Title: Calibrating COVID-19 SEIR models with time-varying effective contact rates Show Chinese title

Title: 校准具有时变有效接触率的 COVID-19 SEIR 模型

Abstract: We describe the population-based SEIR (susceptible, exposed, infected, removed) model developed by the Irish Epidemiological Modelling Advisory Group (IEMAG), which advises the Irish government on COVID-19 responses. The model assumes a time-varying effective contact rate (equivalently, a time-varying reproduction number) to model the effect of non-pharmaceutical interventions. A crucial technical challenge in applying such models is their accurate calibration to observed data, e.g., to the daily number of confirmed new cases, as the past history of the disease strongly affects predictions of future scenarios. We demonstrate an approach based on inversion of the SEIR equations in conjunction with statistical modelling and spline-fitting of the data, to produce a robust methodology for calibration of a wide class of models of this type. Show Chinese abstract

Abstract: 我们描述由爱尔兰流行病建模咨询小组（IEMAG）开发的基于种群的SEIR（易感、暴露、感染、移除）模型，该小组向爱尔兰政府提供关于新冠疫情应对措施的建议。 该模型假设一个随时间变化的有效接触率（等效于随时间变化的再生数），以模拟非药物干预措施的影响。 在应用此类模型时，一个关键的技术挑战是将其准确校准到观测数据，例如每日确诊新病例数，因为疾病的过去历史对未来情景的预测有显著影响。 我们展示了一种方法，结合SEIR方程的反演、统计建模和数据样条拟合，以产生一种稳健的方法论，用于校准此类模型的广泛类别。