Department of Biology, Georgetown University, Washington, DC.
Fogarty International Center, National Institutes of Health, Bethesda, Maryland.
Am J Epidemiol. 2018 Dec 1;187(12):2550-2560. doi: 10.1093/aje/kwy209.
The factors that drive spatial heterogeneity and diffusion of pandemic influenza remain debated. We characterized the spatiotemporal mortality patterns of the 1918 influenza pandemic in British India and studied the role of demographic factors, environmental variables, and mobility processes on the observed patterns of spread. Fever-related and all-cause excess mortality data across 206 districts in India from January 1916 to December 1920 were analyzed while controlling for variation in seasonality particular to India. Aspects of the 1918 autumn wave in India matched signature features of influenza pandemics, with high disease burden among young adults, (moderate) spatial heterogeneity in burden, and highly synchronized outbreaks across the country deviating from annual seasonality. Importantly, we found population density and rainfall explained the spatial variation in excess mortality, and long-distance travel via railroad was predictive of the observed spatial diffusion of disease. A spatiotemporal analysis of mortality patterns during the 1918 influenza pandemic in India was integrated in this study with data on underlying factors and processes to reveal transmission mechanisms in a large, intensely connected setting with significant climatic variability. The characterization of such heterogeneity during historical pandemics is crucial to prepare for future pandemics.
驱动大流行性流感空间异质性和扩散的因素仍存在争议。我们描述了 1918 年流感大流行在英属印度的时空死亡模式，并研究了人口因素、环境变量和流动过程对所观察到的传播模式的作用。我们分析了 1916 年 1 月至 1920 年 12 月期间印度 206 个地区的发热相关和全因超额死亡率数据，同时控制了印度特有的季节性变化。印度 1918 年秋季流感的各个方面都符合流感大流行的特征，年轻人的疾病负担很高，（中度）负担的空间异质性很大，全国各地的爆发高度同步，偏离了年度季节性。重要的是，我们发现人口密度和降雨量解释了超额死亡率的空间变化，而通过铁路的长途旅行则预测了疾病的实际空间扩散。本研究将印度 1918 年流感大流行期间的死亡率模式时空分析与潜在因素和过程的数据相结合，揭示了在一个具有显著气候变异性的大型、紧密连接的环境中的传播机制。在历史大流行中对这种异质性进行特征描述对于为未来的大流行做准备至关重要。
