I still see the persistent, but incorrect, claim that control measures just delay - rather than reduce - the impact of an epidemic. A thread on the problem of 'overshoot'... 1/
This claim seems to arise from a misunderstanding about two related, but different metrics: the % of people infected during an epidemic, and the point at which immunity leads to a decline in transmission. 2/
In an uncontrolled epidemic, 'herd immunity' is reached at the peak (because R<1 after this point), which means the final % infected is generally much larger than the herd immunity threshold. (Below from: science.sciencemag.org/content/368/64…) 3/
Population structure (e.g. variation in connectivity/susceptibility) can change these values - below shows difference variation can make in models (medrxiv.org/content/10.110…). But overall message is same – larger % is infected in uncontrolled epidemic than required to get R<1. 4/
As a hypothetical example, suppose we have an epidemic that would have infected 60% of the population without control measures. For this example, both of above plots imply herd immunity would have been reached when 35-40% acquired immunity. 5/
As a hypothetical example, suppose we have an epidemic that would have infected 60% of the population without control measures. For this specific made-up example, both of above models imply herd immunity would have been reached once 35-40% acquired immunity. 5/
In other words, there's overshoot of 20-25% in this example, i.e. people infected after the epidemic peaked and R was below 1. With control measures, initial R would be lower, which means peak occurs when % infected is below immunity threshold, reducing eventual overshoot. 6/
The exact % values aren't the key point here - the point is that *even if an epidemic isn't sustainably controllable in a population and will end in herd immunity, control measures can still massively reduce the disease burden sustained along the way*. 7/
As a tangible example of behaviour change reducing overshoot, take the 2009 flu pandemic in UK, where school summer holidays interrupted transmission. Had this not happened, it's been estimated epidemic would've infected 20% more people than it did: cambridge.org/core/journals/… 8/
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/