Indonesia’s AI-Powered Early Warning and Response System
Photo Credit: Feepik
Overview
In Indonesia's disease surveillance, data exists, but the systems to act on it quickly enough rarely do. Infectious disease outbreaks shaped increasingly by environmental shifts and gaps in routine health reporting continue to expose the limits of conventional early warning approaches, with the most acute consequences falling on communities already underserved by health systems.
The project strengthens and extends Indonesia's existing Early Warning Alert and Response System (EWARS) by integrating AI-driven predictive models that draw on both routine health system data and environmental data. The result is a more responsive surveillance architecture capable of detecting outbreak signals earlier, supporting faster decision-making, and improving coordination across the epidemic and pandemic response cycle. Capacity building, inclusive data practices, and regulatory alignment are woven into the design, ensuring the system are institutionally sustainable.