Some thoughts on the evolutionary trajectory of SARS-CoV-2 so far, how it compares to other viruses, and what might happen next... 1/
For seasonal coronaviruses & flu, we see a pattern of 'antigenic turnover' over time - circulating viruses give rise to new variants that escape prior immunity against infection, immunity builds against these new variants, then these in turn spawn new variants... 2/
When infections evolve to escape immunity like this, we typically end up with an evolutionary tree that looks like a lopsided ladder as new variants sequentially replace their 'parent' variant lineages (below from: journals.plos.org/ploscompbiol/a…) 3/
After the emergence of the 2009 pandemic influenza A/H1N1p virus, it settled into a similar pattern of antigenic turnover that has continued since (below from: nature.com/articles/s4159…) 5/
But so far we haven't seen this pattern for SARS-CoV-2... Omicron didn't emerge from the Delta lineage and Delta didn't emerge from the Alpha, Beta or Gamma lineages (below from nextstrain.org/ncov/gisaid/gl…) 6/
Given that we have to go far back to find a common ancestor between VOCs and other circulating variants, one hypothesis is that they emerged in individuals with a chronic infection:
Regardless of exact origins, this pattern means we shouldn't assume next variant of interest/concern will emerge from current circulating Omicron viruses - like other variants, it may well have already evolved (or be evolving) somewhere, from a much older ancestor lineage. 8/
I think seasonal coronaviruses and influenza are a sensible 'prior' to bear in mind for what long-term dynamics of antigenic turnover of SARS-CoV-2 could look like. But we also need to remember this process of sequential turnover isn't what's happened so far... 9/
Reduction in global travel may also have influenced variant dynamics globally so far – and subsequent reopening may change this, as there is less potential for 'evolutionary villages' that are relatively cut off from global dynamics. 10/
This uncertainty illustrates the importance of having good surveillance coverage of genomic variation of SARS-CoV-2 globally, optimised to make best use of resources (e.g. medrxiv.org/content/10.110…) as well as ongoing synthesis of available data. 11/11
Final note: above all refers to immunity that reduces transmission, as this is what shapes ability of new variants to spread (and hence successfully replace existing ones).
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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/