论文标题
使用SIR模型的修订版对Covid19在意大利进行建模
Modelling the spread of Covid19 in Italy using a revised version of the SIR model
论文作者
论文摘要
在本文中，我们提出了一个模型，以预测COVID-19的流行病的传播，并将其应用于意大利的特定情况。我们从一个简单的易感性，感染，恢复（SIR）模型开始，并添加了这样的条件，即在一定时间后，基本的繁殖数$ r_0 $呈指数级衰减，正如世界数据经验所暗示的那样。使用此模型，我们能够以5 \％的平均误差来重现流行病的真实行为。此外，我们说明了可能的未来场景，与$ r_0 $的不同间隔相关。自2020年3月初以来，该模型已被使用，预测了2020年4月流行病的意大利峰，约有100.000个检测到的活性病例。流行病的实际峰发生在2020年4月20日，有108.000例活动病例。该结果表明该模型具有意大利案例的预测能力。
In this paper, we present a model to predict the spread of the Covid-19 epidemic and apply it to the specific case of Italy. We started from a simple Susceptible, Infected, Recovered (SIR) model and we added the condition that, after a certain time, the basic reproduction number $R_0$ exponentially decays in time, as empirically suggested by world data. Using this model, we were able to reproduce the real behavior of the epidemic with an average error of 5\%. Moreover, we illustrate possible future scenarios, associated to different intervals of $R_0$. This model has been used since the beginning of March 2020, predicting the Italian peak of the epidemic in April 2020 with about 100.000 detected active cases. The real peak of the epidemic happened on the 20th of April 2020, with 108.000 active cases. This result shows that the model had predictive power for the italian case.
