论文标题
使用IBM SEIR模型对隔离网络的影响量化隔离的影响
Quantifying the effects of quarantine using an IBM SEIR model on scalefree networks
论文作者
论文摘要
共同的19日大流行使几个国家诉诸于社会疏远，这是减缓病毒传播并控制卫生系统的唯一已知方法。在这里，我们使用基于个体的模型（IBM）来研究隔离的持续时间，开始日期和强度如何影响感染曲线峰的高度和位置。我们表明，模型动力学固有的随机效应会导致相同的一组参数的变化结果，这对于计算每个结果的概率至关重要。为了简化分析，我们仅将结果划分为两类，我们称之为{最佳和最坏情况。尽管漫长而强烈的隔离是结束流行病的最佳方法，但在实践中很难实施。在这里，我们表明，相对较短和强烈的隔离时期也可以非常有效地扁平感染曲线，甚至杀死病毒，但是这种结果的可能性很低。另一方面，相对较低强度的长长隔离可以延迟感染峰值并大大减少50％以上的概率，这是比短期内完全锁定更有效的政策。
The COVID-19 pandemic led several countries to resort to social distancing, the only known way to slow down the spread of the virus and keep the health system under control. Here we use an individual based model (IBM) to study how the duration, start date and intensity of quarantine affect the height and position of the peak of the infection curve. We show that stochastic effects, inherent to the model dynamics, lead to variable outcomes for the same set of parameters, making it crucial to compute the probability of each result. To simplify the analysis we divide the outcomes in only two categories, that we call {best and worst scenarios. Although long and intense quarantine is the best way to end the epidemic, it is very hard to implement in practice. Here we show that relatively short and intense quarantine periods can also be very effective in flattening the infection curve and even killing the virus, but the likelihood of such outcomes are low. Long quarantines of relatively low intensity, on the other hand, can delay the infection peak and reduce its size considerably with more than 50% probability, being a more effective policy than complete lockdown for short periods.
