论文标题
建模和预测Covid-19的蔓延的挑战
The challenges of modeling and forecasting the spread of COVID-19
论文作者
论文摘要
我们为COVID-19提供了三种数据驱动的模型类型，其参数数量最少，以提供有关疾病传播的见解，可用于制定政策反应。第一个是指数增长，在分析早期数据时进行了广泛研究。第二个是一个自我激发的分支过程模型，其中包括传输和恢复的延迟。它允许有意义地适合早期随机数据。第三个是著名的易感感染的耐药（SIR）模型及其表弟Seir，具有“暴露”成分。所有三个模型均与数量相关，SIR模型用于说明美国短期距离措施的潜在影响。
We present three data driven model-types for COVID-19 with a minimal number of parameters to provide insights into the spread of the disease that may be used for developing policy responses. The first is exponential growth, widely studied in analysis of early-time data. The second is a self-exciting branching process model which includes a delay in transmission and recovery. It allows for meaningful fit to early time stochastic data. The third is the well-known Susceptible-Infected-Resistant (SIR) model and its cousin, SEIR, with an "Exposed" component. All three models are related quantitatively, and the SIR model is used to illustrate the potential effects of short-term distancing measures in the United States.
