Skip to content

Commit

Permalink
Auto deploy commit 4d8d9fd to GitHub Pages at
Browse files Browse the repository at this point in the history
  • Loading branch information
GitHub Action committed Nov 20, 2023
1 parent a302dce commit 921caea
Show file tree
Hide file tree
Showing 3 changed files with 69 additions and 69 deletions.
Binary file modified pdfs/cv/cvReich.pdf
View file
Binary file not shown.
132 changes: 66 additions & 66 deletions publications.html
View file
Original file line number Diff line number Diff line change
Expand Up @@ -697,6 +697,71 @@ <h6>Abstract</h6>
</div>
</div>

<div class="column pub-item col-12" id="gibson-mortality">
<div class="card-publication columns">
<div class="card-image column col-2 col-sm-12">
<a href="#pub=gibson-mortality">
<img class="rounded pub-image" src="images/publications/gibson-mortality.png">
</a>
</div>
<div class="column col-10 col-sm-12">
<div class="card-header">
<h4 class="card-title">
<a href="#pub=gibson-mortality" class="pub-title">Real-time mechanistic bayesian forecasts of Covid-19 mortality</a>
</h4>
<h6 class="card-subtitle">
Gibson GC , Reich NG , Sheldon D (2023). <em>Annals of Applied Statistics</em>, 17(3): 1801-1819.
</h6>
</div>
<div class="card-footer">

<div class="pub-keywords">

<span class="label pub-keyword">covid-19</span>

</div>

<div class="pub-btns">

<a class="btn btn-link btn-sm pub-btn tooltip"
data-tooltip="Get pdf"
href="/pdfs/publications/gibson-mortality.pdf">
<i class="fa fa-file-pdf-o"></i> pdf
</a>


<a class="btn btn-link btn-sm pub-btn tooltip"
data-tooltip="Open doi link"
href="https://doi.org/10.1214/22-AOAS1671"
target="_blank">
<i class="fa fa-external-link"></i> article
</a>
<a class="btn btn-link btn-sm pub-btn tooltip"
data-tooltip="Save to mendeley"
href="http://www.mendeley.com/import/?url=https://doi.org/10.1214/22-AOAS1671"
target="_blank">
<i class="fa fa-save"></i> save
</a>


<a class="btn btn-link btn-sm pub-btn tooltip"
data-tooltip="Open preprint"
href="https://www.medrxiv.org/content/10.1101/2020.12.22.20248736v2"
target="_blank">
<i class="fa fa-external-link"></i> preprint
</a>


</div>
<div class="pub-abstract">
<h6>Abstract</h6>
The COVID-19 pandemic emerged in late December 2019. In the first six months of the global outbreak, the US reported more cases and deaths than any other country in the world. Effective modeling of the course of the pandemic can help assist with public health resource planning, intervention efforts, and vaccine clinical trials. However, building applied forecasting models presents unique challenges during a pandemic. First, case data available to models in real-time represent a non-stationary fraction of the true case incidence due to changes in available diagnostic tests and test-seeking behavior. Second, interventions varied across time and geography leading to large changes in transmissibility over the course of the pandemic. We propose a mechanistic Bayesian model (MechBayes) that builds upon the classic compartmental susceptible-exposed-infected-recovered (SEIR) model to operationalize COVID-19 forecasting in real time. This framework includes non-parametric modeling of varying transmission rates, non-parametric modeling of case and death discrepancies due to testing and reporting issues, and a joint observation likelihood on new case counts and new deaths; it is implemented in a probabilistic programming language to automate the use of Bayesian reasoning for quantifying uncertainty in probabilistic forecasts. The model has been used to submit forecasts to the US Centers for Disease Control, through the COVID-19 Forecast Hub. We examine the performance relative to a baseline model as well as alternate models submitted to the Forecast Hub. Additionally, we include an ablation test of our extensions to the classic SEIR models. We demonstrate a significant gain in both point and probabilistic forecast scoring measures using MechBayes when compared to a baseline model. We show that MechBayes ranks as one of the top models out of those submitted to the COVID-19 Forecast Hub. Finally, we demonstrate that MechBayes performs significantly better than the classical SEIR model.
</div>
</div>
</div>
</div>
</div>

</div>

<div class="pub-list columns">
Expand Down Expand Up @@ -913,7 +978,7 @@ <h4 class="card-title">
<a href="#pub=ray-comparing-trained-untrained" class="pub-title">Comparing trained and untrained probabilistic ensemble forecasts of COVID-19 cases and deaths in the United States</a>
</h4>
<h6 class="card-subtitle">
Ray EL, Brooks LC, Bien J, Biggerstaff M, Bosse NI, Bracher J, Cramer EY, Funk S, Gerding A, Johansson MA, Rumack A, Wang Y, Zorn M, Tibshirani RJ, Reich NG (2022). <em>International Journal of Forecasting</em>, : .
Ray EL, Brooks LC, Bien J, Biggerstaff M, Bosse NI, Bracher J, Cramer EY, Funk S, Gerding A, Johansson MA, Rumack A, Wang Y, Zorn M, Tibshirani RJ, Reich NG (2022). <em>International Journal of Forecasting</em>, 39: 1366-1383.
</h6>
</div>
<div class="card-footer">
Expand Down Expand Up @@ -1720,71 +1785,6 @@ <h6>Abstract</h6>
</div>
</div>

