cdcepi/FluSight-forecast-hub
Nov 2024 – Nov 2024
1 Month active
Languages Used
CSV
Technical Skills
Data AnalysisData EngineeringData UpdateForecastingTime Series Analysis
MayaSal
PROFILE
Mayasal
Mariah Salcedo developed and maintained influenza forecast data releases for the cdcepi/FluSight-forecast-hub repository, focusing on INLA-based probabilistic forecasts for November 2024. She engineered CSV-based data pipelines to deliver forecasts across multiple locations and time horizons, ensuring data accuracy by re-adding refreshed datasets as needed. Her work involved data analysis, time series forecasting, and release automation, with careful validation to support downstream data consumers. Mariah documented release artifacts and updated repository notes to enhance reproducibility and auditability. The depth of her contributions reflects a strong grasp of data engineering principles and collaborative workflows within a public health context.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
1
Lines of code
18,544
Activity Months1
Your Network
73 people
Work History
November 2024
3 Commits • 1 Features
Nov 1, 2024Month 2024-11 for FluSight-forecast-hub: Delivered INLA-based influenza forecast CSV data releases for November 2024 (Nov 23 and Nov 30) with probabilistic forecasts across multiple locations and horizons. A targeted bug fix re-added the Nov 23 data after a refresh to preserve forecast accuracy. Overall impact includes improved forecast availability and reliability for end users, supporting timely public health decision-making. Demonstrated technologies/skills include INLA forecasting, CSV-based data pipelines, release automation, data validation, version control, and cross-team collaboration.
3 Commits • 1 Features
Nov 1, 2024Month 2024-11 for FluSight-forecast-hub: Delivered INLA-based influenza forecast CSV data releases for November 2024 (Nov 23 and Nov 30) with probabilistic forecasts across multiple locations and horizons. A targeted bug fix re-added the Nov 23 data after a refresh to preserve forecast accuracy. Overall impact includes improved forecast availability and reliability for end users, supporting timely public health decision-making. Demonstrated technologies/skills include INLA forecasting, CSV-based data pipelines, release automation, data validation, version control, and cross-team collaboration.
November 2024
Activity
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Quality Metrics
Correctness100.0%
Maintainability100.0%
Architecture100.0%
Performance100.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
CSV
Technical Skills
Data AnalysisData EngineeringData UpdateForecastingTime Series Analysis
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