Résumé
Objectives: COVID-19 reached its fourth year of pandemic since 2020. The repeated waves of infections have been driven by multiple factors such as pathological traits of variants, diagnostic accuracy, and vaccination conditions. This study revisits and analyzes the dynamic processes of viral transmission to generate new scientific knowledge. Method(s): A cascade model of viral transmission from one case to another was developed, and theoretically analyzed how the number of infected cases at time t, D+[t], can be changed at time t+1, D+[t+1], considering six parameters: 1) k:level of transmission, 2) Rt: effective reproduction number, 3) rho: capture rate of infected cases, 4) theta: immunity protection rate in individuals, 5) epsilon: evasion rate from vaccines, and 6) Sn: test sensitivity. Result(s): The formula which associates D+[t] with D+[t+1] was given as follows: D+[t+1] = K.D+[t], where K = {(1-Sn) + (1-rho) / rho}{1-Rtk (1-theta(1-epsilon))k} / {1-Rt (1-theta(1-epsilon))}. Also, assuming K be smaller than 1, the lower limit of test sensitivity to stop the viral transmission was formulated: Sn > {Rt (1-theta(1-epsilon))-Rtk(1-theta(1-epsilon))k} / {(1-Rtk(1-theta(1-epsilon))k)rho}. In example computations, the formula indicated that a one-off PCR test with the sensitivity of 85% would not be sufficient to contain highly contagious infections such as the Omicron variants, and that it would be practically impossible to control the situation with the immune-evasive sub-variants in circulation. Conclusion(s): The theory developed in this study broadens the science on evidence-based public health and will be useful for outcomes studies and informed decisions on public policy for pandemic control.Copyright © 2023