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Generative Bayesian modeling to nowcast the effective reproduction number from line list data with missing symptom onset dates by Lison, Adrian; Abbott, Sam; Huisman, Jana; Stadler, Tanja is a scholarly article available to read on EtoBox.

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

The time-varying effective reproduction number $R_t$ is a widely used indicator of transmission dynamics during infectious disease outbreaks. Timely estimates of $R_t$ can be obtained from observations close to the original date of infection, such as the date of symptom onset. However, these data often have missing information and are subject to right truncation. Previous methods have addressed these problems independently by first imputing missing onset dates, then adjusting truncated case counts, and finally estimating the effective reproduction number. This stepwise approach makes it difficult to propagate uncertainty and can introduce subtle biases during real-time estimation due to the continued impact of assumptions made in previous steps. In this work, we integrate imputation, truncation adjustment, and $R_t$ estimation into a single generative Bayesian model, allowing direct joint inference of case counts and $R_t$ from line list data with missing symptom onset dates. We then use this framework to compare the performance of nowcasting approaches with different stepwise and generative components on synthetic line list data for multiple outbreak scenarios and across different

Author

Lison, Adrian; Abbott, Sam; Huisman, Jana; Stadler, Tanja

Published

2023

Language

EN