English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Understanding the viral dynamics and immunizing antibodies of the severeacute respiratory syndrome coronavirus 2 (SARS-CoV-2) is crucial for devisingbetter therapeutic and prevention strategies for COVID-19. Here, we present aBayesian hierarchical model that jointly estimates the diagnostic RNA viralload reflecting genomic materials of SARS-CoV-2, the subgenomic RNAs (sgRNA)viral load reflecting active viral replication, and the rate and timing ofseroconversion reflecting presence of antibodies. Our proposed method accountsfor the dynamical relationship and correlation structure between the two typesof viral load, allows for borrowing of information between viral load andantibody data, and identifies potential correlates of viral loadcharacteristics and propensity for seroconversion. We demonstrate the featuresof the joint model through application to the COVID-19 PEP study and conduct across-validation exercise to illustrate the model's ability to impute the sgRNAviral trajectories for people who only had diagnostic viral load data.

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