cdcepi/FluSight-forecast-hub
Jan 2025 – Jan 2025
1 Month active
Languages Used
CSVYAML
Technical Skills
ConfigurationData EngineeringDocumentationEpidemiological ModelingMetadata ManagementTime Series Analysis
Dylan H. Morris
PROFILE
Dylan H. Morris
Worked on the cdcepi/FluSight-forecast-hub repository to enhance epidemiological modeling workflows by introducing structured model metadata, clarifying benchmarking documentation, and adding a new influenza hospitalization forecast dataset. Leveraged skills in configuration, metadata management, and data engineering to align model information and documentation with forecasting hub standards, improving discoverability and reproducibility. Utilized CSV and YAML formats to organize model details, team information, and data inputs, ensuring traceability and facilitating faster model onboarding. The technical approach focused on clear documentation and robust data asset integration, supporting more reliable public health forecasting and enabling transparent model comparisons within the repository framework.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
3
Lines of code
3,498
Activity Months1
Your Network
66 peopleShared Repositories
66
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aperofskyMember
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aniruddhadigaMember
Annabella HinesMember
Anna KrystalliMember
aperofskyMember
ausmeyerMember
Work History
January 2025
3 Commits • 3 Features
Jan 1, 2025Concise monthly summary for 2025-01 focusing on the FluSight-forecast-hub repository. Delivered targeted enhancements in data modeling, documentation, and data assets to improve model discoverability, benchmarking clarity, and public-health forecasting inputs. Key outcomes include structured model metadata, clarified benchmarking documentation, and the addition of a new influenza hospitalization forecast CSV dataset, with clear commits enabling traceability and reproducibility.
3 Commits • 3 Features
Jan 1, 2025Concise monthly summary for 2025-01 focusing on the FluSight-forecast-hub repository. Delivered targeted enhancements in data modeling, documentation, and data assets to improve model discoverability, benchmarking clarity, and public-health forecasting inputs. Key outcomes include structured model metadata, clarified benchmarking documentation, and the addition of a new influenza hospitalization forecast CSV dataset, with clear commits enabling traceability and reproducibility.
January 2025
Activity
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Quality Metrics
Correctness100.0%
Maintainability100.0%
Architecture100.0%
Performance100.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
CSVYAML
Technical Skills
ConfigurationData EngineeringDocumentationEpidemiological ModelingMetadata ManagementTime Series Analysis
Repositories Contributed To
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