Estimating Mean Viral Load Trajectory From Intermittent Longitudinal Data and Unknown Time Origins.

ABSTRACT: Viral load (VL) in the respiratory tract is the leading proxy for assessing infectiousness potential. Understanding the dynamics of disease-related VL within the host is of great importance, as it helps to determine different policies and health recommendations. However, normally the VL is measured on individuals only once, in order to confirm infection, and furthermore, the infection date is unknown. It is therefore necessary to develop statistical approaches to estimate the typical VL trajectory. We show here that, under plausible parametric assumptions, two measures of VL on infected individuals can be used to accurately estimate the VL mean function. Specifically, we consider a discrete-time likelihood-based approach to modeling and estimating partial observed longitudinal samples. We

SUBMITTER: Woodbridge Y 

PROVIDER: S-EPMC11851093 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature