The Hubverse: Streamlining Collaborative Infectious Disease Modeling for Public Health Impact
Year: 2025
Type: article
Abstract: Predictive models have become an essential tool for public health decision-making during infectious disease outbreaks [1]. Yet the rapid proliferation of models, especially during the COVID-19 pandemic, has created a fragmented landscape marked by inconsistent metrics, overlapping or conflicting forecasts, and limited comparability. This has posed serious challenges for decision-makers trying to identify reliable, policy-relevant insights. Collaborative modeling hubs offer a promising solution by coordinating model submissions, promoting transparency, and facilitating ensemble modeling, where aggregated model outputs usually outperform individual ones. Hubs also improve communication between modeling teams and stakeholders by aligning outputs with public health priorities. The hubverse [2] is a modular, open-source software ecosystem designed to support the setup and operation of these hubs. It introduces a set of data standards for probabilistic model output data, as well as utilities for setting up and administering a hub, validation and ensembling tools, visualization templates, and mechanisms for model evaluation and public-facing communication. The hubverse defines five primary user roles: hub administrators, modelers, analysts, stakeholders, and developers, and supports each with tailored tools. Importantly, the hubverse is built primarily on open-source software (R, Python, JavaScript, Arrow) and freely available platforms like GitHub, making it accessible even to research groups with limited resources. While hub administration remains the most technical aspect, the ecosystem is designed to reduce barriers across all roles. Automation, templating, and interactive interfaces lower the technical burden and make engaging meaningfully with hub products easier for a wider range of users, including public health stakeholders. This talk introduces the hubverse through real-world examples, including its recent adoption by the CDC’s FluSight influenza forecasting hub. We will highlight how this infrastructure is helping standardize infectious disease modeling efforts and support evidence-based decision-making at all levels of public health response.
Authors: Krystalli, Anna, Consortium of Infectious Disease Modeling Hubs
Language: English
Subfield: Epidemiology
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7087800947
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