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

TL;DR

This paper introduces a Bayesian generative model that jointly estimates the effective reproduction number from line list data with missing symptom onset dates, improving real-time outbreak analysis accuracy.

Contribution

It presents a unified Bayesian framework that integrates imputation, truncation adjustment, and $R_t$ estimation, avoiding biases of stepwise methods.

Findings

Joint modeling reduces bias under long reporting delays.

Generative approach quantifies uncertainty without multiple imputation.

Method outperforms stepwise approaches on synthetic and real data.

Abstract

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 epidemic phases. We find that under long reporting delays, intermediate smoothing, as is common practice in stepwise approaches, can bias nowcasts of case counts and $R_t$, which is avoided in a joint generative approach due to shared regularization of all model components. On incomplete line list data, a fully generative approach enables the quantification of uncertainty due to missing onset dates without the need for an initial multiple imputation step. In a real-world comparison using hospitalization line list data from the COVID-19 pandemic in Switzerland, we observe the same qualitative differences between approaches. Our generative modeling components have been integrated into the R package epinowcast.