AI 中文总结
本研究针对稀疏计数自回归模型拟合的有偏问题，提出序贯重要性采样方法修正误设模型，经模拟验证有效，并成功应用于德法两国的疫情曲线重建。
AI 中文摘要
稀疏计数自回归模型因灵活性和可解释性，被广泛用于传染病监测数据建模，但该模型拟合难度大，因其涉及高维且序列相关的整数型未知量。一种解决方案是构建类似的连续型替代模型，其值事后映射为整数；但此过程会产生有偏估计，因为推断使用的是替代模型的样本，而非稀疏计数自回归模型本身。本研究提出序贯重要性采样流程以修正该误设模型，通过模拟研究验证其有效性，并将其应用于德国轮状病毒数据和法国脑膜炎球菌数据的疫情曲线重建，证明其适用性。
英文摘要
Thinned count autoregressions are popular for modelling infectious disease surveillance data due to their flexibility and interpretability. However, such a model is challenging to fit since it involves high-dimensional and serially correlated integer-valued unknowns. One solution is to consider an analogous continuous-valued surrogate model whose values are post-hoc mapped to integers. This procedure produces biased estimates as inference is performed using samples from such a surrogate model and not the thinned count autoregression itself. In this work, we propose a sequential importance sampling procedure to correct this misspecified model. We demonstrate its validity in a simulation study and its applicability for epidemic curve reconstruction using rotavirus data from Germany and meningococcus data from France.
