Department of Mathematics and Statistics, University of Jyväskylä, Jyväskylä, Finland.
INVEST Research Flagship Centre, University of Turku, Turku, Finland.
PeerJ. 2024 Sep 26;12:e18155. doi: 10.7717/peerj.18155. eCollection 2024.
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, which could be attributed to immunosuppressive effects or, for example, the general weakening of the population due to recurring infections or poor nutritional conditions.
已知感染会相互作用，因为先前的感染可能会影响对新感染的易感性。共动力学可以通过免疫抑制或调节、共同的环境或气候驱动因素、或对易感宿主的竞争来介导。流行病学中的研究和统计方法通常集中在大型汇总数据集或来自城市的高质量数据上，而农村地区在文献中代表性不足。考虑到农村人口的数据通常很少且稀缺，尤其是在历史数据源的情况下，这可能会带来相当大的方法学挑战。为了克服由于此类数据带来的许多障碍，我们提出了一种用于疾病共动力学的通用贝叶斯时空模型。我们将所提出的模型应用于历史（1820-1850 年）芬兰教区登记数据，研究了前医疗保健时期芬兰传染病的传播。我们观察到麻疹、百日咳和天花表现出正相关的动态，这可能归因于免疫抑制作用，或者例如由于反复感染或营养不良导致人口普遍减弱。
