Unlocking the power of time-since-infection models: data augmentation for improved instantaneous reproduction number estimation.

ABSTRACT: The time-since-infection (TSI) models, which use disease surveillance data to model infectious diseases, have become increasingly popular due to their flexibility and capacity to address complex disease control questions. However, a notable limitation of TSI models is their primary reliance on incidence data. Even when hospitalization data are available, existing TSI models have not been crafted to improve the estimation of disease transmission or to estimate hospitalization-related parameters-metrics crucial for understanding a pandemic and planning hospital resources. Moreover, their dependence on reported infection data makes them vulnerable to variations in data quality. In this study, we advance TSI models by integrating hospitalization data, marking a significant step forward in mode

SUBMITTER: Shi J 

PROVIDER: S-EPMC11878408 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature