This paper critically examines the analytical foundations of Japan’s COVID-19 Test-Trace-Isolate (TTI) policy framework, focusing on the interaction between PCR specificity, cycle threshold (Ct) values, and population prevalence. Using Bayesian predictive value modelling, published virological evidence on infectivity thresholds, and Ministry of Health, Labour and Welfare (MHLW) quality audit data, the study identifies four structural analytical failures embedded in the policy framework: (1) collapse of positive predictive value in low-prevalence settings, (2) temporal mismatch between test turnaround and peak infectiousness, (3) use of high Ct thresholds inconsistent with infectiousness, and (4) opportunity costs imposed on non-COVID healthcare. The analysis argues that the policy framework conflated analytical sensitivity with epidemiological utility and failed to update operational parameters as evidence accumulated. Four institutional reforms are proposed for future pandemic governance: mandatory Bayesian modelling, Ct-threshold governance, opportunity-cost accounting, and structured interdisciplinary review. This work is released as a preprint to facilitate scholarly discussion on pandemic policy design, diagnostic test interpretation, and public health governance. Preprint – not peer reviewed.
George Kujo (Mon,) studied this question.