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.
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.
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.
[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?
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.
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.
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.)
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.
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.
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.
Thanks! The tool is neat, and it’s close to the approach I’d want to see.
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.
Possibly! That would certainly be a convenient finding (from my perspective) if it did end up working out that way.