debate (eg, sample swaps, lab contamination). To repeat an early point all the people I know in this are paranoid about this, test and check in a rather detailed way and these are looooow.
The current debate, picked up by the "he built a team that landed rockets on floating ships" Elon Musk is not really "false positive" it is "scope of test".
RT-PCR (and other tests, like LamPORE) test for the presence of SARS_CoV_2 viral RNA in the swab. This is not quite what you want as at the end of infection one can be RNA positive but no infectious viruses.
If one could magic up a quick, sensitive and specific, send-your-swab-in test for infection viruses present that would be great. Sadly noone has done this (yet) (I will come back to lateral flow tests).
RT-PCR (+ other nucleic acid tests) there are not quite the ideal scope, but they do have really good sensitivity and specificity - in the 99% for good samples.
A note now on sensitivity. It is going to be tempting in a moment to use an internal metric on the test (Ct numbers) to change the scope of the test. As well as the inherent complexity of this the big elephant in the room is swab/sample/process variance.
People don't swab well. Swabs are drier or wetter or contaminated with RNases (which are everywhere). Sample shipping can be disruptive and late. Lab processes vary from the profound (missing a sample shipment) to subtle (the thickness of the foil on the reagent cover varies)
All these things means one get substantial additional variance in the real world from more controlled settings. Welcome to ... real life practical infection biology!
Given this variance, and the danger of false negatives in majority of settings, one has far less scope to try to draw boundaries in the Ct space.
The next, massive elephant in the room (it's a crowded room of elephants) is that there are different RT-PCR machines (this is good - diversity of supply) with different RT-PCR locations on the genome looked at, and all these things systematically vary on (for example) Ct values
This is why individual workflows (these primers on these machines with this swab) are regulated and approved and although if a scheme works on RT-PCR on one machine it is likely to work elsewhere... it is not like there is one "Ct" scale.
ok, a reminder on Ct number. Ct number means cycle threshold and it is the number of cycles needed until there is a large increase in flourescence/optical properties on a RT-PCR machine. With the same machine and primers and experiment, Ct numbers are meaningful >>
A low Ct number means more starting RNA. A high Ct number means less starting RNA. One can semi-quantify this to absolute standards with spike-ins if desired.
The suggestion is that one could change the "scope" of the RT-PCR test by shifting the threshold of this Ct number (even cleverer perhaps do this with different Ct space different parts of the viral genome which amplify due to the viral infection cycle: an n-dimensional plane)
It does, as a pencil-and-paper exercise, look sort of attractive. But - the two elephants in the room - real world variation in swabbed samples (let me repeat - there is RNase in all sorts of places) and variation between machines means this is ... not going to work easily.
Furthermore although more RNA usually means more intact virus it is clear that there is biological variation infection (location, size etc) so "amount RNA" does not have a tight relationship to "will this person infect another person".
(I get Elon Musk built a team that landed rockets on floating ships, so "not going to work" is a red rag here. Let's put this another way - it is not going to be a simple thing to work; getting a robust -70 cold chain world wide would be a better use of engineering innovation).
What about Lateral Flow tests? These are interesting because they test for the folded viral protein (antigen) not the nucleic acid. Again - same gotchas apply - swab variation, weird stuff happening and does viral protein equate to infectious virus?
As it happens, the limit of these tests seem to capture low Ct (high RNA levels) individuals, less so high Ct. It is super super hard to do the experiment you want to (will this person transmit to other humans) but at least in vitro the trend is this way.
For sure, if one could make a *more* sensitive Lateral Flow test and/or say portable nucleic acid tests (my COI comes into play here on LamPORE) you would again use all that sensitivity on getting people who are possible infectious identified.
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Small moan about models and parameters in COVID. R - the number of infections each person makes on average is a parameter but it is also something that one can measure. Each infected person has their own "R" - it is a count - and one could in theory measure all these little Rs
This R is both something one can measure and the average R, or the distribution of R is often components of model. It is a "real measurable thing" and it is "part of our COVID models".
You might want to model other things; one is the distribution of how R varies between people/events. A sensible choice is a negative binomial. Often people use k as a parameter (in fact, in other uses, they often use the letter r as a parameter, but this would be confusing!)
Again, a briefing for journalists, this time on vaccine trials, types of vaccine, efficacy and safety.
(Context: I am a two-steps expert away from vaccine development; I am a one-step away from clinical trials; I am an expert on genetics + computational data science. I am, finally, on one of these clinical trials as a participant)
Standard context: SARS_CoV_2 is an infectious virus that causes a nasty disease, often leading to death, in a subset of humans. It will continue to be a massive issue to manage until we either have good enough vaccines or good enough treatments for the disease.
It's great to see this paper on scaled up Cactus graphs (Progressive Cactus) - on 600 vertebrate genomes - from the great team lead by @BenedictPatennature.com/articles/s4158…
This paper is a very much a methods paper, but I hope Benedict and colleagues will also dive into the data - I don't think we have use the realised ancestor reconstructions (reminds me of the older Enredo days - also with Benedict!). There is a treasure trove in there
Just repeat evolution I think is fascinating here, but also niche-loss pseudogenes for example.
A riff on data, models and intervention in a COVID world.
When you don't have data, you absolutely need models - it helps you understand what could happen, good and bad scenarios and what you need to measure to understand more thing. With no - or little - data, models are king.
When you have data, the role of models change. In fast moving epidemic even working out what is happening *right now* is complex; your data is coming in different streams, it has biases (which change over time), technical issues (also time dependent) >>
There are debates - important debates - to have in these COVID times, but there are some either stupid debates or misguided in my view debates. Here's my list with brief rejoinders.
1. False positives on tests are grossly inflating the number of cases. Straightforwardly they are not; the system understands false positives, goes to a lot of length to prevent them, and, acknowledging that they can never be 0, carefully models them in analysis.
2. "Hard" Stratify and Shield (or segmentation) is a solution. By "Hard" I mean placing all the at risk people in entirely COVID "safe" environments (extremely low risk of infection) and then having the remaining people at low risk live normal lives, and get the infection.
It is somewhat hard to know which strand on England's November lockdown to pick apart - a large number of people in the press (and twitter) are commenting ("hot takes" in the US parlance) - most heat and not so much light.
Yesterday, before the announcement, I tweeted on this here: