论文标题
拟合流行流动模型的边际力矩匹配方法，以减少疾病监测计数
A marginal moment matching approach for fitting endemic-epidemic models to underreported disease surveillance counts
论文作者
论文摘要
计数数据通常会导致不足，尤其是在传染病监测中。我们提出了一种近似最大似然方法，以适合从流行流行类别到报告数据的时间序列模型。该方法基于边缘力矩匹配，其中通过同一类完全观察到的过程近似报道的过程。此外，分析了通过乘法因子忽略或考虑到偏见时的偏差形式。值得注意的是，我们表明，这导致了对有效生殖数的基于模型的估计值的下降偏见。边缘力矩匹配方法也可以用来解释比疾病平均串行间隔更长的报告间隔。在模拟研究中证明了所提出的方法的良好表现。在德国柏林的每周轮状病毒胃肠炎计数的案例研究中，讨论并应用了时间变化参数和报告概率的扩展。
Count data are often subject to underreporting, especially in infectious disease surveillance. We propose an approximate maximum likelihood method to fit count time series models from the endemic-epidemic class to underreported data. The approach is based on marginal moment matching where underreported processes are approximated through completely observed processes from the same class. Moreover, the form of the bias when underreporting is ignored or taken into account via multiplication factors is analysed. Notably, we show that this leads to a downward bias in model-based estimates of the effective reproductive number. A marginal moment matching approach can also be used to account for reporting intervals which are longer than the mean serial interval of a disease. The good performance of the proposed methodology is demonstrated in simulation studies. An extension to time-varying parameters and reporting probabilities is discussed and applied in a case study on weekly rotavirus gastroenteritis counts in Berlin, Germany.
