论文标题
随机爵士模型的锁定/测试缓解策略及其与韩国,德国和纽约数据的比较
Study of lockdown/testing mitigation strategies on stochastic SIR model and its comparison with South Korea, Germany and New York data
论文作者
论文摘要
我们目前面临着全球大流行的高度关键案例。事实证明,新颖的冠状病毒(SARS-COV-2,又名Covid-19)具有极具传染性,亚洲的最初爆发现在已经传播到各大洲。这种情况将在研究方面从研究中获得成果,从而评估有效的对策,以加重采用策略的影响。标准的易感性反感染(SIR)模型是一个非常成功且广泛使用的数学模型,用于预测流行病的传播。我们在随机网络上采用SIR模型,并将模型扩展到包括控制策略{\ em锁定}和{\ em Testing} - 两个经常使用的缓解策略。这些策略控制大流行传播的能力是通过改变实施的有效性来研究的。撤回缓解策略后,对第二次爆发的可能性进行了详细评估。我们注意到,无论如何,这种缓解策略的突然中断可能会引起第二次爆发的复兴,其高峰将与易感人群的数量相关。实际上,我们发现人口将仍然容易受到感染的影响,直到获得牛群的免疫力为止。我们还使用有关韩国,德国和纽约流行病的实际统计数据和信息测试我们的模型,并与模拟数据找到了一个非凡的协议。
We are currently facing a highly critical case of a world-wide pandemic. The novel coronavirus (SARS-CoV-2, a.k.a. COVID-19) has proved to be extremely contagious and the original outbreak from Asia has now spread to all continents. This situation will fruitfully profit from the study in regards of the spread of the virus, assessing effective countermeasures to weight the impact of the adopted strategies. The standard Susceptible-Infectious-Recovered (SIR) model is a very successful and widely used mathematical model for predicting the spread of an epidemic. We adopt the SIR model on a random network and extend the model to include control strategies {\em lockdown} and {\em testing} -- two often employed mitigation strategies. The ability of these strategies in controlling the pandemic spread is investigated by varying the effectiveness with which they are implemented. The possibility of a second outbreak is evaluated in detail after the mitigation strategies are withdrawn. We notice that, in any case, a sudden interruption of such mitigation strategies will likely induce a resurgence of a second outbreak, whose peak will be correlated to the number of susceptible individuals. In fact, we find that a population will remain vulnerable to the infection until the herd immunity is achieved. We also test our model with real statistics and information on the epidemic spread in South Korea, Germany, and New York and find a remarkable agreement with the simulation data.