The Hubverse: Streamlining Collaborative Infectious Disease Modeling for Public Health Impact

Predictive models are an essential tool for public health decision-making during infectious disease outbreaks. Yet the rapid proliferation of models during the COVID-19 pandemic produced a fragmented landscape of inconsistent metrics, conflicting forecasts, and limited comparability, leaving decision-makers struggling 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 outputs usually outperform individual ones. Hubs also improve communication between modeling teams and stakeholders by aligning outputs with public health priorities. The hubverse is a modular, open-source software ecosystem designed to support the setup and operation of these hubs. It introduces data standards for probabilistic model output, alongside utilities for hub administration, validation, ensembling, visualization, model evaluation, and public-facing communication. It defines five primary user roles: hub administrators, modelers, analysts, stakeholders, and developers, and supports each with tailored tools. Built primarily on open-source software (R, Python, JavaScript, Arrow) and freely available platforms like GitHub, the hubverse is 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 through automation, templating, and interactive interfaces, making it easier for a wider range of users, including public health stakeholders, to engage meaningfully with hub products. This talk introduces the hubverse through real-world examples, including its adoption by the CDC's FluSight influenza forecasting hub, showing how it standardizes modeling efforts and supports evidence-based public health decision-making.