Stan being used to study and fight coronavirus

Risk for Transportation of 2019 Novel Coronavirus Disease from Wuhan to Other Cities in China

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Analysis on the trend of the growth rate and the detection rate on Japan and Korea
https://github.com/yoriyuki/COVID-19/blob/master/prediction/Turzin.ipynb (Japan)
https://github.com/yoriyuki/COVID-19/blob/master/prediction/Turzin-Korea.ipynb (Korea)
No paper yet. Some explanation in Japanese
https://note.com/yoriyuki/n/n1fc028da0b68
I’m still improving a model and also want to do model selection (and validation).

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Someone pointed me to this one from the University of Texas COVID-19 Modeling Consortium: https://covid-19.tacc.utexas.edu/projections/ with writeup here: https://covid-19.tacc.utexas.edu/media/filer_public/87/63/87635a46-b060-4b5b-a3a5-1b31ab8e0bc6/ut_covid-19_mortality_forecasting_model_latest.pdf

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A paper by Simas Kucinskas, Tracking R of COVID-19: (https://papers.ssrn.com/sol3/cf_dev/AbsByAuth.cfm?per_id=2441138)

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Update. I write a paper.

https://github.com/yoriyuki/BayesianCOVID19/blob/master/paper/BayesianCOVID-19.pdf

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The paper is in the works, but I’m in Uni Iceland’s modelling group and our predictions for Iceland were very good. We have been surprised in how well we predicted total cases, and using percentages from Imperial College’s paper from March 16 we were also surprisingly spot on with hospital and ICU predictions.

We’ve been updating our website steadily at: https://covid.hi.is/english/

Technical writeup about the modelling at: https://rpubs.com/bgautijonsson/HierarchicalLogisticGrowthCurves

One good thing that’s come of our website is the Icelandic news has used our figures without having to make their own and having some information lost or accidentally changed in the process.

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Some models for errors in prevalence studies:
https://statmodeling.stat.columbia.edu/2020/05/01/simple-bayesian-analysis-inference-of-coronavirus-infection-rate-from-the-stanford-study-in-santa-clara-county/
and
https://statmodeling.stat.columbia.edu/2020/05/04/bayesian-analysis-of-santa-clara-study-run-it-yourself-in-google-collab-play-around-with-the-model-etc/
and

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I don’t know if this thread is still being used, but here’s a new one: Bayesian adjustment for preferential testing in estimating the COVID-19 infection fatality rate: Theory and methods.

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Yes, please continue adding to the thread. Thank you.

Another:

Community prevalence of SARS-CoV-2 in England: Results from the ONS Coronavirus Infection Survey Pilot

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yet another covid paper using Stan:

which extends the standard SIR model with psychosocial factors then fit to survey and covid data. I did the Stan model & happy to answer questions.

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Another:
https://statmodeling.stat.columbia.edu/2020/08/17/epidemia-an-r-package-for-bayesian-epidemiological-modelling/

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The source and corrected draft and a small case study explaining how to code Andrew’s and my model in Stan is available in our diagnostic-testing repo on GitHub.

Every statistical model is an error model in some sense. Specifically, our model lets you perform inference using a hierarchical meta-analysis of the sensitivity and specificity at a specific existing site or at a newly created test site.

Code for published paper

that is to some (unclear) extent being used by the model relied upon by Dallas County health officials

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Has anyone tried Fractional differential equations on STAN for modeling diseases?

The EpiNow2 R package uses Stan. It’s being used for national and subnational R/growth rate estimates which themselves have been used in a number of publications, e.g. Li et al. (2020), Davies et al. (2021), and others.

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Stan has also been used for nowcasts and projections of COVID-19 in the UK , to estimate epidemiological parameters from repeated cross-sectional prevalence studies, to perform nowcasting of censored case counts, and to estimate transmission advantage and generation intervals of new variants.

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The extensive supplement to Jones et al. (2021) is also built on Stan.

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Sharing my 2 main COVID-19 papers, using STAN.

  1. To my knowledge the best estimates of age-stratified COVID-19 severity, estimated through a literature meta-analysis. STAN was instrumental to put together information sources with widely different uncertainties https://link.springer.com/article/10.1186/s12879-022-07262-0 Open code and data here: GitHub - dherrera1911/estimate_covid_severity

  2. A meta-analysis of time-varying sensitivity in COVID-19 serological assays that can serve as a guide to correct for this source of bias in the literature. https://www.medrxiv.org/content/10.1101/2022.09.08.22279731v1 Code and data here: GitHub - dherrera1911/seroreversion_metaanalysis

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