What question did this study set out to answer?

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

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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This approach enables efficient inference of viral load data in large cohorts, indicating significant improvements in computational feasibility.

To develop a cost-effective inference method for mechanistic models of viral dynamics.
Implemented a random time-shift approximation of model dynamics.
Used a combination of random and deterministic processes.
Applied the method to simulated datasets and COVID-19 data from an NBA cohort.
Demonstrated faster inference on large datasets compared to traditional methods.
Facilitated a hierarchical Bayesian treatment accounting for individual parameter differences.

Morris et al. (2025) studied this question.

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December 8, 2025PLoS Computational BiologyOpen Access

View Full Paper

Ask AI

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Authors

This approach enables efficient inference of viral load data in large cohorts, indicating significant improvements in computational feasibility.

To develop a cost-effective inference method for mechanistic models of viral dynamics.
Implemented a random time-shift approximation of model dynamics.
Used a combination of random and deterministic processes.
Applied the method to simulated datasets and COVID-19 data from an NBA cohort.
Demonstrated faster inference on large datasets compared to traditional methods.
Facilitated a hierarchical Bayesian treatment accounting for individual parameter differences.

Morris et al. (2025) studied this question.

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