<div class="column pub-item col-12" id="gibson-mortality">
<div class="card-publication columns">
<div class="card-image column col-2 col-sm-12">
<a href="#pub=gibson-mortality">
<img class="rounded pub-image" src="images/publications/gibson-mortality.png">
</a>
</div>
<div class="column col-10 col-sm-12">
<div class="card-header">
<h4 class="card-title">
<a href="#pub=gibson-mortality" class="pub-title">Real-time mechanistic bayesian forecasts of Covid-19 mortality</a>
</h4>
<h6 class="card-subtitle">
Gibson GC , Reich NG , Sheldon D (2020). <em>medRxiv</em>.
</h6>
</div>
<div class="card-footer">

<div class="pub-keywords">

<span class="label pub-keyword">covid-19</span>

</div>

<div class="pub-btns">

<a class="btn btn-link btn-sm pub-btn tooltip"
data-tooltip="Get pdf"
href="https://www.medrxiv.org/content/10.1101/2020.12.22.20248736v2.full.pdf">
<i class="fa fa-file-pdf-o"></i> pdf
</a>


<a class="btn btn-link btn-sm pub-btn tooltip"
data-tooltip="Open doi link"
href="https://doi.org/10.1101/2020.12.22.20248736"
target="_blank">
<i class="fa fa-external-link"></i> article
</a>
<a class="btn btn-link btn-sm pub-btn tooltip"
data-tooltip="Save to mendeley"
href="http://www.mendeley.com/import/?url=https://doi.org/10.1101/2020.12.22.20248736"
target="_blank">
<i class="fa fa-save"></i> save
</a>


<a class="btn btn-link btn-sm pub-btn tooltip"
data-tooltip="Open preprint"
href="https://www.medrxiv.org/content/10.1101/2020.12.22.20248736v2"
target="_blank">
<i class="fa fa-external-link"></i> preprint
</a>


</div>
<div class="pub-abstract">
<h6>Abstract</h6>
The COVID-19 pandemic emerged in late December 2019. In the first six months of the global outbreak, the US reported more cases and deaths than any other country in the world. Effective modeling of the course of the pandemic can help assist with public health resource planning, intervention efforts, and vaccine clinical trials. However, building applied forecasting models presents unique challenges during a pandemic. First, case data available to models in real-time represent a non-stationary fraction of the true case incidence due to changes in available diagnostic tests and test-seeking behavior. Second, interventions varied across time and geography leading to large changes in transmissibility over the course of the pandemic. We propose a mechanistic Bayesian model (MechBayes) that builds upon the classic compartmental susceptible-exposed-infected-recovered (SEIR) model to operationalize COVID-19 forecasting in real time. This framework includes non-parametric modeling of varying transmission rates, non-parametric modeling of case and death discrepancies due to testing and reporting issues, and a joint observation likelihood on new case counts and new deaths; it is implemented in a probabilistic programming language to automate the use of Bayesian reasoning for quantifying uncertainty in probabilistic forecasts. The model has been used to submit forecasts to the US Centers for Disease Control, through the COVID-19 Forecast Hub. We examine the performance relative to a baseline model as well as alternate models submitted to the Forecast Hub. Additionally, we include an ablation test of our extensions to the classic SEIR models. We demonstrate a significant gain in both point and probabilistic forecast scoring measures using MechBayes when compared to a baseline model. We show that MechBayes ranks as one of the top models out of those submitted to the COVID-19 Forecast Hub. Finally, we demonstrate that MechBayes performs significantly better than the classical SEIR model.
</div>
</div>
</div>
</div>
</div>

<div class="column pub-item col-12" id="bi-dengue-patterns">
<div class="card-publication columns">
<div class="card-image column col-2 col-sm-12">
Expand Down
6 changes: 3 additions & 3 deletions research.html
View file
Original file line number Diff line number Diff line change
Expand Up @@ -242,12 +242,12 @@ <h6 class="project-title">
<small class="label">
Updated by

<a href="https://github.com/apps/github-actions">
github-actions[bot]
<a href="https://github.com/mkim425">
Minsu
</a>

on
<a href="https://github.com/reichlab/covid19-forecast-hub/commit/b7d987cb6845e62238c6622e7d83125d34e9108d">
<a href="https://github.com/reichlab/covid19-forecast-hub/commit/34547861c96704c178e5213139cc13de943e4de4">
Nov 20, 2023
</a>
</small>
Expand Down

0 comments on commit 921caea

Please sign in to comment.