diff --git a/pdfs/cv/cvReich.pdf b/pdfs/cv/cvReich.pdf index d47b7aff..35a3e726 100644 Binary files a/pdfs/cv/cvReich.pdf and b/pdfs/cv/cvReich.pdf differ diff --git a/publications.html b/publications.html index 6a4025cd..72715b3d 100644 --- a/publications.html +++ b/publications.html @@ -697,6 +697,71 @@
Abstract
+
+
+
+ + + +
+
+
+

+ Real-time mechanistic bayesian forecasts of Covid-19 mortality +

+
+ Gibson GC , Reich NG , Sheldon D (2023). Annals of Applied Statistics, 17(3): 1801-1819. +
+
+
+ +
+ + covid-19 + +
+ +
+ + + pdf + + + + + article + + + save + + + + + preprint + + + +
+
+
Abstract
+ 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. +
+
+
+
+
+
@@ -913,7 +978,7 @@

Comparing trained and untrained probabilistic ensemble forecasts of COVID-19 cases and deaths in the United States

- 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). International Journal of Forecasting, : . + 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). International Journal of Forecasting, 39: 1366-1383.
@@ -1720,71 +1785,6 @@
Abstract
-
-
-
- - - -
-
-
-

- Real-time mechanistic bayesian forecasts of Covid-19 mortality -

-
- Gibson GC , Reich NG , Sheldon D (2020). medRxiv. -
-
-
- -
- - covid-19 - -
- -
- - - pdf - - - - - article - - - save - - - - - preprint - - - -
-
-
Abstract
- 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. -
-
-
-
-
-
diff --git a/research.html b/research.html index 9094adc0..04473a4e 100644 --- a/research.html +++ b/research.html @@ -242,12 +242,12 @@
Updated by - - github-actions[bot] + + Minsu on - + Nov 20, 2023