dhis2-chap/chap-core
Mar 2025 – Jan 2026
6 Months active
Languages Used
PythonMarkdownRSTrst
Technical Skills
Data ProcessingData StructuresLicensingModular DesignObject-Oriented ProgrammingSoftware Architecture
geirksa
PROFILE
Geirksa
Geir K. Sandve developed modular data and evaluation frameworks for the dhis2-chap/chap-core repository, focusing on multi-location disease observation and forecasting workflows. He designed extensible data structures and implemented component-based evaluators, enabling flexible, location-aware analytics. Using Python and Markdown, Geir refactored data transformation utilities to standardize and align time series forecasts with observed data, supporting scalable backtesting and improved forecast accuracy. He modernized documentation to streamline onboarding and model integration, restructuring guides and enhancing information architecture. His work demonstrated depth in software architecture, data processing, and technical writing, establishing a robust foundation for reusable analytics and maintainable, compliant codebases.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
23Total
Bugs
0
Commits
23
Features
8
Lines of code
6,259
Activity Months6
Your Network
28 peopleSame Organization
@ifi.uio.no2
Shared Repositories
26
Abyot Asalefew GizawMember
Andreas JensenMember
Andre gregussenMember
AndreGreguMember
audunMember
BumpVersion ActionMember
kanduricMember
Eirik HaugstulenMember
HalvardgithubMember
Work History
January 2026
7 Commits • 2 Features
Jan 1, 2026January 2026 was focused on strengthening the CHAP developer experience through comprehensive documentation and onboarding enhancements, coupled with standardized data formats to support modeling with Chap. Key outcomes include clearer platform guidance for current and future functionalities, improved onboarding resources for new contributors, and standardized data formats for historic data and weather forecasts. There were no critical bugs reported; all changes were documentation-driven, aligning with the roadmap and enhancing maintainability and future feature readiness. The work demonstrates strong collaboration, precision in documentation, and hands-on experience with data standardization and platform guidance.
7 Commits • 2 Features
Jan 1, 2026January 2026 was focused on strengthening the CHAP developer experience through comprehensive documentation and onboarding enhancements, coupled with standardized data formats to support modeling with Chap. Key outcomes include clearer platform guidance for current and future functionalities, improved onboarding resources for new contributors, and standardized data formats for historic data and weather forecasts. There were no critical bugs reported; all changes were documentation-driven, aligning with the roadmap and enhancing maintainability and future feature readiness. The work demonstrates strong collaboration, precision in documentation, and hands-on experience with data standardization and platform guidance.
January 2026
December 2025
4 Commits • 1 Features
Dec 1, 2025December 2025 monthly summary for dhis2-chap/chap-core focused on delivering a comprehensive documentation update for statistical and ML modeling in climate-sensitive disease forecasting, with emphasis on guidance, tutorials, evaluation methods, and uncertainty quantification (including Bayesian approaches). The work enhances onboarding, reproducibility, and methodological transparency for data scientists and epidemiologists working in climate-sensitive contexts.
December 2025
4 Commits • 1 Features
Dec 1, 2025December 2025 monthly summary for dhis2-chap/chap-core focused on delivering a comprehensive documentation update for statistical and ML modeling in climate-sensitive disease forecasting, with emphasis on guidance, tutorials, evaluation methods, and uncertainty quantification (including Bayesian approaches). The work enhances onboarding, reproducibility, and methodological transparency for data scientists and epidemiologists working in climate-sensitive contexts.
September 2025
4 Commits • 1 Features
Sep 1, 2025September 2025 focused on elevating CHAP's developer onboarding and model integration experience through a documentation modernization effort in chap-core. The work centralized and clarified guidance for integrating external models, revamped the landing page, reorganized the external models section, removed redundant pages, and updated links to HTML format. These changes improve discoverability, reduce onboarding time for new contributors, and set a scalable documentation foundation for future model integrations.
4 Commits • 1 Features
Sep 1, 2025September 2025 focused on elevating CHAP's developer onboarding and model integration experience through a documentation modernization effort in chap-core. The work centralized and clarified guidance for integrating external models, revamped the landing page, reorganized the external models section, removed redundant pages, and updated links to HTML format. These changes improve discoverability, reduce onboarding time for new contributors, and set a scalable documentation foundation for future model integrations.
