Diagnostic serial interval as an alternative measure of clinical serial interval using ancestral COVID-19 waves in Hong Kong and mainland China

To estimate the reproductive numbers (R _t ) , it is essential to infer generation time, which is often approximated by serial interval (SI) . The SI based on clinical outcomes is not free from recall biases and even such clinical information is not always available. We defined diagnostic serial interval (SI _d ) , as a potential alternative metric and compared with traditional SI . We analyzed confirmed COVID-19 cases from three ancestral waves in Hong Kong and the first wave in mainland China. Using Bayesian methods, we inferred the distributions of effective SI and SI _d , along with onset-to-reporting delays, and compared the resulting estimates. The distributions of SI and SI _d were comparable across waves, with shorter means observed in SI _d . Reporting delays for infectors ( d ₁ ) were longer than these for infectees (d ₂ ), which was identified as a key factor influencing the temporal variation in SI _d . The PHSMs, case profile and demography were also found to be significant factors of SI _d . Time-varying R _t estimates derived from both SI and SI _d were comparable, with median absolute differences ranging from 0.12 to 0.19. Therefore, SI _d shows potentials as an alternative metric to SI for estimating R _t in assessing COVID-19 transmission dynamics can be extended for other respiratory viruses.