Department of Ecology and Evolution, University of Chicago, Chicago, IL 60637.
Department of Science, Technology, and Society, York University, Toronto, ON M3J 1P3, Canada.
Proc Natl Acad Sci U S A. 2024 Sep 3;121(36):e2318704121. doi: 10.1073/pnas.2318704121. Epub 2024 Aug 27.
The incidence of dengue virus disease has increased globally across the past half-century, with highest number of cases ever reported in 2019 and again in 2023. We analyzed climatological, epidemiological, and phylogenomic data to investigate drivers of two decades of dengue in Cambodia, an understudied endemic setting. Using epidemiological models fit to a 19-y dataset, we first demonstrate that climate-driven transmission alone is insufficient to explain three epidemics across the time series. We then use wavelet decomposition to highlight enhanced annual and multiannual synchronicity in dengue cycles between provinces in epidemic years, suggesting a role for climate in homogenizing dynamics across space and time. Assuming reported cases correspond to symptomatic secondary infections, we next use an age-structured catalytic model to estimate a declining force of infection for dengue through time, which elevates the mean age of reported cases in Cambodia. Reported cases in >70-y-old individuals in the 2019 epidemic are best explained when also allowing for waning multitypic immunity and repeat symptomatic infections in older patients. We support this work with phylogenetic analysis of 192 dengue virus (DENV) genomes that we sequenced between 2019 and 2022, which document emergence of DENV-2 Cosmopolitan Genotype-II into Cambodia. This lineage demonstrates phylogenetic homogeneity across wide geographic areas, consistent with invasion behavior and in contrast to high phylogenetic diversity exhibited by endemic DENV-1. Finally, we simulate an age-structured, mechanistic model of dengue dynamics to demonstrate how expansion of an antigenically distinct lineage that evades preexisting multitypic immunity effectively reproduces the older-age infections witnessed in our data.
在过去的半个世纪里,全球登革热病毒病的发病率一直在增加,2019 年和 2023 年报告的病例数创历史新高。我们分析了气候、流行病学和系统发育基因组学数据,以研究柬埔寨二十年登革热的驱动因素,柬埔寨是一个研究不足的地方性流行地区。我们使用拟合了 19 年数据集的流行病学模型,首先证明仅由气候驱动的传播不足以解释整个时间序列中的三次流行。然后,我们使用小波分解突出了流行年份省内登革热周期的年度和多年同步性增强,表明气候在空间和时间上使动态同质化发挥作用。假设报告的病例对应于有症状的二次感染,我们接下来使用年龄结构催化模型来估计登革热的感染率随时间下降,这会提高柬埔寨报告病例的平均年龄。当允许多价免疫减弱和老年患者再次出现有症状感染时,2019 年流行中报告的>70 岁个体中的病例可以得到最佳解释。我们通过在 2019 年至 2022 年之间测序的 192 个登革热病毒 (DENV) 基因组的系统发育分析支持这项工作,该分析记录了 DENV-2 普遍基因型-II 进入柬埔寨。该谱系在广泛的地理区域内表现出系统发育同质性,与入侵行为一致,与地方性 DENV-1 表现出的高度系统发育多样性形成对比。最后,我们模拟了一个年龄结构的、机制性的登革热动态模型,以证明逃避预先存在的多价免疫的抗原上不同的谱系的扩展如何有效地再现我们数据中观察到的老年感染。