Suppose we have a SARS-CoV-2 variant that is inherently more transmissible, and another that is more likely to reinfect people who've previously developed immunity. Which will spread more easily? A thread... 1/
We know we can measure transmission using R, but it helps to break R down into four components - duration, opportunities, transmission probability and susceptibility - or 'DOTS' for short. As below describes, R = D x O x T x S. 2/
For example, if have a variant (call it V1) that is inherently better at transmitting during social interactions, it would mean an increase in 'T'. If it was 50% more likely to transmit per contact, we'd replace 'T' with '1.5 x T'... 3/
What about a variant (call it V2) that eludes previous immunity to some extent? Here it helps to rejig the equation so rather than proportion susceptible (S) we define in terms of proportion immune P (which is approximately equal to 1-S). So we have R = D x O x T x (1-P). 4/
If new variant means existing immunity isn't as effective at reducing infectiousness, it will reduce value of P, and hence increase R. Suppose our new variant means previous immunity now only reduces infectiousness by 30% – i.e. we replace 'P' with '0.3 x P'. 5/
So in summary, we have variant V1 with R = D x O x (1.5 x T) x (1-P) and variant V2 with R = D x O x T x (1 - 0.3 x P). We can work out relative advantage of one vs other by looking at ratio of R, i.e. [D x O x (1.5 x T) x (1-P)]/[D x O x T x (1 - 0.3 x P)]. 6/
"Hold on", you say, "that's far too much maths and I'm going to stop reading now." But don't stop just yet, because a lot of the stuff in that equation cancels out, which leaves us with ratio of 1.5 x (1-P)/(1 - 0.3 x P). This is what our simple example looks like graphically: 7/
In summary, we can break R down into its components to understand how different variants might have an advantage. When population immunity low, higher inherent transmissibility more important, but as immunity grows, so does advantage of variants that can evade this immunity. 8/8
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Good piece on the value of digital contact tracing in future pandemics by @marcelsalathe – combined with better linkage to venues of transmission (e.g. superspreading events), potential for a lot of impact here. 1/nature.com/articles/d4158…
During COVID, countries were competing with an exponential process, which meant any individual targeted intervention (like testing, isolation and contact tracing) had to be able to scale easily. Some places understood this more than others... 2/
There seemed to be a lot of media hostility to the idea of contact tracing apps at the time (e.g. below from Sep 2020), perhaps fueled mistrust of social media companies, Cambridge Analytica etc... 3/
It's remakable some people are still claiming COVID had a 'susceptible-infected-recovered-susceptible' dynamic early on, i.e. claiming most in UK got it in 1st wave and 2nd wave was driven by reinfections. Let's look at the heroic assumptions that this claim requires... 1/
1. Assumes first waves declined not because of reduction in contacts, but because of lots of infections and resulting strong immunising responses - and yet these widespread strong immune responses somehow weren't detectable on any antibody test. 2/
2. Assumes the similarity between transmission patterns estimated from social contact patterns in mid-2020 (like CoMix in UK) and transmission estimated from community infection data (e.g. REACT/ONS) is just a massive coincidence. 3/
I recently gave a talk at @JuniperConsort1 outlining some of the work we've been doing in @Epiverse_TRACE with @DataDotOrg and a range of collaborators to try and improve software tools for epidemic response - and how others can contribute to these collective efforts... 1/
As a motivation, I asked the question 'What could the final size of an epidemic be?' - as a first pass, there's a relatively simple method we could use based on an SIR model, but even implementing this can be complicated... 2/
As well as solving the above equation numerically, there are several steps we need to get to this point, from wrangling and cleaning data to estimate R0, to incorporating social contact data. 3/
Why it makes no sense to use total overall COVID deaths as the comparison metric when evaluating the impact of COVID measures, and why we need to focus on transmission dynamics instead. A thread… 1/
Suppose we have two countries, A and B. Country A adopts a lighter touch strategy X early on that gets the reproduction number down to 1 (i.e. epidemic remains flat). Country B leaves it later, then adopts a more stringent strategy Y to bring epidemic down (i.e. R below 1)… 2/
If we did a simple naive comparison of total deaths vs measures introduced, we’d conclude that strategy X (the lighter touch one) is linked with fewer deaths than the more stringent one…. 3/
In the past year, @LSHTM_CEPR has (co-)hosted events on a range of epidemic topics, from public trust and global treaties to analytics software and response strategies.
In case you missed them, here are few to catch up on…
Vernon Lee on Experience, evidence and some intuition in responding to COVID-19 in Singapore: lshtm.ac.uk/newsevents/eve…
Our inaugural research showcase, including Rosanna Peeling on diagnostics, Heidi Larson on vaccine confidence and Thom Banks on public health response: lshtm.ac.uk/newsevents/eve…
There's something a eerily familiar about todays' 'new' IEA report on lockdowns, right down to the text, tables, and half-baked methods. And, of course, the massive estimated effect of masks that somehow hasn't made it into the headlines... 1/
Lots has been written already about this issues with this analysis (e.g. above thread and factcheck.org/2022/03/sciche…), from a lack of accounting for epidemic dynamics to performing a 'meta-analysis' on datasets that aren't independent... 2/
It's a shame, because understanding impact of different NPIs is important - albeit difficult - question. Some studies have made sensible effort at untangling, finding that limiting gatherings and settings of gatherings probably had biggest impact (e.g. nature.com/articles/s4146…) 3/