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To develop a cost-effective inference method for mechanistic within-host viral dynamics models.

December 8, 2025PLoS Computational BiologyOpen Access

Random time-shift approximation enables hierarchical Bayesian inference of mechanistic within-host viral dynamics models on large datasets

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Novel inference method improves viral load analysis in large datasets, suggesting efficient hierarchical Bayesian approaches.

To develop a cost-effective inference method for mechanistic within-host viral dynamics models.
Implemented a random time-shift approximation combining random and deterministic processes.
Applied hierarchical Bayesian inference to large datasets.
Analyzed simulated datasets and COVID-19 monitoring data from NBA cohort.
Facilitated efficient inference without significant computational expense.
Allowed for individual-level parameter differences in the analysis.
Improved handling of process noise during early infection stages.

Morris et al. (2025) studied this question.

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December 8, 2025PLoS Computational BiologyOpen Access

View Full Paper

Ask AI

Bookmark

Authors

Novel inference method improves viral load analysis in large datasets, suggesting efficient hierarchical Bayesian approaches.

To develop a cost-effective inference method for mechanistic within-host viral dynamics models.
Implemented a random time-shift approximation combining random and deterministic processes.
Applied hierarchical Bayesian inference to large datasets.
Analyzed simulated datasets and COVID-19 monitoring data from NBA cohort.
Facilitated efficient inference without significant computational expense.
Allowed for individual-level parameter differences in the analysis.
Improved handling of process noise during early infection stages.

Morris et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark