AI-Powered Big Data Models for Early Disease Outbreak Prediction

Data-driven models leveraging artificial intelligence (AI) and big data offer the potential for earlier detection of emerging disease outbreaks over traditional approaches.They operate with real-time visibility, can explore a broad threat landscape, and submit signals with varying reliability.Such capabilities can address a perennial challenge in infectious disease surveillance: signal generation that is timely enough to meaningfully inform response efforts.Yet despite this apparent potential, these models remain largely unexploited in public health.A candidate framework for operationalization and two case studies demonstrate the pathway: COVID-19 incidence time series models employing social media signals and long-range influenza signals for a major city in a resource-rich country-making timely signals available to public health decision-making.AI-and big-data-enabled outbreak models present an alternative detection approach that shifts traditional epidemiological assumptions.Early warnings derived from these models have distinct characteristics.Alerts can emerge at shorter lead times, multiplexed requests-demanding different signals responding to distinct factors-can be launched simultaneously, and AI-based models can harness digital exhaust, unfiltered datasets generated as by-products of everyday human activity.Such a vast volume of high-frequency data could thus enable early warning systems to submit multiple signals with different reliability scores at little additional operational overhead.