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Model-based assessment of Chikungunya and O’nyong-nyong virus circulation in Mali in a serological cross-reactivity context

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  • Nathanaël Hozé

    (Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Université de Paris, UMR2000, CNRS)

  • Issa Diarra

    (Unité des Virus Émergents, (UVE: Aix-Marseille Univ-IRD 190-INSERM 1207-IHU Méditerranée Infection)
    Malaria Research and Training Center, USTT)

  • Abdoul Karim Sangaré

    (Malaria Research and Training Center, USTT
    Centre d’Infectiologie Charles Mérieux)

  • Boris Pastorino

    (Unité des Virus Émergents, (UVE: Aix-Marseille Univ-IRD 190-INSERM 1207-IHU Méditerranée Infection))

  • Laura Pezzi

    (Unité des Virus Émergents, (UVE: Aix-Marseille Univ-IRD 190-INSERM 1207-IHU Méditerranée Infection)
    EA7310, Laboratoire de Virologie, Université de Corse-Inserm)

  • Bourèma Kouriba

    (Malaria Research and Training Center, USTT
    Centre d’Infectiologie Charles Mérieux)

  • Issaka Sagara

    (Malaria Research and Training Center, USTT)

  • Abdoulaye Dabo

    (Malaria Research and Training Center, USTT)

  • Abdoulaye Djimdé

    (Malaria Research and Training Center, USTT)

  • Mahamadou Ali Thera

    (Malaria Research and Training Center, USTT)

  • Ogobara K. Doumbo
  • Xavier de Lamballerie

    (Unité des Virus Émergents, (UVE: Aix-Marseille Univ-IRD 190-INSERM 1207-IHU Méditerranée Infection))

  • Simon Cauchemez

    (Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Université de Paris, UMR2000, CNRS)

Abstract

Serological surveys are essential to quantify immunity in a population but serological cross-reactivity often impairs estimates of the seroprevalence. Here, we show that modeling helps addressing this key challenge by considering the important cross-reactivity between Chikungunya (CHIKV) and O’nyong-nyong virus (ONNV) as a case study. We develop a statistical model to assess the epidemiology of these viruses in Mali. We additionally calibrate the model with paired virus neutralization titers in the French West Indies, a region with known CHIKV circulation but no ONNV. In Mali, the model estimate of ONNV and CHIKV prevalence is 30% and 13%, respectively, versus 27% and 2% in non-adjusted estimates. While a CHIKV infection induces an ONNV response in 80% of cases, an ONNV infection leads to a cross-reactive CHIKV response in only 22% of cases. Our study shows the importance of conducting serological assays on multiple cross-reactive pathogens to estimate levels of virus circulation.

Suggested Citation

  • 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.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-26707-9
    DOI: 10.1038/s41467-021-26707-9
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    References listed on IDEAS

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    1. 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.
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