
Developed and enhanced epidemiological and health economics modeling features in the tfojo1/jheem_analyses repository over four months, focusing on scenario-driven cost analysis and intervention evaluation for public health funding programs. Leveraged R and its ecosystem, including ggplot2 and flextable, to implement modular workflows, robust data extraction, and advanced statistical modeling. Introduced new scripts for ADAP and Ryan White intervention simulations, expanded cost modeling to include DC, and improved data pipeline reliability through targeted code organization and cleanup. Emphasized maintainability and flexibility by integrating parameterized likelihood computations and visualization panels, enabling more accurate forecasting and streamlined policy analysis for stakeholders.
June 2026 monthly summary for tfojo1/jheem_analyses: Delivered ADAP Cost Analysis Enhancements with DC Inclusion and Visualization, integrating enhanced cost-modeling and policy-visualization capabilities into the main Ryan White workflow. Expanded target coverage to include DC and streamlined simulations by removing legacy entries, focusing on adap.100.end. Two commits advanced costing files and DC support, reflecting a clear step forward in cost forecasting and policy analysis capabilities.
June 2026 monthly summary for tfojo1/jheem_analyses: Delivered ADAP Cost Analysis Enhancements with DC Inclusion and Visualization, integrating enhanced cost-modeling and policy-visualization capabilities into the main Ryan White workflow. Expanded target coverage to include DC and streamlined simulations by removing legacy entries, focusing on adap.100.end. Two commits advanced costing files and DC support, reflecting a clear step forward in cost forecasting and policy analysis capabilities.
May 2026 delivered targeted modeling enhancements and data collection improvements in the tfojo1/jheem_analyses repository, reinforcing business value through more accurate cost analyses, richer simulation data, and maintainable code organization. Key outcomes include a modular intervention analysis module with dedicated folder structure, a flexible weights parameter for likelihood computations, an expanded data extraction script for richer results, and CD4-stratified ART cost-saving analyses with re-engagement scenarios. These developments enable scenario-driven decision making, improved model fidelity, and easier future maintenance. Notable stabilization work on compute functions also contributed to more reliable analyses.
May 2026 delivered targeted modeling enhancements and data collection improvements in the tfojo1/jheem_analyses repository, reinforcing business value through more accurate cost analyses, richer simulation data, and maintainable code organization. Key outcomes include a modular intervention analysis module with dedicated folder structure, a flexible weights parameter for likelihood computations, an expanded data extraction script for richer results, and CD4-stratified ART cost-saving analyses with re-engagement scenarios. These developments enable scenario-driven decision making, improved model fidelity, and easier future maintenance. Notable stabilization work on compute functions also contributed to more reliable analyses.
April 2026: Delivered substantive enhancements to Ryan White intervention modeling in tfojo1/jheem_analyses, focusing on data extraction for costing and improved intervention logic. Replaced the adaptation script with a new intervention script to strengthen strategy evaluation and accuracy. No critical bugs reported; primarily focused on feature delivery, code quality, and robustness of the modeling pipeline, enabling earlier, more reliable public health insights.
April 2026: Delivered substantive enhancements to Ryan White intervention modeling in tfojo1/jheem_analyses, focusing on data extraction for costing and improved intervention logic. Replaced the adaptation script with a new intervention script to strengthen strategy evaluation and accuracy. No critical bugs reported; primarily focused on feature delivery, code quality, and robustness of the modeling pipeline, enabling earlier, more reliable public health insights.
March 2026 monthly summary for tfojo1/jheem_analyses focused on enhancing scenario planning capabilities for funding changes and improving pipeline robustness. Delivered an ADAP funding impact model and streamlined the intervention workflow, integrating new analyses into the main workflow and simplifying execution. Performed targeted code cleanup to remove indirect execution paths, reducing complexity and potential run-time errors.
March 2026 monthly summary for tfojo1/jheem_analyses focused on enhancing scenario planning capabilities for funding changes and improving pipeline robustness. Delivered an ADAP funding impact model and streamlined the intervention workflow, integrating new analyses into the main workflow and simplifying execution. Performed targeted code cleanup to remove indirect execution paths, reducing complexity and potential run-time errors.

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