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Interesting preliminary COVID data from Australia. Small numbers, but potentially suggestive of limited transmission to and from children... ncirs.org.au/covid-19-in-sc…
One notable caveat is it looks like the primary cases were identified based on symptoms, so if there were chains of asymptomatic transmission happening, they may not have been picked up.
And some additional important caveats noted here:
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Adam Kucharski
@AdamJKucharski
If the reproduction number isn't so useful when case numbers are very low, what other metrics can we look at? 1/
(By 'very low' case numbers, I'm talking about a handful of reported cases per day. When we adjust for under-reporting, many European countries probably still have thousands of cases per day, so unfortunately R is still very relevant.) 2/
One useful metric at low numbers is the proportion of cases that can be linked to known outbreak clusters. It's clearly more concerning to have 20 scattered local cases that don't have clear sources than a cluster of 20 linked cases that can all be traced to an imported case. 3/
Read 6 tweets
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Adam Kucharski
@AdamJKucharski
Is the reproduction number currently 0.7, or 0.85, or 0.641? Was it bigger yesterday than today? A thread on real-time estimation and false precision... 1/
Most real-time estimation of R involves looking at the ratio of change from one generation of infection to the next (which takes about 5 days on average: eurosurveillance.org/content/10.280…). In reality, however, we rarely see the moment of infection, we just see cases appearing later... 2/
So the first task is to work out data to use. Should we estimate R from cases, or hospitalisations, or deaths? The answer, of course, is we should look at all these data sources, noting that each has different delays we need to account for 3/
Read 10 tweets
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Adam Kucharski
@AdamJKucharski
Leaving aside the obvious issue that an old abridged dictionary probably isn't the best place to look up mathematical terminology, what does 'stochastic' actually mean - and how does it differ from 'random'? 1/
The two words are often used interchangeably, but there is a subtle difference. Think about a coin toss. If you flip a coin, the outcome is a *random* event - you have no idea if it will land heads or tails... 2/
But suppose you flip a coin a few times and record the outcomes - you'll start to notice predictable features in the data. The coin flips are a *stochastic process*, and we can analyse them statistically (e.g. total number of heads/tails will follow binomial distribution) 3/
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