Bayesian back-calculation and nowcasting for line list data during the COVID-19 pandemic

Surveillance is critical to mounting an appropriate and effective response pandemics. However, aggregated case report data suffers from reporting delays can lead misleading inferences. Different data, line list a table contains individual features such as dates of symptom onset for each reported good source modeling delays. Current methods are not particularly which typically has missing that non-ignorable In this paper, we develop Bayesian approach dynamically integrates imputation estimation data. Specifically, accurately estimate the epidemic curve instantaneous reproduction numbers, even with most missing. The also robust deviations model assumptions, changes in delay distribution or incorrect specification maximum delay. We apply COVID-19 Massachusetts find number estimates correspond more closely control measures than based on curve.