Abstract
No measurement, no understanding; no understanding, no control: this foundational scientific principle was exposed as a public health dysfunction by the COVID-19 pandemic. Transmission chains spread invisibly, and the contact histories, mobility patterns, and biosignals necessary for control were never systematically collected. Although sensors and digital technologies existed, the fundamental reason measurement failed was the absence of privacy infrastructure that would have enabled people to provide data with confidence. This failure had structural reasons. The object of measurement in infectious disease control is not a physical phenomenon but human beings, and measurement therefore enters the core of privacy: contact histories, social relationships, and bodily states. Because greater precision also deepens privacy intrusion, contact-tracing apps faced 2 failures: privacy-centered designs lost epidemiological utility, while utility-centered designs were rejected through public distrust. Neither achieved sufficient measurement. This Viewpoint reframes the problem. Privacy protection is not a constraint that impedes infectious disease control but the enabling condition upon which effective measurement depends. Existing regulations and technical methods have not been designed from this premise and have therefore failed to break the cycle of structural distrust. As an institutional approach to filling this gap, we present VRAIO (verifiable record of AI output), which integrates democratic rule-setting, metadata declaration, third-party verification, tamper-proof ledgers, and violation-deterrence incentives. Once privacy infrastructure is established, this foundational principle can operate freely in infectious disease control for the first time. It will enable high-resolution epidemiology and precision intervention, opening a new path for public health that reconciles infection control with individual autonomy and social freedom without relying on blanket social shutdowns.