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Inspired by @lewis_math's useful analysis for Australia (maths.adelaide.edu.au/lewis.mitchell…) we've put together a dashboard visualising changes in (aggregated) Facebook colocation data over time in UK: cmmid.github.io/colocation_das…
Colocation isn't quite the same as mobility, as it gives a rough measure of proximity, so could provide a useful additional/alternative metric for evaluating local changes in behaviour.
Credit to Hamish Gibbs for all his great work on these visualisations.
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Adam Kucharski
@AdamJKucharski
I'm getting asked more about the 'k' parameter that describes variation in the reproduction number, R (i.e. describes superspreading). But what does this parameter actually mean? A short statistical thread... 1/
R measures average transmission per case, but in reality some cases may generate more infection than others, e.g. because of events/places they visit while infectious. So we need a way to estimate variation in R at the individual-level... 2/
We can do this by fitting a curve to the distribution of secondary infections, and see how much variation there is. A commonly used tool is the negative binomial distribution, which has mean=R and variation captured by a dispersion parameter 'k' nature.com/articles/natur… 3/
Read 8 tweets
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Adam Kucharski
@AdamJKucharski
I found this piece a bit strange (theguardian.com/world/2020/may…) e.g. claiming "our predictions have been accurate to within a day or two, so there is a predictive validity to our models that the conventional ones lack" (arxiv.org/pdf/2004.04463…), when cited paper made this prediction:
Implementing Bayesian state-space models with an observation process (which seems to be the basis of 'dynamic causal modelling') is a common approach in epidemiological modelling, so it also seems odd to claim that it's a new/different method...
And as noted by my colleague, the immunological 'dark matter' seems to be an extra parameter added to make the model fit the data, without a clear biological/epidemiological justification:
Read 4 tweets
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Adam Kucharski
@AdamJKucharski
The story I posted about Hilda Hudson recently () seemed to resonate, so here's another bit of epidemiological history, about some ideas that you've almost certainty seen, but might not know the origins of... 1/
First, some background. There are three really important methods in epidemiology: retrospective cohorts, case-control studies, and confounding... 2/
In a 'retrospective cohort', we look back at a group of people who previously have/haven't had some specific characteristic (e.g. certain age/exposure to infection) and examine what this tells us about risk. Here's a COVID-19 example: thelancet.com/journals/lanin… 3/
Read 8 tweets

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