Sample Prevalence vs Global Prevalence
By Jeff Kaufman @ 2024-07-08T21:01 (+34)
This is a crosspost, probably from LessWrong. Try viewing it there.
nullMathiasKB🔸 @ 2024-07-08T21:59 (+7)
Jeff, your notes on NAO are fascinating to read! I have nothing to add other than that I hope you keep posting them
Jeff Kaufman @ 2024-07-08T23:05 (+2)
Thanks!
calebp @ 2024-07-09T02:03 (+4)
Great post - I really enjoyed reading this.
I would have thought the standard way to resolve some of the questions above would be to use a large agent-based model, simulating disease transmission among millions of agents and then observing how successful some testing scheme is within the model (you might be able to backtest the model against well-documented outbreaks).
I'm not sure how much you'd trust these models over your intuitions, but I'd guess they'd have quite a lot of mileage.
I've only skimmed these papers, but these seem promising and illustrative of the direction to me:
- Scaling of agent-based models to evaluate transmission risks of infectious diseases
- 3D Agent-Based Model of Pedestrian Movements for Simulating COVID-19 Transmission in University Students
- BioWar: scalable agent-based model of bioattacks
JoshuaBlake @ 2024-07-10T22:19 (+2)
The best stuff looking at global-scale analysis of epidemics is probably by GLEAM. I doubt full agent-based modelling at small-scales is giving you much but massively complicating the model.
JoshuaBlake @ 2024-07-10T17:31 (+2)
This effect should diminish as the pandemic progresses, but at least in the <1% cumulative incidence situations I'm most interested in it should remain a significant factor.
1% cumulative incidence is quite high, so I think this is probably far along you're fine. E.g. we've estimated London hit this point for COVID around 22 Mar 2020 when it was pretty much everywhere.
Jeff Kaufman @ 2024-07-10T19:07 (+2)
I think this is probably far along you're fine
I'm not sure what you mean by this?
(Yes, 1% cumulative incidence is high -- I wish the NAO were funded to the point that we could be talking about whether 0.01% or 0.001% was achievable.)
JoshuaBlake @ 2024-07-10T22:15 (+4)
Sorry, I answered the wrong question, and am slightly confused what this post is trying to get out. I think your question is: will NYC hit 1% cumulative incidence after global 1% cumulative incidence?
I think this is almost never going to be the case for fairly indiscriminately-spreading respiratory pathogens, such as flu or COVID.
The answer is yes only if NYC's cumulative incidence is lower than the global mean region (weighted by population). Due to connectedness, I expect NYC to always be hit pretty early, as you point out, definitely before most rural communities. I think the key point here is that NYC doesn't need to be ahead of the epicentre of the disease, only the global mean.
One way of looking at this is how early on does NYC get hit compared to other cities/regions. This analysis (pdf) orders cities by connectedness to Wuhan to answer this question for COVID. It looks like they've released an online tool that lets you specify different origin locations and epidemiological parameters. So you could rank how early NYC gets hit for a range of different scenarios.
by carefully choosing a few cities to monitor around the world you can probably get to where it leads global prevalence
This would surprise me. It's hard to imagine a scenario where the arrival time at different major travel hubs is very desynchronized as these locations are highly connected to each other. So you'd probably then end up looking at a long tail of locations which are poorly connected to the main travel hubs.
Jeff Kaufman @ 2024-07-11T00:51 (+4)
[I] am slightly confused what this post is trying to get out. I think your question is: will NYC hit 1% cumulative incidence after global 1% cumulative incidence?
That's one of the main questions, yes.
The core idea is that our efficacy simulations are in terms of cumulative incidence in a monitored population, but what people generally care about is cumulative incidence in the global (or a specific country's) population.
online tool
Thanks! The tool is neat, and it's close to the approach I'd want to see.
I think this is almost never ... would surprise me
I don't see how you can say both that it will "almost never" be the case that NYC will "hit 1% cumulative incidence after global 1% cumulative incidence" but also that it would surprise you if you can get to where your monitored cities lead global prevalence?
JoshuaBlake @ 2024-07-11T14:34 (+4)
I don't see how you can say both that it will "almost never" be the case that NYC will "hit 1% cumulative incidence after global 1% cumulative incidence" but also that it would surprise you if you can get to where your monitored cities lead global prevalence?
Sorry, this is poorly phrased by me. I meant that it would surprise me if there's much benefit from adding a few additional cities.
Jeff Kaufman @ 2024-07-11T17:18 (+2)
Possibly! That would certainly be a convenient finding (from my perspective) if it did end up working out that way.
JM @ 2024-07-13T06:08 (+1)
Thank you, this is fascinating. Is there an option to monitor wastewater just from airports (as well as generally for a whole city)? Then anything brought in on international flights might be less diluted and you might be able to detect it sooner, idk?
I realise that the world is a little bit different than in 1918, but given that the Spanish Flu was spread by troop movements, I wonder what the various militaries are doing and if they see themselves as having a role in pandemic prevention?
Jeff Kaufman @ 2024-07-13T18:27 (+2)
The NAO ran a pilot where we worked with the CDC and Ginkgo to collect and sequence pooled airplane toilet waste. We haven't sequenced these samples as deeply as we would like to yet, but initial results look very promising.
Militaries are generally interested in this kind of thing, but primarily as biodefense: protecting the population and service members.
Kai Williams @ 2024-07-10T19:45 (+1)
Thanks for the post! This may not be helpful, but one thing I would be curious to see would be how the dispersion coefficient k (Discussed here; I'm sure there's a better reference source) affected the importance of having many sites. With COVID, a lot of transmission came from superspreader events, which intuitively would increase the variance of how quickly it spread in different sites. On the other hand, the flu has a low proportion of superspreader events, so testing in a well connected site might explain more of the variance?
Jeff Kaufman @ 2024-07-10T22:00 (+4)
I haven't done or seen any modeling on this, but intuitively I would expect the variance due to superspreading to have most of its impact in the very early days, when single superspreading events can meaningfully accelerate the progress of the pandemic in a specific location, and to be minimal by the time you get to ~1% cumulative incidence?