ReviewCurrent opinion in virology2021
Advancing therapies for viral infections using mechanistic computational models of the dynamic interplay between the virus and host immune response.
Review in Current opinion in virology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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
9 citing papers in PubMed, 12 citations in OpenAlex.
- Analysis and simulation of a stochastic reaction-diffusion model for HBV infection under antiviral treatment.BMC infectious diseases · 2026Article
- The interplay between recovery and immunity in infectious and non-communicable diseases.Frontiers in cellular and infection microbiology · 2026Review
- Discovery and structural characterization of newly identified mutations in the HBx gene of chronic HBV patients.Scientific reports · 2025Article
- Deciphering respiratory viral infections by harnessing organ-on-chip technology to explore the gut-lung axis.Open biology · 2025Review
- Mechanistic model for booster doses effectiveness in healthy, cancer, and immunosuppressed patients infected with SARS-CoV-2.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Modulating Immune Response in Viral Infection for Quantitative Forecasts of Drug Efficacy.Pharmaceutics · 2023Review
- Compositional modelling of immune response and virus transmission dynamics.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2022Article
- Multiscale Model of Antiviral Timing, Potency, and Heterogeneity Effects on an Epithelial Tissue Patch Infected by SARS-CoV-2.Viruses · 2022Article
- Model Integration in Computational Biology: The Role of Reproducibility, Credibility and Utility.Frontiers in systems biology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
The COVID-19 pandemic has highlighted a need for improved frameworks for drug discovery, repurposing, clinical trial design and therapy optimization and personalization. Mechanistic computational models can play an important role in developing these frameworks. We discuss how mechanistic models, which consider viral entry, replication in target cells, viral spread in the body, immune response, and the complex factors involved in tissue and organ damage and recovery, can clarify the mechanisms of humoral and cellular immune responses to the virus, viral distribution and replication in tissues, the origins of pathogenesis and patient-to-patient heterogeneity in responses. These models are already improving our understanding of the mechanisms of action of antivirals and immune modulators. We discuss how closer collaboration between the experimentalists, clinicians and modelers could result in more predictive models which may guide therapies for viral infections, improving survival and leading to faster and more complete recovery.
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.