A riff on data, models and intervention in a COVID world.
When you don't have data, you absolutely need models - it helps you understand what could happen, good and bad scenarios and what you need to measure to understand more thing. With no - or little - data, models are king.
When you have data, the role of models change. In fast moving epidemic even working out what is happening *right now* is complex; your data is coming in different streams, it has biases (which change over time), technical issues (also time dependent) >>
<< and the things you want to measure/know about also change over time - eg, in this case, infections. Here models have three roles.
1. To help do the fusion of the different data streams. This is in some sense "old school" modelling and data fusion; eg Kalman filters from the 1960s is totally in zone here. Here models of reality (how infections become cases become hospitalisations) bind data streams together
You can learn the binding, or enforce the binding; you can have time varying parameters integrated over or not, but your data hopefully constrains everything enough for this to be as much "de-noising" data streams as it is "modelling".
2. Using data and models to do "nowcasts" and short term "forecasts". Forecasts here really do mean "we believe the world will be like this in X days time". Weather forecasts are the best analogy - measurements today to predict 1, 2, 7 days out.
Because of the long chain from infections -> active cases -> hospitalisations -> deaths plus reporting lags one can be pretty accurate given one has enough data in the other areas. The (sad) deaths happening now are about infection events 4-6 weeks ago mainly.
3. The third sort is back to the original scenario models. These can be crude, but useful - no human is good at understanding exponential growth. They can be sophisticated, and thus fragile to assumptions (eg, that control measures will or wont change).
Scenario models are not forecasts. They are there to understand the options we have on the table and critical parameters for this.
We have moved into a COVID world with lots more data (hats off to the ONS survey and the REACT survey in the UK; genuinely a good move here. I don't think a similar thing is present in FR or ES). We have far deeper testing across most European countries (all credit).
Wastewater testing has proved its worth in a variety of settings in Europe and US; we should aim for *every* urban area to have regular COVID testing on their wastewater. Finally the use of hospitalisation data as a key tracking parameter is widespread of course.
As such much of the decision space is constrained by this data. We know largely where we are *now* (or at least, for very sure, last week) in terms of infection levels and can forecast well the next bit.
...BUT... the presence of data, with good, appropriate modelling in any of the 3 ways above does not imply action! To change infection and hospitalisation we don't just have to know about it, estimate it, and plan - we need to *intervene*.
Interventions are many - the simplest, and the most important is isolation (aka "hyper-localised lockdown of individuals"). This is the most important thing to get right as our ability to see the infection more clearly comes into focus.
Other things include the routine but important ... washing hands, wearing masks.
But everything - including Trace - loops back to the key intervention of isolation when we know people (a) have the virus or (b) are at high risk of having the virus.
Vaccines - assumming they work as well as stated for Pfizer and more of the same from the other vaccines would be great - will really really help - not least because we can target the at risk groups.

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More from @ewanbirney

13 Nov
Again, a briefing for journalists, this time on vaccine trials, types of vaccine, efficacy and safety.
(Context: I am a two-steps expert away from vaccine development; I am a one-step away from clinical trials; I am an expert on genetics + computational data science. I am, finally, on one of these clinical trials as a participant)
Standard context: SARS_CoV_2 is an infectious virus that causes a nasty disease, often leading to death, in a subset of humans. It will continue to be a massive issue to manage until we either have good enough vaccines or good enough treatments for the disease.
Read 37 tweets
12 Nov
It's great to see this paper on scaled up Cactus graphs (Progressive Cactus) - on 600 vertebrate genomes - from the great team lead by @BenedictPaten nature.com/articles/s4158…
This paper is a very much a methods paper, but I hope Benedict and colleagues will also dive into the data - I don't think we have use the realised ancestor reconstructions (reminds me of the older Enredo days - also with Benedict!). There is a treasure trove in there
Just repeat evolution I think is fascinating here, but also niche-loss pseudogenes for example.
Read 6 tweets
1 Nov
There are debates - important debates - to have in these COVID times, but there are some either stupid debates or misguided in my view debates. Here's my list with brief rejoinders.
1. False positives on tests are grossly inflating the number of cases. Straightforwardly they are not; the system understands false positives, goes to a lot of length to prevent them, and, acknowledging that they can never be 0, carefully models them in analysis.
2. "Hard" Stratify and Shield (or segmentation) is a solution. By "Hard" I mean placing all the at risk people in entirely COVID "safe" environments (extremely low risk of infection) and then having the remaining people at low risk live normal lives, and get the infection.
Read 24 tweets
1 Nov
It is somewhat hard to know which strand on England's November lockdown to pick apart - a large number of people in the press (and twitter) are commenting ("hot takes" in the US parlance) - most heat and not so much light.
Yesterday, before the announcement, I tweeted on this here:
Read 17 tweets
1 Nov
Can we have a ban of the use the definite article “the” with science ? Unpack to at least one level to be clear “we need to heed the dire warnings of the coronavirus infection modellers” or “we cannot ignore the impact of change in weather patterns”
“The science” implies certainty when science rarely has it (but parts of science can have high confidence - lack of complete certainty does *not* mean no very confident view) and it often socially placed science as an actor in other societal debates
Ie, rather than societal debates accepting shared facts +understanding, poking and asking questions of the science, some people think of science being allied with their point of view, often spilling over from the “understanding the world” bit to the “and so what do we do” bit
Read 6 tweets
31 Oct
An explainer thread (often I feel I am pitching these to journalists as much as anyone else) on COVID this month.
Context: I am an expert in one area (human genetics) with battlescars in complex data flow+analysis; I know experts in most other areas and aim to be curious about their viewpoints; I have a clear conflict of interest in that I am consultant to ONT, which makes a new COVID test
Again, worth reminding people of the overall situation; SARS_CoV_2 is an infectious virus which causes a nasty, often lethal, disease in a subset of people. It is now across the world.
Read 29 tweets

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