Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Université de Paris, UMR2000, CNRS, Paris, France.
Unité des Virus Émergents, (UVE: Aix-Marseille Univ-IRD 190-INSERM 1207-IHU Méditerranée Infection), Marseille, France.
Nat Commun. 2021 Nov 18;12(1):6735. doi: 10.1038/s41467-021-26707-9.
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.
血清学调查对于定量评估人群中的免疫情况至关重要，但血清学交叉反应常常会影响血清流行率的估计。在这里，我们通过将基孔肯雅热病毒（CHIKV）和奥尼昂-奥尼昂病毒（ONNV）之间的重要交叉反应性作为案例研究，展示了建模有助于解决这一关键挑战。我们开发了一个统计模型来评估这些病毒在马里的流行病学情况。我们还使用法属西印度群岛的配对病毒中和滴度对模型进行了校准，该地区有已知的 CHIKV 传播，但没有 ONNV。在马里，模型估计的 ONNV 和 CHIKV 流行率分别为 30%和 13%，而未经调整的估计值分别为 27%和 2%。虽然 CHIKV 感染会导致 80%的病例产生 ONNV 反应，但 ONNV 感染只会导致 22%的病例产生交叉反应性 CHIKV 反应。我们的研究表明，对多个具有交叉反应性的病原体进行血清学检测对于估计病毒传播水平非常重要。
