Coordinating collaborative infectious disease modeling projects with the hubverse.

Area of Science:

• Computational science
• Epidemiology
• Data science

Background:

• Decision-makers require trustworthy predictions for threat response.
• Collaborative modeling enhances prediction relevance, transparency, and robustness.
• Centralized data portals (hubs) facilitate collaborative modeling.

Purpose of the Study:

• Introduce the hubverse, an open-source suite of standards and software.
• Streamline the creation and operation of collaborative modeling hubs.
• Enable common tools for validating, aggregating, visualizing, evaluating, and communicating model output.

Main Methods:

• Developed open-source standards for file structure and model output.
• Created software tools to support hub operations.
• Facilitated the use of common tools across multiple hubs.

Main Results:

• The hubverse enables standardized data handling and analysis.
• Nearly two dozen modeling hubs globally utilize the hubverse.
• Supported infectious disease modeling efforts by major public health organizations.

Conclusions:

• The hubverse promotes reproducible and transparent collaborative modeling.
• It enhances the utility of modeling hubs for diverse applications.
• Facilitates global collaboration in scientific prediction and forecasting.