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.

受最近爆发的冠状病毒 (COVID-19) 的启发，我们提出了一种基于时间调制霍克斯过程的流行病时间增长和缓解的随机模型。该模型足够丰富，可以纳入新型冠状病毒的具体特征，捕捉未被发现、无症状和超扩散个体的影响，尤其是考虑到随时间变化的对策和检测工作。然而，它足够简单，可以对流行病的时间演变进行可扩展和高效的计算，并探索假设情景。与传统的隔间模型相比，我们的方法可以更忠实地描述病毒的特定特征，例如分阶段花费的时间分布，这在控制时间尺度（例如，行动限制）与一次感染的生命周期相当。我们将该模型应用于意大利的第一波和第二波 COVID-19，揭示了与政府引入的行动限制相关的几种影响，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。