Introduction

As the world continues to suffer from the growing burden of infectious and chronic diseases due to their rising epidemiological complexity, effective surveillance systems and clinical risk stratification tools are crucial for timely interventions and improved patient care. A common mistake is to design intervention plans that overlook this complexity, leading to high costs and limited success. Additionally, the sustainability and scalability of such systems depend equally on economic feasibility and technological progress.

This two-day workshop examines both the scientific advances and the economics of disease surveillance and machine learning-based risk stratification approaches. Participants will be exposed to currently generated health data in the Ministry of Health and explore how predictive models can improve patient outcomes and outbreak responses while addressing the critical questions of cost-effectiveness, resource allocation, and return on investment. A particular emphasis will be placed on streamlining whole-genome sequencing (WGS) for infectious disease surveillance, highlighting its transformative role in tracking pathogen evolution, informing public health interventions, and ensuring cost-efficient implementation at scale. Yet, the currently implemented risk stratification approaches in surgery and medicine (including chronic diseases), will also be explored.

By bringing together experts in epidemiology, health economics, genomics, data science, and clinical practice, the workshop offers a holistic perspective on how to design, evaluate, and implement surveillance frameworks that are technologically advanced and economically sustainable.