论文标题
一种连续的蒙特卡洛方法，用于估计传染病模型中繁殖数的时间变化的时间：COVID-19病例
A sequential Monte Carlo approach to estimate a time varying reproduction number in infectious disease models: the Covid-19 case
论文作者
论文摘要
在最初的几个月中，Covid-19的大流行要求大多数国家实施复杂的非药物干预措施，以控制人群中病毒的传播。为了能够快速做出决定，必须详细了解当前情况。时间变化的瞬时繁殖数估计代表了实时量化病毒传播的一种方法。它们通常是通过流行病的数学隔室模型来定义的，例如随机SEIR模型，其参数必须从多个时间序列的流行病学数据中估算。由于尺寸很高的参数空间（部分是由于传播模型中的随机性）和数据不完整和延迟的数据，因此推断非常具有挑战性。我们提出了该模型的状态空间形式化和一种顺序的蒙特卡洛方法，该方法允许根据日常住院和阳性测试率估算挪威Covid-19的日常繁殖数。该方法是在挪威定期使用的，是流行病监测和管理的强大工具。
During the first months, the Covid-19 pandemic has required most countries to implement complex sequences of non-pharmaceutical interventions, with the aim of controlling the transmission of the virus in the population. To be able to take rapid decisions, a detailed understanding of the current situation is necessary. Estimates of time-varying, instantaneous reproduction numbers represent a way to quantify the viral transmission in real time. They are often defined through a mathematical compartmental model of the epidemic, like a stochastic SEIR model, whose parameters must be estimated from multiple time series of epidemiological data. Because of very high dimensional parameter spaces (partly due to the stochasticity in the spread models) and incomplete and delayed data, inference is very challenging. We propose a state space formalisation of the model and a sequential Monte Carlo approach which allow to estimate a daily-varying reproduction number for the Covid-19 epidemic in Norway with sufficient precision, on the basis of daily hospitalisation and positive test incidences. The method is in regular use in Norway and is a powerful instrument for epidemic monitoring and management.
