Evidence map›Paper›PMID 33410134›Full record

ReviewClinical pharmacology and therapeutics2021

Mechanistic Modeling of SARS-CoV-2 and Other Infectious Diseases and the Effects of Therapeutics.

Alan S Perelson, Ruian Ke

Open access · bronzeAbstract readReview
In one paragraph

Review in Clinical pharmacology and therapeutics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers.

0numbers the graph read from it
0cells of the map it votes in
61citing papers in PubMed
6.1field-weighted citation impact, top 3% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

61 citing papers in PubMed, 107 citations in OpenAlex.

  1. Trial
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  4. Bemnifosbuvir: An HCV NS5B Inhibitor With Multiple Modes of Action.Clinical pharmacology and therapeutics · 2026
    Article
  5. Article
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  7. Review
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  12. The kinetics of SARS-CoV-2 infection based on a human challenge study.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
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1 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors at 2 institutions in 1 country.

Alan S PerelsonLos Alamos National Laboratory, Theoretical Biology and Biophysics Group, Los Alamos, New Mexico, USA.
Ruian KeLos Alamos National Laboratory, Theoretical Biology and Biophysics Group, Los Alamos, New Mexico, USA.
Los Alamos National Laboratory · USNew Mexico Consortium · US

Funding

Modeling Viral and T Lymphocyte DynamicsR01OD011095 · OD · TRIAD NATIONAL SECURITY, LLC · PI PERELSON, ALAN S · 2012 to 2024
$5.0M
Immune System Modeling/HIVR01AI028433 · NIAID · UNIVERSITY OF CALIF-LOS ALAMOS NAT LAB · PI PERELSON, ALAN S · 1990 to 2021
$3.7M
Modeling the HIV latent reservoir, latency reversal and immunotherapeutics for HIV cureR01AI152703 · NIAID · TRIAD NATIONAL SECURITY, LLC · PI KE, RUIAN · 2020 to 2024
$3.0M
High resolution profiling and computational modeling of influenza virus-immune dynamics during natural infectionU01AI186993 · NIAID · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · PI Christopher Byron Brooke, Ruian Ke · 2025 to 2026
$2.7M
NIAID NIH HHS R01 AI028433NIAID NIH HHS R01 AI152703NIAID NIH HHS U01 AI186993NIH HHS R01 OD011095
6 · The paper itself

Abstract

Modern viral kinetic modeling and its application to therapeutics is a field that attracted the attention of the medical, pharmaceutical, and modeling communities during the early days of the AIDS epidemic. Its successes led to applications of modeling methods not only to HIV but a plethora of other viruses, such as hepatitis C virus (HCV), hepatitis B virus and cytomegalovirus, which along with HIV cause chronic diseases, and viruses such as influenza, respiratory syncytial virus, West Nile virus, Zika virus, and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which generally cause acute infections. Here we first review the historical development of mathematical models to understand HIV and HCV infections and the effects of treatment by fitting the models to clinical data. We then focus on recent efforts and contributions of applying these models towards understanding SARS-CoV-2 infection and highlight outstanding questions where modeling can provide crucial insights and help to optimize nonpharmaceutical and pharmaceutical interventions of the coronavirus disease 2019 (COVID-19) pandemic. The review is written from our personal perspective emphasizing the power of simple target cell limited models that provided important insights and then their evolution into more complex models that captured more of the virology and immunology. To quote Albert Einstein, "Everything should be made as simple as possible, but not simpler," and this idea underlies the modeling we describe below.

Indexed as

Models, TheoreticalAnti-Retroviral AgentsCommunicable DiseasesCOVID-19Hepatitis CHIV InfectionsHumansPandemicsSARS-CoV-2Viral LoadAnti-Retroviral Agents

Identifiers

PMID33410134
PMCPMC8142935
OpenAlexW3119992512

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.