Why a SARS-CoV-2 variant that's 50% more transmissible would in general be a much bigger problem than a variant that's 50% more deadly. A short thread... 1/
As an example, suppose current R=1.1, infection fatality risk is 0.8%, generation time is 6 days, and 10k people infected (plausible for many European cities recently). So we'd expect 10000 x 1.1^5 x 0.8% = 129 eventual new fatalities after a month of spread... 2/
What happens if fatality risk increases by 50%? By above, we'd expect 10000 x 1.1^5 x (0.8% x 1.5) = 193 new fatalities. 3/
Now suppose transmissibility increases by 50%. By above, we'd expect 10000 x (1.1 x 1.5)^5 x 0.8% = 978 eventual new fatalities after a month of spread. 4/
The above is just an illustrative example, but the key message: an increase in something that grows exponentially (i.e. transmission) can have far more effect than the same proportional increase in something that just scales an outcome (i.e. severity). 5/5
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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/