September 2025
June 2025
1 Commits • 1 Features
Jun 1, 2025June 2025: Delivered a targeted Forecast Data Representation Refactor and Enhancements in dhis2-chap/chap-core to enable accurate, multi-location forecasting aligned with observations. Implementations include: refactoring data transformation for multi-location forecasts; grouping and sorting by last seen period for reliable trend analysis; a new function to convert single split-point forecasts; and time-period filtering to ensure forecasts reflect observed intervals. All work is evidenced by the commit history and testing scaffolding to bootstrap local validation using YAML configurations, enabling smoother future CI integration. This lays the groundwork for scalable forecasting, improved data quality, and faster, more reliable analytics for multi-location operations.
June 2025
1 Commits • 1 Features
Jun 1, 2025June 2025: Delivered a targeted Forecast Data Representation Refactor and Enhancements in dhis2-chap/chap-core to enable accurate, multi-location forecasting aligned with observations. Implementations include: refactoring data transformation for multi-location forecasts; grouping and sorting by last seen period for reliable trend analysis; a new function to convert single split-point forecasts; and time-period filtering to ensure forecasts reflect observed intervals. All work is evidenced by the commit history and testing scaffolding to bootstrap local validation using YAML configurations, enabling smoother future CI integration. This lays the groundwork for scalable forecasting, improved data quality, and faster, more reliable analytics for multi-location operations.
May 2025
1 Commits • 1 Features
May 1, 2025May 2025 monthly summary for dhis2-chap/chap-core: Delivered a Forecast Evaluation Framework enabling multi-location time series representations and an MAE-based evaluator for backtest forecasts vs true values across locations and time periods. Implemented data representation and transformation utilities to standardize backtest data across multiple locales, and resolved a Knut length mismatch bug in the evaluation path to ensure correct alignment of series. This work enhances structured forecast accuracy analysis, supports data-driven decisions, and lays groundwork for location-specific diagnostics and scalable backtesting.
1 Commits • 1 Features
May 1, 2025May 2025 monthly summary for dhis2-chap/chap-core: Delivered a Forecast Evaluation Framework enabling multi-location time series representations and an MAE-based evaluator for backtest forecasts vs true values across locations and time periods. Implemented data representation and transformation utilities to standardize backtest data across multiple locales, and resolved a Knut length mismatch bug in the evaluation path to ensure correct alignment of series. This work enhances structured forecast accuracy analysis, supports data-driven decisions, and lays groundwork for location-specific diagnostics and scalable backtesting.
May 2025
March 2025
6 Commits • 2 Features
Mar 1, 2025March 2025 monthly summary for dhis2-chap/chap-core. Key features delivered include the Modular Assessment Data and Evaluation Framework, with modular data structures for disease observations, time series, and multi-location data, along with an abstract base evaluator and a component-based evaluator to enable flexible, location-aware assessment workflows. Licensing updates were completed with a move to AGPLv3, including temporary license file removal to re-establish the license via GitHub wizard and subsequent license text update. A minor fix addressed a stability issue in the modularised evaluation representations. Overall impact includes a solid foundation for reusable analytics across locations, improved compliance posture, and enhanced maintainability. Technologies demonstrated include modular architecture design, abstraction layers, component-based evaluation, license management, and disciplined Git-based traceability.
March 2025
6 Commits • 2 Features
Mar 1, 2025March 2025 monthly summary for dhis2-chap/chap-core. Key features delivered include the Modular Assessment Data and Evaluation Framework, with modular data structures for disease observations, time series, and multi-location data, along with an abstract base evaluator and a component-based evaluator to enable flexible, location-aware assessment workflows. Licensing updates were completed with a move to AGPLv3, including temporary license file removal to re-establish the license via GitHub wizard and subsequent license text update. A minor fix addressed a stability issue in the modularised evaluation representations. Overall impact includes a solid foundation for reusable analytics across locations, improved compliance posture, and enhanced maintainability. Technologies demonstrated include modular architecture design, abstraction layers, component-based evaluation, license management, and disciplined Git-based traceability.
Activity
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Quality Metrics
Correctness95.6%
Maintainability95.6%
Architecture95.6%
Performance91.4%
AI Usage20.0%
Skills & Technologies
Programming Languages
MarkdownPythonRSTrst
Technical Skills
Bayesian analysisCSVData ProcessingData RepresentationData StructuresData TransformationDocumentationForecastingInformation ArchitectureLicensingModel EvaluationModular DesignObject-Oriented ProgrammingPythonR
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