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The time-varying effective reproduction number $R_t$ is a widely usedindicator of transmission dynamics during infectious disease outbreaks. Timelyestimates of $R_t$ can be obtained from observations close to the original dateof infection, such as the date of symptom onset. However, these data often havemissing information and are subject to right truncation. Previous methods haveaddressed these problems independently by first imputing missing onset dates,then adjusting truncated case counts, and finally estimating the effectivereproduction number. This stepwise approach makes it difficult to propagateuncertainty and can introduce subtle biases during real-time estimation due tothe continued impact of assumptions made in previous steps. In this work, weintegrate imputation, truncation adjustment, and $R_t$ estimation into a singlegenerative Bayesian model, allowing direct joint inference of case counts and$R_t$ from line list data with missing symptom onset dates. We then use thisframework to compare the performance of nowcasting approaches with differentstepwise and generative components on synthetic line list data for multipleoutbreak scenarios and across different epidemic phases. We find that underlong reporting delays, intermediate smoothing, as is common practice instepwise approaches, can bias nowcasts of case counts and $R_t$, which isavoided in a joint generative approach due to shared regularization of allmodel components. On incomplete line list data, a fully generative approachenables the quantification of uncertainty due to missing onset dates withoutthe need for an initial multiple imputation step. In a real-world comparisonusing hospitalization line list data from the COVID-19 pandemic in Switzerland,we observe the same qualitative differences between approaches. Our generativemodeling components have been integrated into the R package epinowcast.

Generative Bayesian modeling to nowcast the effective reproduction number from line list data with missing symptom onset dates