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THE STRUCTURAL IDENTIFIABILITY OF A GENERAL EPIDEMIC (SIR) MODEL WITH SEASONAL FORCING

机译:季节性强制通用疫情(SIR)模型的结构可识别性

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In this paper it is shown that a general SIR epidemic model, with the force of infection subject to seasonal variation, and a proportion of the number of infectives measured, is unidentifiable. This means that an uncountable number of different parameter vectors can, theoretically, give rise to the same idealised output data. Any subsequent parameter estimation from real data must be viewed with less confidence as a result. The approach is essentially that developed by Evans et al. (2002), with modifications to allow for time-variation in the effective contact rate. This approach utilises the existence of an infinitely differentiable transformation that connects the state trajectories corresponding to parameter vectors that give rise to identical output data.
机译:在本文中,显示了一般的SIR疫情模型,具有季节性变异的感染力,并且测量的感染率的比例是无法识别的。这意味着理论上,不可数数量的不同参数向量可以产生相同的理想化输出数据。结果必须在实际数据的任何后续参数估计,因此必须较少的置信度。该方法基本上由Evans等人开发。 (2002),修改以允许有效接触率的时间变化。这种方法利用了一个无限可分子的变换,该变换连接到对应于参数向量的状态轨迹,其产生相同的输出数据。

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