论文标题
不平等的影响和空间汇总扭曲了COVID-19的增长率
Unequal Impact and Spatial Aggregation Distort COVID-19 Growth Rates
论文作者
论文摘要
COVID-19大流行已成为全球公共卫生危机。为了决定缓解策略并了解疾病动态，决策者和流行病学家必须知道该疾病如何在其社区中传播。我们分析了确认的多个地理量表的感染和死亡，以表明Covid-19的影响是高度不平等的：许多子区域的感染几乎为零，而其他则是热点。我们将这种影响归因于一种类似芦苇的疾病的机制，在这种机制中，疾病在不同的时间到来并成倍增长。然而，热点似乎比相邻子区域的增长快，并主导了空间汇总的统计量，从而扩大了生长速度。 COVID-19的交错蔓延也可以使总体增长率看起来更高，即使子区域以相同的速度生长。公共政策，经济分析和流行模型需要考虑到空间聚集引起的潜在扭曲。
The COVID-19 pandemic has emerged as a global public health crisis. To make decisions about mitigation strategies and to understand the disease dynamics, policy makers and epidemiologists must know how the disease is spreading in their communities. We analyze confirmed infections and deaths over multiple geographic scales to show that COVID-19's impact is highly unequal: many subregions have nearly zero infections, and others are hot spots. We attribute the effect to a Reed-Hughes-like mechanism in which disease arrives at different times and grows exponentially. Hot spots, however, appear to grow faster than neighboring subregions and dominate spatially aggregated statistics, thereby amplifying growth rates. The staggered spread of COVID-19 can also make aggregated growth rates appear higher even when subregions grow at the same rate. Public policy, economic analysis and epidemic modeling need to account for potential distortions introduced by spatial aggregation.
