Data-driven, automated machine-learning system for detecting emerging public health threats

A dire threat to public health can emerge from a huge variety of sources--for example, infectious diseases, a spate of drug overdoses, or exposures to toxic chemicals. Federal, state, and local health departments must respond rapidly to disease outbreaks and other emerging bio-threats. While the current automated systems for "syndromic surveillance" can help by monitoring health data and detecting disease clusters, they are not able to detect clusters with rare or previously unseen symptomology. The method is incorporated in an automated system that can enable public health practitioners to respond more quickly and effectively in the future to fast-emerging threats, including those that are unusual or novel. "Existing systems are good at detecting outbreaks of diseases that we already know about and are actively looking for, like flu or COVID," comments NYU professor Daniel B. Neill, the senior author of the study and director of the ML4G Lab.