The class provides a practical and accessible introduction to infectious disease modeling as a tool for strengthening public health decision-making in Papua. Participants will learn how mathematical modeling translates complex real-world health challenges—such as disease transmission, intervention timing, and resource allocation—into structured analytical frameworks that can guide more effective action. With Papua’s unique epidemiological patterns, geographic barriers, and limited health resources, modeling offers invaluable insights for designing targeted, context-specific strategies.

Through a combination of lectures and hands-on exercises, the course will equip participants with foundational skills in building, interpreting, and applying infectious disease models using R and RStudio. Participants will explore local disease dynamics, simulate intervention scenarios, and assess their potential impact before implementation. The course also emphasizes the importance of integrating scientific modeling with local public health priorities, enabling participants to support evidence-based program planning and policy decisions.

Beyond technical skills, this course aims to strengthen collaboration between modelers, public health practitioners, and policymakers, ensuring that modeling outputs are relevant, actionable, and aligned with real-world needs. By the end of the training, participants will understand how to use modeling as a tool to improve infectious disease control and advance better health outcomes for communities across Papua.

Class Structure

The four-day class combines theoretical foundations with hands-on practice and policy discussions:

Day 1 focuses on introducing infectious disease modeling fundamentals, including theory, model demonstration, and hands-on model building. Participants will also learn how modeling research supports public health improvement through case studies.

Day 2 emphasizes Papua's specific public health context, including current disease priorities, surveillance systems, and challenges. Interactive breakout sessions will identify key research questions and facilitate partnerships between modelers and policymakers to develop actionable project plans.

Day 3 provides intensive hands-on training in modeling disease outbreaks and fitting models to real-world data using R.

Day 4 covers advanced topics, including modeling public health interventions and vector-borne disease modeling.