PhD student @ MRC Biostatistics Unit, University of Cambridge
1301 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!


Since the launch of GPT-4, the next relevant data point will be GPT-5. All the rest is ~noise. 

Yes, and the longer until this release the more we should update towards longer timelines

Why are you ballparking $10b when all of the examples given are many multiples of that? $100b seems like a better estimate.

I also suspect we're targeting easy to eradicate diseases. Those without animal reservoirs that will cause resurgences and where there are effective interventions. Therefore, I'd suggest this is a lower bound.

Can I get alerts when new jobs get added matching my criteria?

Your objections seem reasonable but I do not understand their implications due to a lack of finance background. Would you mind helping me understand how your points affect the takeaway? Specifically, do you think that the estimates presented here are biased, much more uncertain than the post implies, or something else?

Thanks - I think this type of careful empirical analysis, and its distillation, is some of the next content on the forum. I found your section on varying parameters particularly helpful for quantifying how sensitive the approach is to these non-empirical inputs.

Based on the abstract, that study is based on a survey where they asked people about hypothetical future scenarios. Those surveys are generally considered pretty inaccurate (most people forecast their future decisions poorly) so I wouldn't put much weight on it.

So the question is basically whether the (upkeep costs + opportunity cost of money - benefit from events) is more or less than discount from selling quickly?

What do you mean by take a huge loss? I'm not sure paper losses are relevant here.

Interesting read, I'm left unconvinced that traditional pharma is moving much slower than optimal. That would seem to imply that they're leaving a lot of money on the table (quicker approval = longer selling the drug before patent expires).

I have three speculative ideas on why this might be. Cost of the process, ability to scale the process, and risk (e.g. amount of resources wasted if a drug fails at some stage in development).

As the article points out, pharma can do this when the incentives are right (COVID vaccines) which implies there's a reason to not do it normally.

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.

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