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 R_t 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.