论文标题
在流行病中屏蔽弱势群体：一种数值方法
Shielding the vulnerable in an epidemic: a numerical approach
论文作者
论文摘要
通过将人口分为两个类别，脆弱的和拟合，可以减少Covid-19的死亡人数，并具有不同的锁定制度。现在有四个参数，而不是一个复制号码。这些使得可以量化社会疏远措施的影响。在两种类型的种群中，有一个简单的随机模型。除了脆弱和拟合度的人口的大小以及两类感染的初始数量外，只需要四个繁殖参数才能运行两种类型的REED REED-FROST模型。该程序简单快捷。在PC上，对于美国规模的人口，进行数十万个流行病的模拟需要不到五分钟。流行病是非线性过程。结果可能是违反直觉的。在两种类型的人群中，感染感染者感染的脆弱人群的平均数量是流行病的关键参数。直观地，此参数应该很小。但是，模拟表明，即使此参数很小，死亡人数也可能高于没有屏蔽的情况。在某些条件下，增加参数的价值可能会减少死亡人数。本文在我们的直觉中介绍了这些盲点。
The death toll for Covid-19 may be reduced by dividing the population into two classes, the vulnerable and the fit, with different lockdown regimes. Instead of one reproduction number there now are four parameters. These make it possible to quantify the effect of the social distancing measures. There is a simple stochastic model for epidemics in a two type population. Apart from the size of the population of the vulnerable and the fit, and the initial number of infected in the two classes, only the four reproduction parameters are needed to run the two type Reed-Frost model. The program is simple and fast. On a pc it takes less than five minutes to do a hundred thousand simulations of the epidemic for a population of the size of the US. Epidemics are non-linear processes. Results may be counterintuitive. The average number of vulnerable persons infected by an infectious fit person is a crucial parameter of the epidemic in the two type population. Intuitively this parameter should be small. However simulations show that even if this parameter is small the death toll may be higher than without shielding. Under certain conditions increasing the value of the parameter may reduce the death toll. The article addresses these blind spots in our intuition.
