Project Methodology

Last updated: 2026-03-27

Overview

The ALIGN Global Hub transforms fragmented data into decision-ready insights through structured integration, validation, and projection methods. This report outlines the overarching methodology for the project.

Data Integration Pipeline

The Global Hub follows a multi-stage data processing pipeline:

1. Data Collection

  • Identification of high-priority health applications: HIV/AIDS, Tuberculosis (TB), Malaria, and MNCH.
  • Continuous scanning of global clinical trials, regulatory approvals, and procurement signals.
  • Regular updates from key integrated databases (e.g., Impact Global Health, MMV, FIND).

2. Harmonization and Deduplication

  • Name Matching: Standardization of product names across sources (e.g., brand names vs. generic names).
  • Identifier Mapping: Use of unique IDs (e.g., NCT trial numbers, WHO PQ IDs) to link records.
  • Record Reconciliation: When conflicting data exists (e.g., different trial phases across sources), the Hub applies rule-based logic (e.g., keep the most advanced phase).

3. Quantitative Reference Framework

  • Disease Burden: Integration of Disability-Adjusted Life Years (DALYs) from the Global Burden of Disease (GBD) study.
  • Population Impact: Calculation of potential impact based on prevalence and target population size.
  • Cost-Effectiveness: Standardized analysis using published data or comparative benchmarks.

4. Projection and Forecasting

  • Application of the Mao et al. (2025) methodology to estimate launch timelines and market uptake in LMICs based on historical patterns of similar products.

5. Validation Protocol

  • Data validation is performed through internal reviews and cross-source comparisons to ensure high-quality and reliable outputs.

Decision Support Workflow

The Hub supports a simple but robust decision-making workflow:

  1. Explore: Use the Overview page to filter by application, development stage, or geography.
  2. Compare: Add products to the comparison cart and view them on the Product Comparison page.
  3. Interpret: Review projected vs. actual milestone timelines and readiness scores.
  4. Decide: Use the structured data to support evidence-informed prioritization and introduction decisions.

Future Roadmap

The methodology will continue to evolve as more data sources are integrated and forecasting models are refined. Upcoming enhancements include:

  • Advanced forecasting models.
  • Portfolio prioritization tools.
  • Multi-country rollout with demand and delivery data integration.

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