CDCgov/covid19-forecast-hub
Nov 2024 – Mar 2025
3 Months active
Languages Used
YAMLCSV
Technical Skills
Metadata ManagementData ManagementTime Series AnalysisFile Deletion
Dmitry Shemetov
PROFILE
Dmitry Shemetov
Dmitry Shmatov contributed to the CDCgov/covid19-forecast-hub by building and maintaining core data management features for COVID-19 forecasting models. He developed a centralized YAML metadata system for the CMU-TimeSeries model, consolidating team, contributor, and data input information to streamline onboarding and ensure reproducibility. Dmitry also engineered a standardized CSV asset for CMU-Delphi submissions, enabling reliable time series analysis by structuring hospitalization metrics across key dimensions. His work included targeted file deletion to remove outdated data artifacts, improving data hygiene and governance. Throughout, he applied skills in metadata management, time series analysis, and repository integration using CSV and YAML.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
3Total
Bugs
1
Commits
3
Features
2
Lines of code
18,313
Activity Months3
Your Network
12 people
Work History
March 2025
1 Commits
Mar 1, 2025March 2025 summary for CDCgov/covid19-forecast-hub: Maintenance-focused month delivering data hygiene improvements. Removed outdated CMU data CSV files (CMU-TimeSeries and CMU-climate_baseline) from the model-output directory to prevent stale data from entering forecasts. Implemented via commit b2c0946c0a826251a443e55f4a6e2d24fe55e353, reducing confusion and data inconsistencies across downstream analyses. No new features shipped this month; primary business value is improved data reliability, clarity of model outputs, and stronger governance of output artifacts. Technologies demonstrated include Git-based change management and data artifact cleanup practices, aligning with standards for maintainable forecasting pipelines.
1 Commits
Mar 1, 2025March 2025 summary for CDCgov/covid19-forecast-hub: Maintenance-focused month delivering data hygiene improvements. Removed outdated CMU data CSV files (CMU-TimeSeries and CMU-climate_baseline) from the model-output directory to prevent stale data from entering forecasts. Implemented via commit b2c0946c0a826251a443e55f4a6e2d24fe55e353, reducing confusion and data inconsistencies across downstream analyses. No new features shipped this month; primary business value is improved data reliability, clarity of model outputs, and stronger governance of output artifacts. Technologies demonstrated include Git-based change management and data artifact cleanup practices, aligning with standards for maintainable forecasting pipelines.
March 2025
January 2025
1 Commits • 1 Features
Jan 1, 2025January 2025 monthly summary for the CDC.gov COVID-19 forecasting hub. This period focused on delivering a new data asset to support CMU-Delphi submissions and strengthen forecasting reliability. The key feature introduced is a COVID-19 hospitalization time-series CSV containing metrics categorized by reference date, target, horizon, location, and output type, improving data availability and reproducibility for downstream analyses.
January 2025
1 Commits • 1 Features
Jan 1, 2025January 2025 monthly summary for the CDC.gov COVID-19 forecasting hub. This period focused on delivering a new data asset to support CMU-Delphi submissions and strengthen forecasting reliability. The key feature introduced is a COVID-19 hospitalization time-series CSV containing metrics categorized by reference date, target, horizon, location, and output type, improving data availability and reproducibility for downstream analyses.
November 2024
1 Commits • 1 Features
Nov 1, 2024November 2024: Enhanced model governance for the COVID-19 Forecast Hub by delivering centralized metadata management for the CMU-TimeSeries forecasting model. Implemented a YAML metadata file that consolidates team details, model info, contributors, and data inputs, enabling consistent documentation, easier onboarding, and improved reproducibility across forecasting runs.
1 Commits • 1 Features
Nov 1, 2024November 2024: Enhanced model governance for the COVID-19 Forecast Hub by delivering centralized metadata management for the CMU-TimeSeries forecasting model. Implemented a YAML metadata file that consolidates team details, model info, contributors, and data inputs, enabling consistent documentation, easier onboarding, and improved reproducibility across forecasting runs.
November 2024
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
Data ManagementFile DeletionMetadata ManagementTime Series Analysis
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline