Evidence map›Paper›PMID 33564783›Full record

ArticlemedRxiv : the preprint server for health sciences2021

A Quantitative Evaluation of COVID-19 Epidemiological Models.

Osman N Yogurtcu, Marisabel Rodriguez Messan, Richard C Gerkin, Artur A Belov, Hong Yang, Richard A Forshee, Carson C Chow

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Accuracy of US CDC COVID-19 forecasting models.Frontiers in public health · 2024
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Osman N YogurtcuOffice of Biostatistics and Epidemiology, Center for Biologics Evaluation and Research, US FDA, 10903 New Hampshire Ave, Silver Spring, 20993, Maryland, USA.
Marisabel Rodriguez MessanOffice of Biostatistics and Epidemiology, Center for Biologics Evaluation and Research, US FDA, 10903 New Hampshire Ave, Silver Spring, 20993, Maryland, USA.
Richard C GerkinSchool of Life Sciences, Arizona State University, Tempe, 85287, Arizona, USA.
Artur A BelovOffice of Biostatistics and Epidemiology, Center for Biologics Evaluation and Research, US FDA, 10903 New Hampshire Ave, Silver Spring, 20993, Maryland, USA.
Hong YangOffice of Biostatistics and Epidemiology, Center for Biologics Evaluation and Research, US FDA, 10903 New Hampshire Ave, Silver Spring, 20993, Maryland, USA.
Richard A ForsheeOffice of Biostatistics and Epidemiology, Center for Biologics Evaluation and Research, US FDA, 10903 New Hampshire Ave, Silver Spring, 20993, Maryland, USA.
Carson C ChowMathematical Biology Section, NIDDK/LBM, NIH, Bethesda, 20892, Maryland, USA.

Funding

Modeling and data analysis of obesity, insulin resistance, and diabetesZIADK013022 · NIDDK · NATIONAL INSTITUTE OF DIABETES AND DIGESTIVE AND KIDNEY DISEASES · PI CHOW, CARSON · 2009 to 2025
$2.8M
CRCNS Data Sharing: Exchange and Evaluation of Reduced Neuron ModlesR01EB021711 · NIBIB · ARIZONA STATE UNIVERSITY-TEMPE CAMPUS · PI GERKIN, RICHARD C · 2015 to 2017
$390k
NIBIB NIH HHS R01 EB021711
6 · The paper itself

Abstract

Quantifying how accurate epidemiological models of COVID-19 forecast the number of future cases and deaths can help frame how to incorporate mathematical models to inform public health decisions. Here we analyze and score the predictive ability of publicly available COVID-19 epidemiological models on the COVID-19 Forecast Hub. Our score uses the posted forecast cumulative distributions to compute the log-likelihood for held-out COVID-19 positive cases and deaths. Scores are updated continuously as new data become available, and model performance is tracked over time. We use model scores to construct ensemble models based on past performance. Our publicly available quantitative framework may aid in improving modeling frameworks, and assist policy makers in selecting modeling paradigms to balance the delicate trade-offs between the economy and public health.

Indexed as

COVID-19EpidemiologyForecastingModelingScoring

Identifiers

PMID33564783
PMCPMC7872378

What OpenQuestion holds

Textmetadata
LicenceCC0
Read underepoch 390

Registered trials

None linked

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.