论文标题
使用潜在标记的霍克斯工艺，年龄分层的流行模型
Age-stratified epidemic model using a latent marked Hawkes process
论文作者
论文摘要
我们扩展了Lamprinakou等人引入的非结构化同质混合流行模型。 [Arxiv：2208.07340]考虑到按年龄段分层的有限种群。我们使用潜在标记的霍克斯过程和报告的汇总感染将实际未观察到的感染对实际的未观察到的感染进行了建模，以此作为由基础霍克斯过程驱动的随机数量。我们应用核密度颗粒滤波器（KDPF）来推断明显的计数过程，每个年龄段的瞬时繁殖数，并在不久的将来预测流行病的未来轨迹；考虑到年龄段和人口规模不会增加计算工作。我们证明了拟议的推理算法在合成数据集上的性能，而Covid-19报告了英国各地当局的案件。我们说明，考虑到年龄的个体异质性降低了估计的不确定性，并提供了对干预措施和行为变化的实时测量。
We extend the unstructured homogeneously mixing epidemic model introduced by Lamprinakou et al. [arXiv:2208.07340] considering a finite population stratified by age bands. We model the actual unobserved infections using a latent marked Hawkes process and the reported aggregated infections as random quantities driven by the underlying Hawkes process. We apply a Kernel Density Particle Filter (KDPF) to infer the marked counting process, the instantaneous reproduction number for each age group and forecast the epidemic's future trajectory in the near future; considering the age bands and the population size does not increase the computational effort. We demonstrate the performance of the proposed inference algorithm on synthetic data sets and COVID-19 reported cases in various local authorities in the UK. We illustrate that taking into account the individual heterogeneity in age decreases the uncertainty of estimates and provides a real-time measurement of interventions and behavioural changes.
