Welcome & Workshop Overview

WISE Workshop — Data Sciences for Modeling & HTA

Sachiko Ozawa, PhD, Sean Sylvia, PhD & Gebremedhin Beedemariam Gebretekle, PhD

2026-02-02

Welcome!

Data Sciences for Modeling & Health Technology Assessment

WISE Project Training Workshop

Addis Ababa University | February 2-7, 2026

The WISE Project

Gates Grand Challenges Award

Building local capacity in:

  • Data science for health systems
  • Predictive modeling for supply chains
  • Agent-based simulation for policy analysis

Ethiopia Context

  • Pharmaceutical supply chain challenges
  • Health workforce distribution
  • Data-driven decision making

Why This Workshop?

The WISE project aims to strengthen Ethiopia’s pharmaceutical supply chain through data-driven approaches.

The Challenge The Opportunity
Medication stockouts Rich data sources
Inefficient distribution Growing technical capacity
Limited forecasting capacity Commitment to improvement

Meet the Team

Your Instructors

Instructor Affiliation Expertise
Dr. Sachiko Ozawa UNC Eshelman School of Pharmacy WISE PI, Supply chains, ABM
Dr. Sean Sylvia UNC Gillings School of Global Public Health Health economics, ML, Reproducibility
Dr. Gebremedhin Gebretekle Public Health Agency of Canada HTA, Health economics, Antimicrobial stewardship

Participant Introductions

Let’s Get to Know Each Other

Please share:

  1. Your name
  2. Your institution/organization
  3. Your background in data sceince
  4. One thing you hope to learn this week

We have diverse backgrounds and perspectives — that’s a strength!

Workshop Goals

What You’ll Learn

By the end of this workshop, you will be able to:

  1. Apply reproducible research practices to health policy analysis
  1. Build and evaluate ML models for supply chain forecasting
  1. Understand how ML predictions feed into simulation models
  1. Design agent-based models for pharmaceutical supply chains
  1. Apply health technology assessment methods to evaluate interventions

What You’ll Build

Technical Skills Conceptual Understanding
Python for data analysis When to use ML vs. statistics
Machine learning pipeline Bias-variance tradeoff
Demand forecasting models Model validation strategies
ABM simulation basics Connecting predictions to policy

Schedule Overview

Six Days, Six Themes

Day Focus Lead
Day 1 Foundations & Reproducible Research Joint
Day 2 Machine Learning Pipeline Sylvia
Day 3 Modeling Concepts & Integration Joint
Day 4 ABM Deep Dive Ozawa
Day 5 Health Technology Assessment Gebretekle
Day 6 Economic Evaluation Methods Gebretekle

Day 1: Foundations

Today’s Schedule

Time Session
8:30-9:30 Welcome & Introductions ← You are here
9:30-10:30 Fundamentals of Data Science
10:50-12:30 Data Science in Public Health Policy
1:30-3:00 ML Foundations: Core Concepts
3:20-5:00 Hands-on: Setup + ML Demo

Day 2: Machine Learning

Time Session
8:30-10:30 Regularization: LASSO & Ridge
10:50-12:30 Hands-on: Regularized Regression
1:30-3:00 Trees & Ensembles
3:20-5:00 Hands-on: Demand Forecasting

Day 3: Integration

Time Session
8:30-10:30 Modeling Concepts (SD vs ABM)
10:50-12:30 Integration: ML Predictions to Simulation
1:30-3:00 Supply Chain Simulation Foundations
3:20-5:00 Hands-on: Introduction to ABM

Day 4: ABM Deep Dive

Time Session
8:30-10:30 Agent-Based Modeling for Supply Chains
10:50-12:30 ABM Implementation

Days 5-6: HTA & Economic Evaluation

Led by Dr. Gebremedhin Gebretekle

Day Focus
Day 5 Health Technology Assessment — frameworks, methods, decision criteria
Day 6 Economic Evaluation — cost-effectiveness, budget impact, policy application

Computing Environment

No Installation Required!

We’ll use Google Colab for all hands-on exercises.

What You Need What’s Provided
Google account Pre-configured notebooks
Web browser Sample datasets
Laptop (charged!) Shared Google Drive folder

All materials on workshop website

Getting Started with Colab

  1. Go to the Computing Setup page on the workshop website
  2. Sign in with your Google account
  3. Click the Colab badge for any notebook to open it directly
  4. Run code cells with Shift+Enter

We’ll practice this in the hands-on session this afternoon!

Workshop Norms

How We’ll Work Together

Engagement Logistics
Questions welcome anytime Breaks are important — take them!
No question is too basic Step out if needed
Help your neighbors Let us know if pace is too fast/slow
Share your expertise

A Collaborative Learning Environment

“The best way to learn is to teach, and the best way to understand is to explain.”

  • We learn from each other
  • Diverse perspectives strengthen our understanding
  • Mistakes are learning opportunities
  • Ask “why” — it helps everyone

Questions?

Before We Continue

About the Workshop About the Project
Schedule WISE goals
Materials Expected outcomes
Computing setup Next steps

Let’s Get Started!

Up Next: Fundamentals of Data Science

Session 2 | 9:30-10:30

We’ll explore:

  • What is data science?
  • ML vs. statistics vs. econometrics
  • The role of prediction in health policy