论文标题
不完美的流行模型的有效校准，用于分析Covid-19
Efficient calibration for imperfect epidemic models with applications to the analysis of COVID-19
论文作者
论文摘要
模拟中未知参数的估计，也称为校准，对于流行病的实际管理和大流行风险的预测至关重要。一种简单但广泛使用的方法是通过最小化实际观测值和仿真输出之间的平方距离之和来估计参数。本文表明，该方法效率低下，尤其是当基于某些现实的简化（也称为不完美模型）开发流行模型时，通常在实践中使用。为了解决这个问题，引入了一个新的估计器，该估计值在渐近估计器上均不一致，具有较小的估计差异，并实现了半参数效率。进行数值研究以检查有限的样本性能。根据确定性和随机仿真，该方法将所提出的方法应用于20个国家（易感性暴露于疾病的恢复）模型的20个国家的Covid-19大流行分析。参数的估计，包括基本的繁殖数和平均孵化期，揭示了每个国家疾病爆发的风险，并为公共卫生干预设计设计提供了见解。
The estimation of unknown parameters in simulations, also known as calibration, is crucial for practical management of epidemics and prediction of pandemic risk. A simple yet widely used approach is to estimate the parameters by minimizing the sum of the squared distances between actual observations and simulation outputs. It is shown in this paper that this method is inefficient, particularly when the epidemic models are developed based on certain simplifications of reality, also known as imperfect models which are commonly used in practice. To address this issue, a new estimator is introduced that is asymptotically consistent, has a smaller estimation variance than the least squares estimator, and achieves the semiparametric efficiency. Numerical studies are performed to examine the finite sample performance. The proposed method is applied to the analysis of the COVID-19 pandemic for 20 countries based on the SEIR (Susceptible-Exposed-Infectious-Recovered) model with both deterministic and stochastic simulations. The estimation of the parameters, including the basic reproduction number and the average incubation period, reveal the risk of disease outbreaks in each country and provide insights to the design of public health interventions.
