PhD student @ MRC Biostatistics Unit, University of Cambridge
1253 karmaJoined Sep 2019Pursuing a doctoral degree (e.g. PhD)Seeking workCambridge, UK



I am an (almost finished) PhD student in biostatistics and infectious disease modelling (population-level); my research focuses on Bayesian statistical methods to produce improved estimates of the number of new COVID-19 infections. During the pandemic, I was a member of SPI-M-O (the UK government committee providing expert scientific advice based on infectious disease modelling and epidemiology).

I enjoy applying my knowledge broadly, including to models of future pandemics, big picture thinking on pandemic preparedness, and forecasting.

How others can help me

I'm currently nearing PhD competition with nothing lined up for after. I'm interested in opportunities in biosecurity and global health, especially answering questions about cost-effectiveness and prioritisation using modelling / stats / epidemiology skills. Please DM if of even vague interest.

How I can help others

Happy to chat about my experience providing scientific advice to government, the biosecurity field, epidemic modelling, doing a PhD, or pretty much anything else!


You need a step beyond this though. Not just that we are coming up with harder moral problems, but that solving those problems is important to future moral progress.

Perhaps a structure as simple as the one that has worked historically will prove just as useful in the future, or, as you point out has happened in the past, wider societal changes (not progress in moral philosophy at an academic discipline) is the major driver. In either case, all this complex moral philosophy is not the important factor for practical moral progress across society.

Bear in mind that even if FTX can pay everyone back now, that does not mean they were solvent at the point they were put into bankruptcy.

In your argument for 3, I think I accept the part that moral philosophising hasn't happened much historically. However, I can't really find the argument that it probably will in the future. Could you perhaps spell it out a bit more explicitly, or highlight where you think the case is being made please?

Great and interesting post though, I love seeing people rigourously exploring EA ideas and fitting them into the wider academic literature.

Thank you Ricardo, this is an insightful analysis. I'd like to see more EA Forum posts with this level of investigation invested into them. In particular, the balance of more longtermist and less global health funding is in contrast with other analyses on the forum.

I think your write-up could be improved more than the underlying analysis. To make this more accessible to others, and your work higher impact, I'd recommend the following.

  • Include your most important takeaways, and less information on your methods (eg the link to the notebook) in the tl;dr. Very few of your readers will have the time to dig into your code, and those who do are also likely to read the whole post. Almost all your readers want to know about your conclusions though.
  • Relatedly, your conclusions are quite meta (eg the stats page could be clearer), but don't highlight your important findings (eg: 2023 had a lot less money distributed, GHD is declining and LTFF is increasing).

This seems weird. We don't write 0156 for the year 156. I think this is likely to cause confusion.

This would surprise me. Surveillance is a very expensive ongoing cost, and the actions you should take upon detecting a new microbe which could potentially be a pathogen are unclear. Have you got a more detailed version of why you think this?

Do you know of anything else that feels similar to this? People in public areas collecting biological samples from volunteers (perhaps lightly compensated).

Afraid not. The closest I can think of is collecting samples from healthy volunteers without any benefit to them, but not in public areas. In particular, I'm thinking of swabbing in primary health settings (eg RGCP/UKHSA run something like this in England, I can't remember if it only includes those with respiratory symptoms) and testing blood donations (normally serological testing looking for antibodies). REACT (run by Imperial College) did swabbing for COVID via postal recruitment.

A bit of an aside, so maybe not of interest, however, this made me think of serological testing of residual blood samples. That is, when blood is collected for testing (for any clinical reason), not all of it is used in the tests, and the remaining (residual) part is tested. Here, there are no sample collection costs (the blood was collected anyway). However, it doesn't map exactly because you don't swab people without respiratory suspicion but you might take blood (eg anemia). Maybe there is an opportunity for either testing blood samples for pathogens (but I have no idea what that looks like) or samples taken for other respiratory reasons (but then you need to think about co-infection, ie does infection with influenza make you less likely to have another respiratory infection).

Finally, some shameless self-promotion. I'm currently nearing PhD competition with nothing lined up. If there are projects looking at these sorts of questions interested in modelling / stats / epidemiology input I'd be very interested, please DM. Please ignore this if unappreciated.

