Evidence map›Paper›PMID 33574342›Full record

ArticleScientific data2021

The Zoltar forecast archive, a tool to standardize and store interdisciplinary prediction research.

Nicholas G Reich, Matthew Cornell, Evan L Ray, Katie House, Khoa Le

Abstract read
In one paragraph

Article in Scientific data, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States.Proceedings of the National Academy of Sciences of the United States of America · 2022
    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

5 authors.

Nicholas G ReichDepartment of Biostatistics and Epidemiology, University of Massachusetts-Amherst, Amherst, MA, 01003, USA. nick@umass.edu.ORCID 0000-0003-3503-9899
Matthew CornellDepartment of Biostatistics and Epidemiology, University of Massachusetts-Amherst, Amherst, MA, 01003, USA.
Evan L RayDepartment of Biostatistics and Epidemiology, University of Massachusetts-Amherst, Amherst, MA, 01003, USA.
Katie HouseCollege of Information and Computer Sciences, University of Massachusetts-Amherst, Amherst, MA, 01003, USA.
Khoa LeCollege of Information and Computer Sciences, University of Massachusetts-Amherst, Amherst, MA, 01003, USA.

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 GM119582U.S. Department of Health & Human Services | Centers for Disease Control and Prevention (CDC) 1U01IP001122U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS) R35GM119582
6 · The paper itself

Abstract

Forecasting has emerged as an important component of informed, data-driven decision-making in a wide array of fields. We introduce a new data model for probabilistic predictions that encompasses a wide range of forecasting settings. This framework clearly defines the constituent parts of a probabilistic forecast and proposes one approach for representing these data elements. The data model is implemented in Zoltar, a new software application that stores forecasts using the data model and provides standardized API access to the data. In one real-time case study, an instance of the Zoltar web application was used to store, provide access to, and evaluate real-time forecast data on the order of 10

Indexed as

SoftwareCOVID-19Datasets as TopicDisease OutbreaksForecastingHumansReference Standards

Identifiers

PMID33574342
PMCPMC7878896

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.