论文标题
COVID-19传播：使用Euclidean网络上的SIR模型复制数据和预测
Covid-19 spread: Reproduction of data and prediction using a SIR model on Euclidean network
论文作者
论文摘要
我们研究了中国Covid-19爆发中的累积和日常案例数量的数据。累积数据可以适合从先前在欧几里得网络上研究的易感感染的（SIR）模型获得的经验形式。在中国和意大利，绘制与距离中心距离的距离的案例数，我们发现近似功率定律的变化，指数$ \ sim 1.85 $强烈表明空间依赖性起着关键作用，这是模型中的一个因素。我们在这里报告说，Eucledean网络上的SIR模型可以高精度地重现给定参数值的中国数据，并且还可以预测至少在本地的流行病何时可以结束。
We study the datafor the cumulative as well as daily number of cases in the Covid-19 outbreak in China. The cumulative data can be fit to an empirical form obtained from a Susceptible-Infected-Removed (SIR) model studied on an Euclidean network previously. Plotting the number of cases against the distance from the epicenter for both China and Italy, we find an approximate power law variation with an exponent $\sim 1.85$ showing strongly that the spatial dependence plays a key role, a factor included in the model. We report here that the SIR model on the Eucledean network can reproduce with a high accuracy the data for China for given parameter values, and can also predict when the epidemic, at least locally, can be expected to be over.
