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首页> 外文期刊>Zoonoses and Public Health >Model-based prediction of nephropathia epidemica outbreaks based on climatological and vegetation data and bank vole population dynamics.
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Model-based prediction of nephropathia epidemica outbreaks based on climatological and vegetation data and bank vole population dynamics.

机译：基于气候和植被数据以及河岸田鼠种群动态的基于模型的肾病疫情暴发预测。

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摘要

Wildlife-originated zoonotic diseases in general are a major contributor to emerging infectious diseases. Hantaviruses more specifically cause thousands of human disease cases annually worldwide, while understanding and predicting human hantavirus epidemics pose numerous unsolved challenges. Nephropathia epidemica (NE) is a human infection caused by Puumala virus, which is naturally carried and shed by bank voles (Myodes glareolus). The objective of this study was to develop a method that allows model-based predicting 3 months ahead of the occurrence of NE epidemics. Two data sets were utilized to develop and test the models. These data sets were concerned with NE cases in Finland and Belgium. In this study, we selected the most relevant inputs from all the available data for use in a dynamic linear regression (DLR) model. The number of NE cases in Finland were modelled using data from 1996 to 2008. The NE cases were predicted based on the time series data of average monthly air temperature ( degrees C) and bank voles' trapping index using a DLR model. The bank voles' trapping index data were interpolated using a related dynamic harmonic regression model (DHR). Here, the DLR and DHR models used time-varying parameters. Both the DHR and DLR models were based on a unified state-space estimation framework. For the Belgium case, no time series of the bank voles' population dynamics were available. Several studies, however, have suggested that the population of bank voles is related to the variation in seed production of beech and oak trees in Northern Europe. Therefore, the NE occurrence pattern in Belgium was predicted based on a DLR model by using remotely sensed phenology parameters of broad-leaved forests, together with the oak and beech seed categories and average monthly air temperature ( degrees C) using data from 2001 to 2009. Our results suggest that even without any knowledge about hantavirus dynamics in the host population, the time variation in NE outbreaks in Finland could be predicted 3 months ahead with a 34% mean relative prediction error (MRPE). This took into account solely the population dynamics of the carrier species (bank voles). The time series analysis also revealed that climate change, as represented by the vegetation index, changes in forest phenology derived from satellite images and directly measured air temperature, may affect the mechanics of NE transmission. NE outbreaks in Belgium were predicted 3 months ahead with a 40% MRPE, based only on the climatological and vegetation data, in this case, without any knowledge of the bank vole's population dynamics. In this research, we demonstrated that NE outbreaks can be predicted using climate and vegetation data or the bank vole's population dynamics, by using dynamic data-based models with time-varying parameters. Such a predictive modelling approach might be used as a step towards the development of new tools for the prevention of future NE outbreaks.
机译：通常，野生生物引起的人畜共患病是导致新兴传染病的主要因素。更具体地讲，汉坦病毒每年在世界范围内引起数千例人类疾病病例，而了解和预测人汉坦病毒的流行则提出了许多尚未解决的挑战。流行性肾病（NE）是由Puumala病毒引起的人类感染，这种病毒是由岸田鼠（Myodes glareolus）自然携带和排出的。这项研究的目的是开发一种方法，该方法可以在NE流行病发生前3个月进行基于模型的预测。利用两个数据集来开发和测试模型。这些数据集与芬兰和比利时的NE病例有关。在这项研究中，我们从所有可用数据中选择了最相关的输入，以用于动态线性回归（DLR）模型。芬兰的NE病例数是使用1996年至2008年的数据进行建模的。NE病例的预测是使用DLR模型根据月平均气温（℃）和岸田鼠诱捕指数的时间序列数据进行的。使用相关的动态谐波回归模型（DHR）内插河岸田鼠的诱捕指数数据。在这里，DLR和DHR模型使用时变参数。 DHR和DLR模型均基于统一的状态空间估计框架。对于比利时案例，没有银行田鼠种群动态的时间序列。然而，一些研究表明，在北欧，河岸田鼠的数量与山毛榉和橡树种子生产的变化有关。因此，比利时的NE发生模式是根据DLR模型预测的，方法是使用2001-2009年的数据，结合阔叶林的遥感物候参数，橡树和山毛榉种子类别以及平均每月气温（摄氏度），我们的结果表明，即使不了解宿主人群中的汉坦病毒动态，芬兰的NE暴发的时间变化也可以提前3个月预测，平均相对预测误差（MRPE）为34％。这仅考虑了携带者物种（河岸田鼠）的种群动态。时间序列分析还显示，以植被指数为代表的气候变化，从卫星图像获得的森林物候变化以及直接测量的气温可能会影响NE传播的机理。仅在气候和植被数据的情况下，才预测比利时的NE暴发提前3个月，MRPE为40％，在这种情况下，对银行田鼠的种群动态一无所知。在这项研究中，我们证明了可以通过使用具有时变参数的基于动态数据的模型，使用气候和植被数据或河岸田鼠的种群动态来预测NE暴发。这种预测性建模方法可以用作迈向开发新工具的一步，以预防未来的NE爆发。

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