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Time series non-Gaussian Bayesian bivariate model applied to data on HMPV and RSV: a case of Dadaab in Kenya

机译:时间序列非高斯贝叶斯二元模型应用于HMPV和RSV数据:肯尼亚Dadaab案

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摘要

BackgroundHuman metapneumovirus (HMPV) have similar symptoms to those caused by the respiratory syncytial virus (RSV). The modes of transmission and dynamics of time series data still remain poorly understood. Climatic factors have long been suspected to be implicated in impacting on the number of cases for these epidemics. Currently, only a few models satisfactorily capture the dynamics of time series data of these two viruses. Our objective was to assess the presence of influence of high incidences between the viruses and to ascertain whether higher incidences of one virus are influenced by the other.
机译:背景人类偏肺病毒(HMPV)与由呼吸道合胞病毒(RSV)引起的症状相似。时间序列数据的传输方式和动态方式仍然知之甚少。长期以来,人们一直怀疑气候因素会影响这些流行病的病例数。当前,只有很少的模型能够令人满意地捕获这两种病毒的时间序列数据的动态。我们的目标是评估病毒之间高发事件的影响,并确定一种病毒的高发事件是否受到另一种病毒的影响。

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