Pandemic Modeling [Sick]

Covid-19 discussion, bring your own statistics
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Pucksoppet
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Pandemic Modeling [Sick]

Post by Pucksoppet » Tue Apr 14, 2020 6:16 pm

Zac Weinersmith (of the comic Saturday Morning Breakfast Cereal) with an explainer published on fivethirtyeight.com.

fivethirtyeight: A Comic Strip Tour Of The Wild World Of Pandemic Modeling
Last edited by Stephanie on Thu May 14, 2020 9:36 am, edited 1 time in total.
Reason: Moved from Nerd Lab

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Bird on a Fire
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Re: Pandemic Modeling [Sick]

Post by Bird on a Fire » Wed Apr 15, 2020 1:22 pm

Thanks for this, it's an interesting overview.

Given the issues with data reliability and comparability, I'm surprised I've not seen more mention of hierarchical modelling.

For e.g. wildlife biologists, we might want to model a population in space and/or time. We do this by surveying, e.g. BoaF goes to the woods and counts all the birds he sees/hears.

But this data has inherent problems with reliability: I'll never count all the birds that are present, because I'll miss some. I might make an identification mistake, or double-count something, or whatever.

There are also comparability issues. For example, it's much easier to count all the birds in a grassy field than in a tangled thicket.

So you build a hierarchical model. Say you're interested in population size (N) at time t, and you have a bunch of counts (C) for the 10 previous time steps.

Now, you can model your counts directly, and get an estimate of C at time t, quite easily. You could then make an assumption about the (constant) relationship between C and N and call it a day.

But there might several variables influencing the relationship. As N goes up, the relative proportion counted might go down as detection facilities are overwhelmed. (This is true for birds - it's very hard to count flocks of 1000s of individuals accurately, and you typically underestimate. It also seems to be the case with COVID, as tests are limited and healthcare systems reach capacity). There's a variable lag between N and C, at least for deaths, with C depressed during the weekends. And so on.

So in hierarchical models you treat detection as a variable (p) to be estimated, and allow changes in C to be caused by either changes in N - the population of interest - or in p, the detection processes at play.

I've not seen this mentioned explicitly, but AFAICT people are mostly treating counts as if they were accurate reflections of N, and ignoring p. There may be good reasons for this (eg if the role of p is relatively small with an exponential increase in N), but given the importance of prioritising testing it seems to me that using the available data to understand what's going on with detection, and where the biggest uncertainties lie, might be valuable.

Disclaimer: I'm neither an epidemiologist nor a statistician.

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Re: Pandemic Modeling [Sick]

Post by shpalman » Wed Apr 15, 2020 5:16 pm

I've seen yet another couple of preprints trying to model if the virus arrived in Italy some time before the official first cases, or trying to figure out how many cases are really circulating in the population.

This is probably a sign that there are actually a huge number of preprints being shared around a susceptible population on ResearchGate and medRxiv and we're only seeing a relatively small proportion of those on social media; it's clear a lot more testing is required but fairly early on the authorities couldn't keep up with "review and trace co-authors" and had to go to "let any moron with a spreadsheet shed any old sh.t all over the place".

We need some research on whether a substantial fraction of the articles published in January were already about covid-19 but we didn't realize it at the time. However it's probably the case that there are a significantly higher number of research articles published in the past month compared to the same period in previous years, despite the lockdown. Or maybe because of it.
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Re: Pandemic Modeling [Sick]

Post by Woodchopper » Thu Apr 16, 2020 10:13 am

shpalman wrote:
Wed Apr 15, 2020 5:16 pm
I've seen yet another couple of preprints trying to model if the virus arrived in Italy some time before the official first cases, or trying to figure out how many cases are really circulating in the population.

This is probably a sign that there are actually a huge number of preprints being shared around a susceptible population on ResearchGate and medRxiv and we're only seeing a relatively small proportion of those on social media; it's clear a lot more testing is required but fairly early on the authorities couldn't keep up with "review and trace co-authors" and had to go to "let any moron with a spreadsheet shed any old sh.t all over the place".

We need some research on whether a substantial fraction of the articles published in January were already about covid-19 but we didn't realize it at the time. However it's probably the case that there are a significantly higher number of research articles published in the past month compared to the same period in previous years, despite the lockdown. Or maybe because of it.
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Re: Pandemic Modeling [Sick]

Post by Bird on a Fire » Thu Apr 16, 2020 8:08 pm

Preprints aren't really publications though, they're by definition unreviewed* pre-publication drafts. I'm not sure what the authorities could do to keep crap out - if they start reviewing things they're not preprints anymore.

*I know they often have a comment section, but the contents of the MS doesn't need to have been checked by anyone independent.

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Re: Pandemic Modeling [Sick]

Post by shpalman » Sun Apr 26, 2020 12:50 pm

Twitter conversation about the herd immunity and "four weeks behind Italy" b.llsh.t https://twitter.com/Kit_Yates_Maths/sta ... 77632?s=09
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Re: Pandemic Modeling [Sick]

Post by shpalman » Sun May 03, 2020 4:46 pm

Some models from Singapore https://ddi.sutd.edu.sg/
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Re: Pandemic Modeling [Sick]

Post by sTeamTraen » Sun May 17, 2020 11:51 pm

A laughably sh.t paper, ostensibly from Manchester University but mostly written by two management consultants, has been getting attention for suggesting that half of the UK population is currently infected. (It was 26%, or 29%, they aren't quite sure, on the day it was accepted by the journal, but in the meantime if the model is right it would now be between 54% and 84%.) I blogged about it, but it had already appeared on the radar of several other people. It would deserve no more than a "commendable effort, but you do need to do a lot more reading" as a first-year undergraduate statistics project.
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