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Nature. 2018 May;557(7707):719-723. doi: 10.1038/s41586-018-0157-4. Epub 2018 May 23.

Reconstruction of antibody dynamics and infection histories to evaluate dengue risk.

Author information

1
Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Paris, France. henrik.salje@pasteur.fr.
2
CNRS UMR2000, Génomique évolutive, modélisation et santé (GEMS), Institut Pasteur, Paris, France. henrik.salje@pasteur.fr.
3
Center of Bioinformatics, Biostatistics and Integrative Biology, Institut Pasteur, Paris, France. henrik.salje@pasteur.fr.
4
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA. henrik.salje@pasteur.fr.
5
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.
6
Department of Biology, University of Florida, Gainesville, FL, USA.
7
Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA.
8
University of California, San Francisco, San Francisco, CA, USA.
9
Department of Virology, Armed Forces Research Institute of Medical Sciences, Bangkok, Thailand.
10
International Vaccine Institute, Seoul, South Korea.
11
Viral Diseases Branch, Walter Reed Army Institute of Research, Silver Spring, MD, USA.
12
Department of Medicine, Upstate Medical University of New York, Syracuse, NY, USA.
13
Institute for Immunology and Informatics, Department of Cell and Molecular Biology, University of Rhode Island, Providence, RI, USA.
14
Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Paris, France.
15
CNRS UMR2000, Génomique évolutive, modélisation et santé (GEMS), Institut Pasteur, Paris, France.
16
Center of Bioinformatics, Biostatistics and Integrative Biology, Institut Pasteur, Paris, France.

Abstract

As with many pathogens, most dengue infections are subclinical and therefore unobserved 1 . Coupled with limited understanding of the dynamic behaviour of potential serological markers of infection, this observational problem has wide-ranging implications, including hampering our understanding of individual- and population-level correlates of infection and disease risk and how these change over time, between assay interpretations and with cohort design. Here we develop a framework that simultaneously characterizes antibody dynamics and identifies subclinical infections via Bayesian augmentation from detailed cohort data (3,451 individuals with blood draws every 91 days, 143,548 haemagglutination inhibition assay titre measurements)2,3. We identify 1,149 infections (95% confidence interval, 1,135-1,163) that were not detected by active surveillance and estimate that 65% of infections are subclinical. After infection, individuals develop a stable set point antibody load after one year that places them within or outside a risk window. Individuals with pre-existing titres of ≤1:40 develop haemorrhagic fever 7.4 (95% confidence interval, 2.5-8.2) times more often than naive individuals compared to 0.0 times for individuals with titres >1:40 (95% confidence interval: 0.0-1.3). Plaque reduction neutralization test titres ≤1:100 were similarly associated with severe disease. Across the population, variability in the size of epidemics results in large-scale temporal changes in infection and disease risk that correlate poorly with age.

Comment in

PMID:
29795354
PMCID:
PMC6064976
DOI:
10.1038/s41586-018-0157-4
[Indexed for MEDLINE]
Free PMC Article

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