Time-Since-Infection Model for Hospitalization and Incidence Data (preprint)

ABSTRACT

The Time Since Infection (TSI) models, which use disease surveillance data to model infectious diseases, have become increasingly popular recently 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 estimate disease transmission or predict disease-related hospitalizations - 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 modeling with TSI models. Our improvements enable the estimation of key infectious disease parameters without relying on contact tracing data, reduce bias in incidence data, and provide a foundation to connect TSI models with other infectious disease models. We introduce hospitalization propensity parameters to jointly model incidence and hospitalization data. We use a composite likelihood function to accommodate complex data structure and an MCEM algorithm to estimate model parameters. We apply our method to COVID-19 data to estimate disease transmission, assess risk factor impacts, and calculate hospitalization propensity.