Detecting infectious disease outbreaks promptly is crucial for effective public health responses, minimizing transmission, and enabling critical interventions. This study introduces a method that integrates machine learning (ML)-based diagnostic predictions with traditional epidemiological surveillance to enhance biosurveillance systems. Using 4.5 million patient records from 2010 to 2022, ML models were trained to predict, within 24-hour intervals, the likelihood of patients being diagnosed with infectious or unspecified gastrointestinal, respiratory, or neurological diseases. High-confidence predictions were combined with final diagnoses and analyzed using spatiotemporal outbreak detection techniques. Among diseases with five or more outbreaks between 2014 and 2022, 33.3% (41 of 123 outbreaks) were detected earlier, with lead times ranging from 1 to 24 days and an average of 1.33 false positive outbreaks detected annually. This approach demonstrates the potential of integrating ML with conventional methods for faster outbreak detection, provided adequate disease-specific training data is available.

The authors have declared no competing interest.

The work was partially funded by Agreement No. 70RWMD21K00000009 (with the U.S. Department of Homeland Security) awarded to the Lawrence Livermore National Laboratory by the Department of Homeland Security (DHS) Countering Weapons of Mass Destruction Office (CWMD) and LLNL LDRD Program under Project No. 25-ERD-023. VXL was supported in part by NIH R35GM128672. The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains, a nonexclusive, paid up, irrevocable, world-wide license to publish or reproduce the published form of this article, or allow others to do so, for U.S. Government purposes.

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This study was approved by the Kaiser Permanente Northern California Institutional Review Board.

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