A mathematical model to estimate percentage secondary infections from margin of error of diagnostic sensitivity: Useful tool for regulatory agencies to assess the risk of propagation due to false negative outcome of diagnostics

摘要: False negative outcome of a diagnosis is one the major reasons for dissemination diseases with high risk propagation. Diagnostic sensitivity and margin error determine false diagnosis. A mathematical model had been developed to estimate mean % secondary infections based on diagnostic sensitivity, prevalence R0 value. This recommends test [≥] 96% at least 92% lower bound limit 95% CI or [≤] 4% highly infectious like COVID-19 curb transmission infection due cases. Positive relationship was found between suggesting greater higher number in population Negative correlation kits (>90% sensitivity) regulatory approval (R= -0.92, p=0.023) lesser test, chances getting approved by agencies. However, there are no specific standards available tests. Highly disease such as COVID-19, certainly need cases our can be used set CI.