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 engaged fourth-year undergraduate Systems Engineering students at UNSA in a semester-long, hands-on learning experience focused on addressing real-world public health challenges in Peru.

The practicum was developed within the broader framework of the Arequipa Global Change and Human Health Institute (GCHHI), a collaborative institute that facilitates research partnerships between UNSA and OU across three core areas: understanding environmental change, advancing human health, and designing adaptive social systems. GCHHI is jointly administered by the Office of the Vice Rector for Research at UNSA and OU’s Latin America Sustainability Initiative (LASI), which serves as OU’s coordinating hub for sustainability-focused research and engagement across Latin America. Through this partnership, GCHHI brings together multidisciplinary teams of faculty, staff, students, and public and private sector partners to generate new knowledge and develop solutions that improve Arequipa’s environment while supporting the health and well-being of its people.

Course Leadership and Institutional Collaboration

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

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, a core partner within the GCHHI framework. Dr. Charles Kenney (OU) serves as the Principal Investigator for Project 3, providing overall leadership. At OU, Dr. Danala and Dr. Michael C. Wimberly serve as Co-Principal Investigators representing DISC, while 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 were introduced to 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 guided project work. During project sessions, student teams presented progress updates, discussed challenges, and received feedback. This structure enabled students to reinforce theoretical knowledge through practice and iteratively strengthen their analytical skills throughout 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 composed of four members, completed projects across three public health themes—Infecciones Respiratorias Agudas (IRA), Enfermedad Diarreica Aguda (EDA), and Dengue. Many projects integrated environmental and socio-economic data to better understand health outcomes. Teams applied data engineering, statistical analysis, machine learning, and visualization techniques, with some developing interactive dashboards in Tableau to complement Python-based analyses.

The teams and their projects included:

• Pandas Crew – Identificación de zonas de alto riesgo de dengue en el Perú mediante un modelo predictivo
  (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
  (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
  (Descriptive and predictive analysis of Acute Diarrheal Disease 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
  (Predictive analysis of acute respiratory infections 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
  (Spatio-temporal analysis between mining production and ARI incidence in Arequipa: a data engineering and environmental epidemiology approach)

Mentorship and Research Training

Beyond technical skills, the practicum emphasized professional research practices including proposal development, documentation, collaboration, and reproducibility. Student 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 through a final technical report and team presentation.

Final Thoughts

The OU–UNSA HADS practicum demonstrates how international collaboration—anchored by GCHHI and coordinated through LASI—can provide students with meaningful, hands-on experience in healthcare data science while addressing real-world public health challenges. By working with local datasets and regionally relevant questions, students developed practical skills in data analysis, visualization, and modeling. At the same time, the practicum strengthened institutional partnerships and contributed to ongoing research efforts that support public health decision-making in Arequipa and across Peru, particularly at the intersection of epidemiology, environmental health, and data-driven policy.