论文标题
推断意大利的COVID-19感染曲线
Inferring the COVID-19 infection curve in Italy
论文作者
论文摘要
本手稿的目的是显示一种简单的方法，可以从可用的汇总数据（例如死亡和住院数量）中推断出新的Covid-19感染（最重要的信息，以建立遏制策略的效果）。该方法用于HIV-AID，并被称为“反计算”，依赖于对感染和观察到的事件之间延迟分布的良好估计；假设流行病遵循具有已知生成间隔的简单SIR模型，我们可以估计通过最大似然定义时间变化的接触率的参数。我们显示了该方法在意大利及其几个地区的数据中的应用；发现$ r_0 $在3月20日左右始终下降到1次以下，并且在4月初，在整个意大利和大多数地区，它在0.5至0.8之间。
Aim of this manuscript is to show a simple method to infer the time-course of new COVID-19 infections (the most important information in order to establish the effect of containment strategies) from available aggregated data, such as number of deaths and hospitalizations. The method, that was used for HIV-AIDS and was named `back-calculation', relies on good estimates of the distribution of the delays between infection and the observed events; assuming that the epidemic follows a simple SIR model with a known generation interval, we can then estimate the parameters that define the time-varying contact rate through maximum likelihood. We show the application of the method to data from Italy and several of its region; it is found that $R_0$ had decreased consistently below 1 around March 20, and in the beginning of April it was between 0.5 and 0.8 in the whole Italy and in most regions.
