AI Article Synopsis
- Serological surveys help measure immunity in populations, but cross-reactivity between viruses can make it hard to get accurate prevalence estimates.
- In this study, researchers focused on Chikungunya (CHIKV) and O'nyong-nyong virus (ONNV) in Mali, using a statistical model to better understand their epidemiology.
- The model showed higher estimates of ONNV and CHIKV prevalence (30% and 13%, respectively) compared to non-adjusted estimates, highlighting the need to consider cross-reactivity among different pathogens in serological assays for more accurate results.
Article 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.
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Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8602252 | PMC |
| http://dx.doi.org/10.1038/s41467-021-26707-9 | DOI Listing |
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