December 2025 performance highlights for the CDC Forecast Hub. Focused on data quality and data management improvements to support reliable dashboards and submission history. Implemented CMU-Delphi Forecast Data Refresh and Quality Control, updating forecasts to the latest December 10, 2025 data and removing non-submitted gap forecasts across multiple submissions. Key commits include: f8d66ec4a6848531eb1cc88499bcc5c3ec119968 (2025-12-03, #1078), 7c9744e7b1d78892e2cee8489dab5d5de2450f4d (2025-12-10, #1090), 72485d1842c93a352a1dce466033882762b6f656 (2025-12-17, #1112), 049715e470839833c463c3d739f5870cfab85a95 (2025-12-29, #1148). In addition, performed NSSP Data Cleanup and Performance Optimization by removing outdated NSSP CSV files (commit: drop stale nssp csv #1107). These changes improved data accuracy, dashboards’ reliability, and system performance. Business impact includes more accurate forecasts, cleaner data pipelines, and faster data processing across dashboards and submission history. Technologies/skills demonstrated include data quality controls, data pipeline maintenance, version control with granular commits, and cross-team collaboration.

November 2025: Focused on delivering a critical data hygiene feature for the CDC COVID-19 Forecast Hub (CMU-Delphi integration) and aligning forecast data with the latest CMU-Delphi releases. Implemented an initial CMU-Delphi forecast submission, removed non-submitted gap forecasts to improve data accuracy, and integrated the latest CMU-Delphi forecasts, enabling cleaner, more reliable time-series data for downstream stakeholders. The work enhances data integrity, reduces noise, and accelerates the publication cycle for updated forecasts.

September 2025 focused on expanding the CDC COVID-19 Forecast Hub data ingestion by adding CMU-Delphi time-series CSV integration, strengthening the data foundation for forecasting and trend analysis. Delivered with a clear CSV schema and traceable commit, enabling reliable downstream analytics and model input. No major bugs fixed this month. Prepared groundwork for data validation/monitoring in the next cycle to support data quality and maintainability.

July 2025 focused on delivering and integrating enhanced CMU-Delphi hospitalization forecasts into the CDC COVID-19 Forecast Hub, with emphasis on multi-location, multi-horizon quantile predictions to support public health monitoring and resource allocation. No major bugs fixed this month; work centered on feature delivery, validation, and release readiness. The initiative strengthens decision support by improving forecast uncertainty communication and aligning submission artifacts with community standards.

Concise monthly summary for 2025-04 focusing on feature delivery and its business value for the CDC COVID-19 Forecast Hub. In this period, the primary delivery was a new data product that enhances public health monitoring and forecasting capabilities by providing a time series CSV of weekly incident COVID-19 hospitalizations across multiple locations and horizons. There were no documented major bug fixes in this dataset. Overall impact and accomplishments: The new CMU-Delphi Submissions Time Series CSV enables quicker access to standardized forecast data for decision makers and downstream modeling. This release improves data availability, traceability, and reproducibility of forecast submissions, supporting more timely and accurate public health insights. Technologies/skills demonstrated: CSV-based time-series data product, multi-location and multi-horizon forecasting data handling, Git-based version control and release (commit 5bde32d0e903f231fa0c06b963616c4caffa20f9), collaboration with CMU-Delphi, and publish-ready data artifact for the hub repository CDCgov/covid19-forecast-hub.

March 2025 performance summary for CDCgov/covid19-forecast-hub focusing on the newly delivered forecast data submission format and its impact.

February 2025 monthly summary: Delivered end-to-end CMU-Delphi forecast data ingestion for FluSight-forecast-hub by introducing CSV-based inputs for CMU-TimeSeries and CMU-climate_baseline, enabling forecast values across locations, horizons, and quantiles to support up-to-date influenza hospitalization forecasting. Updated CMU-climate_baseline data for 2025-02-15 in CDCcov/covid19-forecast-hub to improve granularity of predictions. Added CMU-Delphi forecast data for March 1, 2025 with location/horizon-specific quantiles and updated the March 1 submission accordingly. All changes are tracked via explicit commit messages to ensure reproducibility and traceability. This work improves forecast accuracy, timeliness, and consistency across two major forecasting repositories, enabling better public health decision-making.

January 2025 performance summary: Focused on delivering high-quality baseline data and forecast capabilities across FluSight-forecast-hub and COVID19-forecast-hub to enable reliable January forecasting and data hub integration. Key deliverables include standardized CMU-climate baseline data (naming conventions and file paths), Jan 2025 baseline update, removal of obsolete columns, and data-entry error corrections, plus a comprehensive CMU-climate baseline forecast for 2025-01-15 in FluSight. In CDC's COVID-19 forecast hub, CMU-TimeSeries forecasts for January 2025 were updated with new quantiles across multiple locations to reflect updated hospitalization trends, and climatological baseline data/forecasts were added with data hub integration. Additional metadata work includes YAML-based climatological baseline model documentation outlining team, model version, contributors, and data inputs.

December 2024 monthly update for cdcepi/FluSight-forecast-hub: Implemented Forecast Data Horizon Cleanup to improve data quality and focus national forecasts on valid horizons; data pipeline streamlined for downstream modeling and dashboards.