AI Insight
Researchers developed a new mathematical modeling framework to analyze dengue antibody data from cross-sectional blood surveys in Vietnam (2013-2017), enabling them to determine which of the four dengue serotypes infected people and how antibodies from one serotype react to others. The model successfully estimated serotype-specific transmission rates and revealed asymmetric cross-reactivity patterns between serotypes, showing that susceptibility to secondary dengue infection peaked around age 10 and affected approximately 25-30% of individuals aged 1-30 years depending on serotype. This approach extracts significantly more epidemiological information from standard blood surveys than previously possible, even reconstructing individual infection histories despite complex antibody cross-reactions.
Why it matters
This method allows public health authorities to better assess dengue risk, estimate disease burden, and plan vaccination strategies using readily available cross-sectional blood survey data, without requiring expensive and difficult-to-collect longitudinal studies that track individuals over time. The ability to identify which serotypes are circulating and who is susceptible to severe secondary infections is crucial for targeting dengue control efforts in endemic regions.
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⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Dengue remains a growing global health concern, with four co-circulating serotypes (DENV-1 to DENV-4). Cross-sectional serosurveys are essential for inferring past transmission and population immunity, but serotype-specific interpretation is constrained by antibody cross-reactivity across serotypes. Here, we developed a novel modelling framework to jointly infer serotype-specific force of infection (FOI) and cross-reactivity from cross-sectional serosurveys. The model is flexible to data granularity, functioning with binary serostatus alone or combined with quantitative titres. In simulations, the model effectively recovered FOI and cross-reactivity across varying endemicity, sampling designs, and sample age coverage. Applied to annual cross-sectional serosurveys in Vietnam (2013-2017), the model estimated serotype-specific FOI patterns comparable in overall magnitude to analysis requiring supplementary longitudinal post-infection antibody measurements, which are difficult to collect. We further identified higher DENV-3 transmission intensity than previously estimated. Our estimates revealed asymmetric cross-reactivity between infecting and heterologous antibody-response serotypes after primary infections, and high cross-reactivity after post-primary infections. We reconstructed population immune profiles by age, time, serotype, and past infection number, resolving single-serotype and multi-serotype exposure histories not directly distinguishable from observed seroprevalence. Our estimates showed that susceptibility to secondary infection in Vietnam peaked at approximately age 10 for each serotype, and averaged ~25% for DENV-1 to DENV-3 and ~30% for DENV-4 among individuals aged 1-30 years. Additionally, incorporating titres enabled individual-level infection-history inference and revealed exposure-history signals in titre profiles despite extensive cross-reactivity. These findings show that improved modelling can substantially expand the information recoverable from cross-sectional serology, strengthening the public-health utility of serosurveys for risk assessment, burden estimation, and vaccination planning.