InSTEDD: Integrated Global Early Warning and Response System Taha A. Kass-Hout, MD, MS and Nicolás di Tada InSTEDD, Palo Alto, California, USA With funding from: Objective Methods Results We describe a hybrid (event-based and indicator-based) surveillance platform designed to streamline the collaboration between domain experts and machine-learning algorithms for detection, prediction and response to health-related events (such as disease outbreaks or pandemics). The platform consists of several high-level modules, including: The platform synthesizes health-related event indicators from a wide variety of information sources (structured and unstructured) into a consolidated picture for analysis, maintenance of “community-wide coherence”, and collaboration. This helps detect anomalies, visualize clusters of potential events, predict the rate and spread of a disease outbreak, and provide decision makers with tools, methodologies and processes to investigate the event. Presently, the platform and associated modules are being piloted in the Mekong Basin region of Southeast Asia. Current classification includes: Data fusion: Data ETL (Extract, Transform, Load) and fusing multiple data streams Automatic feature extraction, classification and tagging Human input, hypothesis generation, collaboration and review Predictions and alerts output Field confirmation and feedback (both for human reputation and machine-learning re-training) 7 syndromes 10 transmission modes > 100 infectious diseases > 180 micro-organisms > 140 symptoms > 50 chemicals

Data fusion: Data ETL (Extract, Transform, Load) and fusing multiple data streams

Automatic feature extraction, classification and tagging

Human input, hypothesis generation, collaboration and review

Predictions and alerts output

Field confirmation and feedback (both for human reputation and machine-learning re-training)

7 syndromes

10 transmission modes

> 100 infectious diseases

> 180 micro-organisms

> 140 symptoms

> 50 chemicals