Evidence map›Paper›PMID 38983109›Full record

ArticleThe annals of applied statistics2023

REAL-TIME MECHANISTIC BAYESIAN FORECASTS OF COVID-19 MORTALITY.

Graham C Gibson, Nicholas G Reich, Daniel Sheldon

Abstract read
In one paragraph

Article in The annals of applied statistics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

18 citing papers in PubMed.

  1. Article
  2. Improving outbreak forecasts through model augmentation.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  3. Comparative evaluation of behavioral epidemic models using COVID-19 data.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. PAN-cODE: COVID-19 forecasting using conditional latent ODEs.Journal of the American Medical Informatics Association : JAMIA · 2022
    Article
  12. Article
  13. Article
  14. Review
  15. Trend estimation and short-term forecasting of COVID-19 cases and deaths worldwide.Proceedings of the National Academy of Sciences of the United States of America · 2022
    Article
  16. Article
  17. Article
  18. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Graham C GibsonSchool of Public Health and Health Sciences, University of Massachusetts Amherst.
Nicholas G ReichSchool of Public Health and Health Sciences, University of Massachusetts Amherst.
Daniel SheldonCollege of Information and Computer Sciences, University of Massachusetts Amherst.

Funding

Statistical methods for real-time forecasts of infectious disease: expanding dynamic time-series and machine learning approaches for pandemic scenariosR35GM119582 · NIGMS · UNIVERSITY OF MASSACHUSETTS AMHERST · PI Nicholas G Reich · 2016 to 2026
$4.8M
NIGMS NIH HHS R35 GM119582
6 · The paper itself

Abstract

The COVID-19 pandemic emerged in late December 2019. In the first six months of the global outbreak, the U.S. 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 nonstationary 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 that builds upon the classic compartmental susceptible-exposed-infected-recovered (SEIR) model to operationalize COVID-19 forecasting in real time. This framework includes nonparametric modeling of varying transmission rates, nonparametric 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 U.S. Centers for Disease Control through the COVID-19 Forecast Hub under the name MechBayes. 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 model. We demonstrate a significant gain in both point and probabilistic forecast scoring measures using MechBayes, when compared to a baseline model, and show that MechBayes ranks as one of the top two models out of nine which regularly submitted to the COVID-19 Forecast Hub for the duration of the pandemic, trailing only the COVID-19 Forecast Hub ensemble model of which which MechBayes is a part.

Indexed as

Bayesian differential equationscompartmental modelsCOVID-19 mortality forecasting

Identifiers

PMID38983109
PMCPMC11229176

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.