Thank you for that very detailed reply Jeff, I learnt a lot about how to think about costing this.

The easiest way to collect a pooled sample is the walk around some building and sample everyone. This gets you a big sample pretty cheaply, but it's not a great one if you want to understand the containing city because it's likely that many people in the building will get sick on a similar timeframe.

I agree this is true for an office block, but I would think you can do much better without much cost. For example, if you use a high-traffic commuter train station or supermarket I would guess you get a fairly broad cross-section of the city. They'd be somewhat uncorrelated (different home locations with children at different schools, different offices etc.) although obviously the geographical component is still there. Perhaps similar to wastewater though? You could do multiple locations as well though.

It seems like you ought to be able to get down to more like $2/person in which case a pool of 1k costs you $2k in collection. Then add in $1k for sequencing and you're still well above wastewater.

These numbers are maybe optimistic, but not ridiculously so.

The Coronavirus Infection Survey (big UK study which I've worked on) cost ~£1b for ~11.5m swabs (Excel sheets with data from Mar 2023 and historical data). Works out as ~$100 / swab.

Very likey overestimated upper bound though because that is a proper random sample of the whole population, with ~9.5m of the swabs collected by study workers going to houses. I think this budget might exclude the cost of PCR testing (done individually, not pooled) and a lot of time spent running / analysing the data.

am I practicing my handwriting in 1439?

I'm not sure what the question is here, I find your metaphor opaque. I guess this is a reference to the invention of the printing press around then, which in some sense makes handwriting pointless. But, being able to have legible handwriting seems pretty useful up until at least this century, perhaps until widespread smartphones.

Thank you for this write-up, very interesting. I'm excited to see more investigations of different surveillance systems' potential.

Hopefully, the SIREN 2.0 study, running this winter, will generate some more data to answer this question.

A few questions now I've had time to consider this post a bit more. Apologies, if these are very basic, I'm pretty unfamiliar with metagenomics.

First, how do you relate relative abundance to detection probability? I would have thought the total number of reads of the pathogen of interest also matters. That is, if you tested the entire population you would have some reads on every pathogen even if the relative abundance of some pathogens is very low.

Relatedly, does the cost of the sequencing scale roughly linearly with the relative abundance required? That is, if your 40,000x figure is correct, would that imply swabbing is ~40,000x cheaper than wastewater?

Finally, could you please expand on your figure of percentiles vs relative abundance? Why does the number of swabs affect the relative abundance? If you double the number of swabs, I would expect that both the total reads and the number of SARS-COV-2 reads double, hence holding the relative abundance constant. Perhaps it's that the variance in the number of individuals infected and their viral load increases but the mean of all the lines is the same, is that correct?

And now one point of disagreement.

If you could cost-effectively swab a large and diverse group of people, this would allow surveillance with much lower sequencing costs than wastewater. But that's a big "if": swabbing cost goes up in proportion to the number of people, and it's hard to avoid drawing from a correlated subgroup.

I don't think this is that big an "if".

1% incidence per week is extremely high. For context, SARS-CoV-2 at 1% incidence per week implies a prevalence of 2-3%. The UK, which had a middle-of-the-road rich world pandemic, peaked at this prevalence just before implementing a lockdown in the January 2021 (Alpha variant) wave.

The main scenario discussed (in the context of NAO) is stealth respiratory pathogens. Respiratory pathogens spread fairly indiscriminately because they spread in public areas. This is unlike pathogens spread through close contact (eg: HIV or mpox), which can be contained in smaller communities more easily.

In other words, a respiratory pathogen at 1% incidence per week is as widespread as SARS-CoV-2 at its worst in the pre-vaccine era, and spreading roughly as indiscriminately (it could be spreading slower). I don't see it being contained in hard-to-target populations.

Furthermore, wastewater sequencing doesn't fully solve this. Unless you cover all wastewater across the globe, you're still not covering everything. If you do large cities it's probably not as bad as individual swabbing but I'm not sure it's much better. Other settings, such as airports/airplanes, seem at least as bad as the populations you might target for convenient swabbing (eg: hospital or primary care patients).

So while the sample might be a few times either way biased, I'm sceptical this is bridging a 40,000x gap (maybe 40,000x isn't the relevant benchmark here - see comments previously).

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