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

ABSTRACT: Surveillance is critical to mounting an appropriate and effective response to pandemics. However, aggregated case report data suffers from reporting delays and can lead to misleading inferences. Different from aggregated case report data, line list data is a table contains individual features such as dates of symptom onset and reporting for each reported case and a good source for modeling delays. Current methods for modeling reporting delays are not particularly appropriate for line list data, which typically has missing symptom onset dates that are non-ignorable for modeling reporting delays. In this paper, we develop a Bayesian approach that dynamically integrates imputation and estimation for line list data. Specifically, this Bayesian approach can accurately estimate the epidemic curv

SUBMITTER: Li T 

PROVIDER: S-EPMC8297945 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature