A joint Bayesian hierarchical model for estimating SARS-CoV-2 diagnostic and subgenomic RNA viral dynamics and seroconversion

TL;DR

This paper introduces a Bayesian hierarchical model that jointly estimates SARS-CoV-2 viral loads and seroconversion timing, improving understanding of viral dynamics and immune response for COVID-19.

Contribution

The novel joint Bayesian model captures the relationship between viral loads and antibody development, enabling better inference and data imputation in COVID-19 studies.

Findings

Model accurately estimates viral load trajectories.

Successfully imputes sgRNA data from diagnostic viral load.

Identifies correlates of viral load and seroconversion.

Abstract

Understanding the viral dynamics and immunizing antibodies of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is crucial for devising better therapeutic and prevention strategies for COVID-19. Here, we present a Bayesian hierarchical model that jointly estimates the diagnostic RNA viral load reflecting genomic materials of SARS-CoV-2, the subgenomic RNAs (sgRNA) viral load reflecting active viral replication, and the rate and timing of seroconversion reflecting presence of antibodies. Our proposed method accounts for the dynamical relationship and correlation structure between the two types of viral load, allows for borrowing of information between viral load and antibody data, and identifies potential correlates of viral load characteristics and propensity for seroconversion. We demonstrate the features of the joint model through application to the COVID-19 PEP study and conduct a cross-validation exercise to illustrate the model's ability to impute the sgRNA viral trajectories for people who only had diagnostic viral load data.