Nitsch Aïda, Lummaa Virpi, Ketola Tarmo, Honkola Terhi, Vesakoski Outi, Briga Michael
Department of Biology, University of Turku, Vesilinnantie 5, 20014 Turku, Finland.
Department of Forest Sciences, University of Helsinki, P.O. Box 24, 00014 Helsinki, Finland.
iScience. 2025 Apr 26;28(6):112530. doi: 10.1016/j.isci.2025.112530. eCollection 2025 Jun 20.
Infections spreading from host to host are a burden of social lifestyle mostly documented at the local scale (within groups). The influence of social structure at a broader scale (e.g., between groups or regions) on infectious disease dynamics is less understood partly due to the difficulty to identify the relevant social groups at this scale. Dialect groups encompass long-held human contacts and could indicate social groups relevant to infections. Using nationwide individual-level mortality records from pre-industrial Finland (1800-1850), we investigated which social grouping best predicted spatial variation in smallpox, pertussis, and measles mortality by comparing models with no regional information, administrative regions, and dialect groups. Dialect groups explained spatial variation of pertussis, administrative regions for smallpox, while measles showed no broader scale spatial variation. These results highlight the complex spatial structuring of infectious diseases and stress the need for studies to identify the relevant social structure.
从一个宿主传播到另一个宿主的感染是社会生活方式的一种负担，大多在地方层面（群体内部）有记录。社会结构在更广泛层面（例如群体之间或地区之间）对传染病动态的影响了解较少，部分原因是难以在这个层面识别相关社会群体。方言群体包含长期的人际接触，可能表明与感染相关的社会群体。利用芬兰工业化前（1800 - 1850年）全国范围的个人层面死亡率记录，我们通过比较没有区域信息的模型、行政区模型和方言群体模型，研究了哪种社会分组最能预测天花、百日咳和麻疹死亡率的空间变异。方言群体解释了百日咳的空间变异，行政区解释了天花的空间变异，而麻疹没有表现出更广泛层面的空间变异。这些结果凸显了传染病复杂的空间结构，并强调了开展研究以识别相关社会结构的必要性。
