Evidence map›Paper›PMID 41282807›Full record

ArticlemedRxiv : the preprint server for health sciences2025

Coordinating collaborative infectious disease modeling projects with the hubverse.

Consortium of Infectious Disease Modeling Hubs, Melissa Kerr, Rebecca Borchering, Alvaro Castro Rivadeneira, Lucie Contamin, Sebastian Funk, Harry Hochheiser, Emily Howerton, Anna Krystalli, Li Shandross and 1 more

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors.

Consortium of Infectious Disease Modeling Hubs
Melissa KerrUniversity of Massachusetts Amherst, Amherst, MA, USA.ORCID 0009-0002-4838-0850
Rebecca BorcheringInfluenza Division, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA.ORCID 0000-0003-4309-2913
Alvaro Castro RivadeneiraUniversity of Massachusetts Amherst, Amherst, MA, USA.
Lucie ContaminUniversity of Pittsburgh, Pittsburgh, PA, USA.
Sebastian FunkLondon School of Hygiene and Tropical Medicine, London, UK.ORCID 0000-0002-2842-3406
Harry HochheiserUniversity of Pittsburgh, Pittsburgh, PA, USA.ORCID 0000-0001-8793-9982
Emily HowertonPrinceton University, Princeton, NJ, USA.
Anna KrystalliR-RSE SMPC.
Li ShandrossUniversity of Massachusetts Amherst, Amherst, MA, USA.
Nicholas G ReichUniversity of Massachusetts Amherst, Amherst, MA, USA.ORCID 0000-0003-3503-9899

Funding

WORK ORDER 126643 B539 EXPAND IC SUITE75N91019D00024 · NIAID · LEIDOS BIOMEDICAL RESEARCH, INC. · PI BRISCOE, LYNN · 2019 to 2025
$3932.6M
MIDAS Coordination CenterU24GM132013 · NIGMS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI HOCHHEISER, HARRY S · 2019 to 2023
$8.1M
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
MIDAS Coordination Center - Year 6-10R24GM153920 · NIGMS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI HARRY S HOCHHEISER · 2024 to 2026
$4.3M
NCI NIH HHS 75N91019D00024NCIRD CDC HHS U01 IP001122NIGMS NIH HHS R24 GM153920NIGMS NIH HHS R35 GM119582NIGMS NIH HHS U24 GM132013Wellcome Trust
6 · The paper itself

Abstract

To better respond to a range of threats, decision-makers in diverse fields are increasingly interested in predictions they can understand and trust. Collaborative modeling can help increase the relevance, transparency, and robustness of predictions. This approach can be facilitated with hubs, or centralized data portals to collect, analyze, and communicate model output. This paper introduces the hubverse, an open-source suite of standards and software tools to streamline the creation and operation of collaborative modeling hubs. Hubverse file structure and model output standards enable the use of common tools to validate, aggregate, visualize, evaluate, and communicate model output. Currently, the hubverse is used by nearly two dozen collaborative and local modeling hubs around the globe to support infectious disease modeling efforts, including hubs hosted and/or used by the United States Centers for Disease Control and Prevention, the European Centre for Disease Prevention and Control, the Australia-Aotearoa Consortium for Epidemic Forecasting and Analytics, and the California Department of Public Health.

Identifiers

PMID41282807
PMCPMC12632657

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.