ReviewVirology2026
Quantitative viral dynamics: Methods for parameter estimation.
Review in Virology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Clinical trial simulation of antiviral drugs.Journal of virology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Fitting mathematical models of viral dynamics to serial, quantitative viral load data provides inferences on the mechanisms in virus infection. This process can reveal the speed and magnitude of viral replication, cell proliferation and death, immune responses, and/or treatment efficacy. Viral dynamics modeling involves developing conceptual models, translating them into equations, and applying the appropriate statistical tools to determine the optimal parameters such that the model recapitulates observations from human and animal infections. In this review, we outline the theoretical foundations needed to understand model fitting, parameter estimation, and what it means to achieve a good fit. We provide examples and explain the strengths and limitations of three commonly used model fitting approaches: individual fitting, population mixed effects fitting, and feature fitting. We briefly review fitting algorithms and highlight powerful available computer software packages that can be used for fitting and parameter estimation. We discuss different model types, parameter identifiability, and how future modeling efforts can leverage advances in multi-dimensional data. Finally, we conclude with simple guidelines for choosing the best approach based on available data and scientific questions.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.