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Reconstruction of antibody dynamics and infection histories to evaluate dengue risk

Author

Listed:
  • Henrik Salje

    (Institut Pasteur
    Institut Pasteur
    Institut Pasteur
    Johns Hopkins University)

  • Derek A. T. Cummings

    (Johns Hopkins University
    University of Florida
    University of Florida)

  • Isabel Rodriguez-Barraquer

    (University of California, San Francisco)

  • Leah C. Katzelnick

    (University of Florida)

  • Justin Lessler

    (Johns Hopkins University)

  • Chonticha Klungthong

    (Armed Forces Research Institute of Medical Sciences)

  • Butsaya Thaisomboonsuk

    (Armed Forces Research Institute of Medical Sciences)

  • Ananda Nisalak

    (Armed Forces Research Institute of Medical Sciences)

  • Alden Weg

    (Armed Forces Research Institute of Medical Sciences)

  • Damon Ellison

    (Armed Forces Research Institute of Medical Sciences)

  • Louis Macareo

    (Armed Forces Research Institute of Medical Sciences)

  • In-Kyu Yoon

    (International Vaccine Institute)

  • Richard Jarman

    (Walter Reed Army Institute of Research)

  • Stephen Thomas

    (Upstate Medical University of New York)

  • Alan L. Rothman

    (University of Rhode Island)

  • Timothy Endy

    (Upstate Medical University of New York)

  • Simon Cauchemez

    (Institut Pasteur
    Institut Pasteur
    Institut Pasteur)

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.

Suggested Citation

  • Henrik Salje & Derek A. T. Cummings & Isabel Rodriguez-Barraquer & Leah C. Katzelnick & Justin Lessler & Chonticha Klungthong & Butsaya Thaisomboonsuk & Ananda Nisalak & Alden Weg & Damon Ellison & Lo, 2018. "Reconstruction of antibody dynamics and infection histories to evaluate dengue risk," Nature, Nature, vol. 557(7707), pages 719-723, May.
  • Handle: RePEc:nat:nature:v:557:y:2018:i:7707:d:10.1038_s41586-018-0157-4
    DOI: 10.1038/s41586-018-0157-4
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    Cited by:

    1. Tim K. Tsang & Ranawaka A. P. M. Perera & Vicky J. Fang & Jessica Y. Wong & Eunice Y. Shiu & Hau Chi So & Dennis K. M. Ip & J. S. Malik Peiris & Gabriel M. Leung & Benjamin J. Cowling & Simon Caucheme, 2022. "Reconstructing antibody dynamics to estimate the risk of influenza virus infection," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    2. Rúbens Prince dos Santos Alves & Julia Timis & Robyn Miller & Kristen Valentine & Paolla Beatriz Almeida Pinto & Andrew Gonzalez & Jose Angel Regla-Nava & Erin Maule & Michael N. Nguyen & Norazizah Sh, 2024. "Human coronavirus OC43-elicited CD4+ T cells protect against SARS-CoV-2 in HLA transgenic mice," Nature Communications, Nature, vol. 15(1), pages 1-20, December.
    3. Ellen Young & Boyd Yount & Petraleigh Pantoja & Sandra Henein & Rita M. Meganck & Jennifer McBride & Jennifer E. Munt & Thomas J. Baric & Deanna Zhu & Trevor Scobey & Stephanie Dong & Longping V. Tse , 2023. "A live dengue virus vaccine carrying a chimeric envelope glycoprotein elicits dual DENV2-DENV4 serotype-specific immunity," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    4. Nathanaël Hozé & Issa Diarra & Abdoul Karim Sangaré & Boris Pastorino & Laura Pezzi & Bourèma Kouriba & Issaka Sagara & Abdoulaye Dabo & Abdoulaye Djimdé & Mahamadou Ali Thera & Ogobara K. Doumbo & Xa, 2021. "Model-based assessment of Chikungunya and O’nyong-nyong virus circulation in Mali in a serological cross-reactivity context," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
    5. Hay, James & Routledge, Isobel & Takahashi, Saki, 2023. "Serodynamics: a review of methods for epidemiological inference using serological data," OSF Preprints kqdsn, Center for Open Science.
    6. Ruopeng Xie & Kimberly M. Edwards & Dillon C. Adam & Kathy S. M. Leung & Tim K. Tsang & Shreya Gurung & Weijia Xiong & Xiaoman Wei & Daisy Y. M. Ng & Gigi Y. Z. Liu & Pavithra Krishnan & Lydia D. J. C, 2023. "Resurgence of Omicron BA.2 in SARS-CoV-2 infection-naive Hong Kong," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    7. Sandra Bos & Aaron L. Graber & Jaime A. Cardona-Ospina & Elias M. Duarte & Jose Victor Zambrana & Jorge A. Ruíz Salinas & Reinaldo Mercado-Hernandez & Tulika Singh & Leah C. Katzelnick & Aravinda Silv, 2024. "Protection against symptomatic dengue infection by neutralizing antibodies varies by infection history and infecting serotype," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    8. Yun Lin & Bingyi Yang & Sarah Cobey & Eric H. Y. Lau & Dillon C. Adam & Jessica Y. Wong & Helen S. Bond & Justin K. Cheung & Faith Ho & Huizhi Gao & Sheikh Taslim Ali & Nancy H. L. Leung & Tim K. Tsan, 2022. "Incorporating temporal distribution of population-level viral load enables real-time estimation of COVID-19 transmission," Nature Communications, Nature, vol. 13(1), pages 1-8, December.

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