Garetto Michele, Leonardi Emilio, Torrisi Giovanni Luca
Università degli Studi di Torino, C.so Svizzera 185, Torino, Italy.
Politecnico di Torino, C.so Duca degli Abruzzi 24, Torino, Italy.
Annu Rev Control. 2021;51:551-563. doi: 10.1016/j.arcontrol.2021.02.002. Epub 2021 Mar 12.
Motivated by the recent outbreak of coronavirus (COVID-19), we propose a stochastic model of epidemic temporal growth and mitigation based on a time-modulated Hawkes process. The model is sufficiently rich to incorporate specific characteristics of the novel coronavirus, to capture the impact of undetected, asymptomatic and super-diffusive individuals, and especially to take into account time-varying counter-measures and detection efforts. Yet, it is simple enough to allow scalable and efficient computation of the temporal evolution of the epidemic, and exploration of what-if scenarios. Compared to traditional compartmental models, our approach allows a more faithful description of virus specific features, such as distributions for the time spent in stages, which is crucial when the time-scale of control (e.g., mobility restrictions) is comparable to the lifetime of a single infection. We apply the model to the first and second wave of COVID-19 in Italy, shedding light onto several effects related to mobility restrictions introduced by the government, and to the effectiveness of contact tracing and mass testing performed by the national health service.
受近期冠状病毒（COVID-19）疫情爆发的启发，我们基于时间调制的霍克斯过程提出了一种疫情时间增长和缓解的随机模型。该模型足够丰富，能够纳入新型冠状病毒的特定特征，捕捉未检测到的、无症状的和超扩散个体的影响，尤其能够考虑随时间变化的应对措施和检测工作。然而，它又足够简单，能够对疫情的时间演变进行可扩展且高效的计算，并探索假设情景。与传统的 compartmental 模型相比，我们的方法能够更如实地描述病毒的特定特征，例如在各阶段所花费时间的分布，当控制时间尺度（例如行动限制）与单次感染的持续时间相当时，这一点至关重要。我们将该模型应用于意大利 COVID-19 的第一波和第二波疫情，揭示了与政府实施的行动限制以及国家卫生服务部门进行的接触者追踪和大规模检测的有效性相关的若干影响。
