Evidence map›Paper›PMID 27855153›Full record

ArticlePLoS computational biology2016

Drivers of Inter-individual Variation in Dengue Viral Load Dynamics.

Rotem Ben-Shachar, Scott Schmidler, Katia Koelle

Open access · goldAbstract read
In one paragraph

Article in PLoS computational biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed, 1 pooled it
5.4field-weighted citation impact, top 4% 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

32 citing papers in PubMed, 1 synthesis or guideline pooled it, 56 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Review
  4. Dengue Dilemma: Navigating Cross-Reactivity and Immune Challenges.Current topics in microbiology and immunology · 2026
    Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Molecular Communications in Viral Infections Research: Modeling, Experimental Data, and Future Directions.IEEE transactions on molecular, biological, and multi-scale communications · 2021
    Article
  18. Article
  19. Increased TNF-Journal of immunology research · 2021
    Observational
  20. Article
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

3 authors at 1 institution in 1 country.

Rotem Ben-ShacharProgram in Computational Biology and Bioinformatics, Duke University, Durham, North Carolina, United States of America.
Scott SchmidlerProgram in Computational Biology and Bioinformatics, Duke University, Durham, North Carolina, United States of America.
Katia KoelleDepartment of Biology, Duke University, Durham, North Carolina, United States of America.
Duke University · US

Funding

Understanding Transmission with Integrated Genetic and Epidemiologic InferenceU54GM111274 · NIGMS · FRED HUTCHINSON CANCER RESEARCH CENTER · PI VESPIGNANI, ALESSANDRO · 2014 to 2018
$12.5M
NIGMS NIH HHS U54 GM111274
6 · The paper itself

Abstract

Dengue is a vector-borne viral disease of humans that endemically circulates in many tropical and subtropical regions worldwide. Infection with dengue can result in a range of disease outcomes. A considerable amount of research has sought to improve our understanding of this variation in disease outcomes and to identify predictors of severe disease. Contributing to this research, patterns of viral load in dengue infected patients have been quantified, with analyses indicating that peak viral load levels, rates of viral load decline, and time to peak viremia are useful predictors of severe disease. Here, we take a complementary approach to understanding patterns of clinical manifestation and inter-individual variation in viral load dynamics. Specifically, we statistically fit mathematical within-host models of dengue to individual-level viral load data to test virological and immunological hypotheses explaining inter-individual variation in dengue viral load. We choose between alternative models using model selection criteria to determine which hypotheses are best supported by the data. We first show that the cellular immune response plays an important role in regulating viral load in secondary dengue infections. We then provide statistical support for the process of antibody-dependent enhancement (but not original antigenic sin) in the development of severe disease in secondary dengue infections. Finally, we show statistical support for serotype-specific differences in viral infectivity rates, with infectivity rates of dengue serotypes 2 and 3 exceeding those of serotype 1. These results contribute to our understanding of dengue viral load patterns and their relationship to the development of severe dengue disease. They further have implications for understanding how dengue transmissibility may depend on the immune status of infected individuals and the identity of the infecting serotype.

Indexed as

Models, StatisticalAdolescentAdultComputer SimulationDengueDengue VirusFemaleHumansMaleMiddle AgedPrevalenceReproducibility of ResultsRisk FactorsSensitivity and SpecificitySpecies SpecificityVietnam

Identifiers

PMID27855153
PMCPMC5113863
OpenAlexW2549787299

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.