Infections are known to interact as previous infections may have an effect on risk of succumbing to a new infection. The co-dynamics can be mediated by immunosuppression or -modulation, shared environmental or climatic drivers, or competition for susceptible hosts. Research and statistical methods in epidemiology often concentrate on large pooled datasets, or high quality data from cities, leaving rural areas underrepresented in literature. Data considering rural populations are typically sparse and scarce, especially in the case of historical data sources, which may introduce considerable methodological challenges. In order to overcome many obstacles due to such data, we present a general Bayesian spatio-temporal model for disease co-dynamics. Applying the proposed model on historical (1820-1850) Finnish parish register data, we study the spread of infectious diseases in pre-healthcare Finland. We observe that measles, pertussis and smallpox exhibit positively correlated dynamics such that any new infection increased mortality in all three diseases, indicating possibly general immunosuppressive effects at population level.

翻译：已知感染之间存在相互作用，既往感染可能影响新发感染的易感性风险。这种共动态可能由免疫抑制或免疫调节、共同的环境或气候驱动因素，或对易感宿主的竞争所介导。流行病学的研究与统计方法通常集中于大型合并数据集或城市高质量数据，导致农村地区在文献中代表性不足。涉及农村人口的数据通常稀少且稀缺，尤其在历史数据源的情况下，这可能带来显著的方法学挑战。为克服此类数据造成的诸多障碍，我们提出了一种适用于疾病共动态的通用贝叶斯时空模型。将该模型应用于历史时期（1820-1850年）芬兰教区登记数据，我们研究了前医疗时代芬兰传染病的传播过程。我们观察到麻疹、百日咳和天花呈现出正相关动态，即任何新发感染均会导致三种疾病的死亡率上升，表明在人群水平上可能存在普遍的免疫抑制效应。