Dr. Elysée Tuyishime
Dr. Elysée Tuyishime is a data scientist and biostatistician with over 12 years of experience in Data Science, Applied Statistics, Epidemiology, and Public Health Informatics. He holds a Ph.D. in Data Science applied to Biostatistics, a Master’s in Applied Statistics, and a Bachelor’s in Applied Mathematics Since November 2024, he has worked with the U.S. Centers for Disease Control and Prevention (CDC), leading the development of cloud-based pipelines for processing electronic health record data and supporting national hypertension and policy surveillance dashboards. Prior to that, he spent seven years at CDC-Rwanda as a Research and Evaluation Statistician. His earlier roles include positions with UNICEF Rwanda, the Rwanda Development Board (RDB), and the Rwanda Biomedical Center (RBC), contributing to major public health and data systems initiatives. Dr. Tuyishime specializes in translating complex health data into actionable insights for policy and program improvement. Outside of work, he enjoys traveling, movies, soccer, and spending time with friends and family. For more info: LinkedIn
Core Specializations:
1. 🔬 Study Design & Research Methods Consulting
Expert support in designing robust, ethical, and efficient health research studies.
Study protocol development
Sample size calculation & power analysis
Sampling strategies and design
Research ethics and regulatory compliance guidance
2. 📋 Survey Tools & Data Infrastructure Development
Design and implement digital data collection systems tailored to your field operations.
Design of digital forms (ODK, REDCap)
Survey workflow architecture
3. 🔄 Data Cleaning & Processing Automation
Reduce human error and increase speed through reproducible, automated pipelines.
Data validation checks and automated QC scripts
Data wrangling using R, Python, or Stata
Integration of APIs and automation tools
4. 📈 Statistical Analysis & Epidemiological Interpretation
Turn raw data into actionable insights for scientific, policy, or programmatic use.
Descriptive & inferential statistics
Multivariate and longitudinal modeling
Epidemiological analysis
Results interpretation tailored for policymakers, scientists, or donors
5. 📝 Scientific Writing & Results Dissemination
Help your team publish and share findings through compelling scientific communication.
Manuscript preparation and statistical sections
Report writing for donors, ministries, and NGOs
Data visualization & graphical summaries
Slide decks for academic or policy presentations
6. 🧪 Monitoring & Evaluation (M&E) of Public Health Programs
Build or assess M&E frameworks for effective tracking and learning.
Theory of Change & logical frameworks
Indicator selection and metadata documentation
Baseline, midline, and endline evaluations
Mixed-methods study design and integration
7. 💻 EMR Data Wrangling & Health Informatics Support
Make use of routine health data from EMR and informatics systems for insights.
Cleaning and harmonizing EMR datasets
Patient-level longitudinal data structuring
ETL pipelines for routine monitoring
8. 📊 Interactive Dashboards & Data Visualization
Communicate data effectively through dynamic, user-friendly visual tools.
Dashboards in Power BI, Tableau, R Shiny, Google Data Studio, and Excel
Real-time KPI tracking for programs
9. 🧪 Evaluation of Informatics and Digital Health Systems
Assess performance and value of digital tools in public health settings.
Usability assessments of health informatics platforms
System performance monitoring and evaluation
Digital transformation advisory
10. 🧰 Tools & Platforms Capacity Building
Statistical: R, SAS, Python, Stata
Data Collection: ODK, KoboToolbox, REDCap
Visualization: Power BI, Tableau, R Shiny
Public Health Systems: DHIS2, OpenMRS, SmartCare
Workflow: Git, SQL, Google Cloud, AWS, APIs
