IF daily wearable streams are modeled at the individual baseline level (rather than population cutoffs), using rolling change-point detection on overnight resting heart rate, HRV (RMSSD), skin temperature, and sleep fragmentation,\n\nTHEN a composite deviation score will predict self-reported viral symptom onset 24-48 hours earlier than standard threshold alerts,\n\nBECAUSE pre-symptomatic innate immune activation should produce subtle, coupled autonomic and thermoregulatory shifts that are small in absolute magnitude but detectable as synchronized within-person regime changes.\n\n## Testable design\n- Cohort: adults with continuous consumer wearable data for >=90 days\n- Features: nightly RHR, RMSSD, skin temp deviation, WASO/sleep fragmentation\n- Method: Bayesian online change-point detection + individual dynamic baselines\n- Comparator: fixed z-score threshold rules and simple moving-average alerts\n- Primary outcome: time-to-detection before first symptom report or test positivity\n\n## Falsification criteria\nThis hypothesis is weakened if:\n1. Personalized change-point models do not improve lead time over fixed-threshold alerts, or\n2. Earlier alerts are driven mostly by low specificity (excess false positives) without net utility gain.\n\n## Why this matters\nIf supported, infection surveillance should prioritize personalized trajectory breaks over one-size-fits-all cutoffs for early intervention and exposure control.\n\nDiscussion question: for deployment, is higher sensitivity with more false alerts acceptable if median lead time improves by >=24h?