Advancing Global Health Data Science Through an International HADS Practicum: OU–UNSA Collaboration in Peru with DISC

In Fall 2025, researchers from the University of Oklahoma (OU) and the Universidad Nacional de San Agustín de Arequipa (UNSA), Peru, launched an international practicum aimed at strengthening skills in healthcare analytics and data science. The Healthcare Analytics with Data Science (HADS) Practicum, designed and led by researchers, engaged fourth-year undergraduate Systems Engineering students at UNSA in a semester-long, hands-on experience addressing public health challenges in Peru.

The practicum was led by Dr. Gopichandh Danala, Research Scientist II at the Data Institute for Societal Challenges (DISC) at OU, who served as the primary instructor and research lead. It combined foundational instruction in healthcare analytics with team-based capstone projects focused on real-world public health problems.

This course was developed under Project 3: Public Health and Society Research in Arequipa (ISPySA), funded under Proposal No. 24-1294-NOA by the UNSA Institute for Global Changes and Human Health Research. Dr. Charles Kenney (OU) is the Principal Investigator for Project 3, providing overall leadership. At OU, Dr. Danala and Dr. Michael C. Wimberly (OU) serve as Co-Principal Investigators, representing DISC.  At UNSA, Dr. Jesús Silva, Co-Principal Investigator, coordinates local academic and research activities.

Building Foundational Skills in Health Data Science

The HADS practicum was designed for students new to data science and applied research, emphasizing the development of foundational skills alongside practical applications. Students learned core concepts, including exploratory data analysis, data preprocessing, supervised machine learning, time series analysis, model evaluation, cross-validation, bootstrapping, and model interpretability.

Each week included approximately 45 minutes of instruction with hands-on examples, followed by a short break, and 45 minutes of project work, during which student teams presented their progress, discussed challenges, and received feedback. This structure allowed students to reinforce theoretical knowledge through practical applications and iteratively develop their analytical skills over the semester.

Research Grounded in Local Public Health Needs

The practicum focused on public health issues relevant to Peru, particularly in the Arequipa region, using publicly available datasets from institutions such as MINSA, OEFA, INEI, GERESA, and SENAMHI/SINAMI.

Five student teams, each with four members, completed projects across three public health themes—Infecciones Respiratorias Agudas (IRA), Enfermedad Diarreica Aguda (EDA), and Dengue—often integrating environmental and socio-economic data to understand health outcomes. The teams applied data engineering, statistical analysis, machine learning, and visualization techniques. Some developed interactive dashboards in Tableau to complement Python-based analyses. The teams and their projects were:

• Pandas Crew – “Identificación de zonas de alto riesgo de dengue en el Perú mediante un modelo predictivo”. [English translation - “Identification of high-risk dengue areas in Peru using a predictive model.”]
• Data Health Innovators – “Diseño y desarrollo de un modelo predictivo de infecciones respiratorias agudas en la región Arequipa incorporando variables ambientales”. [English translation - “Design and development of a predictive model for acute respiratory infections in the Arequipa region incorporating environmental variables.”]
• Beetle Team – “Análisis descriptivo y predictivo de la Enfermedad Diarreica Aguda (EDA) en el Perú: un enfoque basado en datos para la vigilancia epidemiológica”. [English translation - “Descriptive and predictive analysis of Acute Diarrheal Disease (ADD) in Peru: a data-driven approach for epidemiological surveillance.”]
• Ant Team – “Análisis predictivo de infecciones respiratorias agudas (IRA) con integración de datos ambientales usando Tableau”. [English translation - “Predictive analysis of acute respiratory infections (ARI) with integration of environmental data using Tableau.”]
• Bumblebees – “Análisis espacio-temporal entre producción minera e incidencia de IRA en Arequipa: un enfoque de ingeniería de datos y epidemiología ambiental”. [English translation - “Spatio-temporal analysis between mining production and ARI incidence in Arequipa: a data engineering and environmental epidemiology approach.”]

Mentorship and Research Training

The practicum also emphasized professional research practices such as proposal development, documentation, collaboration, and reproducibility. Teams submitted a research proposal early in the semester, and weekly updates and short presentations supported ongoing progress. By the end of the course, all teams presented their findings in a final technical report and team presentation.

Final Thoughts

The OU–UNSA HADS practicum demonstrates how international collaboration can provide students with hands-on experience in healthcare data science while addressing real-world public health challenges. By using local datasets, students developed practical skills in data analysis, visualization, and modeling. At the same time, the projects supported ongoing research and strengthened partnerships that can inform future work addressing both local public health needs in Arequipa and national health priorities across Peru, including epidemiology, environmental health, and data-driven decision-making.