Evidence map›Paper›PMID 31253823›Full record

ArticleScientific reports2019

Cross-serotype interactions and disease outcome prediction of dengue infections in Vietnam.

R Aguas, I Dorigatti, L Coudeville, C Luxemburger, N M Ferguson

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.

0numbers the graph read from it
0cells of the map it votes in
37citing papers in PubMed
6.0field-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

37 citing papers in PubMed, 208 citations in OpenAlex.

  1. Trial
  2. Article
  3. Review
  4. Dengue Dilemma: Navigating Cross-Reactivity and Immune Challenges.Current topics in microbiology and immunology · 2026
    Review
  5. Dengue and severe dengue.Clinical microbiology reviews · 2025
    Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Dengue.Lancet (London, England) · 2024
    Review
  14. Article
  15. Review
  16. Article
  17. Article
  18. Article
  19. Article
  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

5 authors at 3 institutions in 4 countries.

R AguasMRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, Faculty of Medicine, Norfolk Place, London, W2 1PG, UK. r.aguas@imperial.ac.uk.
I DorigattiMRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, Faculty of Medicine, Norfolk Place, London, W2 1PG, UK.ORCID http://orcid.org/0000-0001-9959-0706
L CoudevilleSanofi Pasteur, 14 espace Henry Vallée, 69007, Lyon, France.
C LuxemburgerSanofi Pasteur, 14 espace Henry Vallée, 69007, Lyon, France.
N M FergusonMRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, Faculty of Medicine, Norfolk Place, London, W2 1PG, UK.ORCID http://orcid.org/0000-0002-1154-8093
Imperial College London · GBSanofi (France) · FRMahidol University · TH

Funding

Medical Research Council MR/R015600/1
6 · The paper itself

Abstract

Dengue pathogenesis is extremely complex. Dengue infections are thought to induce life-long immunity from homologous challenges as well as a multi-factorial heterologous risk enhancement. Here, we use the data collected from a prospective cohort study of dengue infections in schoolchildren in Vietnam to disentangle how serotype interactions modulate clinical disease risk in the year following serum collection. We use multinomial logistic regression to correlate the yearly neutralizing antibody measurements obtained with each infecting serotype in all dengue clinical cases collected over the course of 6 years (2004-2009). This allowed us to extrapolate a fully discretised matrix of serotype interactions, revealing clear signals of increased risk of clinical illness in individuals primed with a previous dengue infection. The sequences of infections which produced a higher risk of dengue fever upon secondary infection are: DEN1 followed by DEN2; DEN1 followed by DEN4; DEN2 followed by DEN3; and DEN4 followed by DEN3. We also used this longitudinal data to train a machine learning algorithm on antibody titre differences between consecutive years to unveil asymptomatic dengue infections and estimate asymptomatic infection to clinical case ratios over time, allowing for a better characterisation of the population's past exposure to different serotypes.

Indexed as

AlgorithmsAntibodies, NeutralizingAntibodies, ViralCross ReactionsDengueDengue VirusFemaleHumansMaleModels, TheoreticalOdds RatioPopulation SurveillanceSerogroupVietnamAntibodies, NeutralizingAntibodies, Viral

Identifiers

PMID31253823
PMCPMC6598999
OpenAlexW2956118262

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.