School of Mathematics and Statistics, University of Sydney, NSW 2006, Australia.
Yau Mathematical Sciences Center, Tsinghua University, Beijing 100084, China.
Chaos. 2020 Sep;30(9):091102. doi: 10.1063/5.0024204.
This paper introduces a mathematical framework for determining second surge behavior of COVID-19 cases in the United States. Within this framework, a flexible algorithmic approach selects a set of turning points for each state, computes distances between them, and determines whether each state is in (or over) a first or second surge. Then, appropriate distances between normalized time series are used to further analyze the relationships between case trajectories on a month-by-month basis. Our algorithm shows that 31 states are experiencing second surges, while four of the 10 largest states are still in their first surge, with case counts that have never decreased. This analysis can aid in highlighting the most and least successful state responses to COVID-19.
本文介绍了一种用于确定美国 COVID-19 病例二次激增行为的数学框架。在这个框架内,一种灵活的算法方法为每个州选择一组转折点,计算它们之间的距离,并确定每个州是处于(或超过)第一次还是第二次激增。然后,使用归一化时间序列之间的适当距离进一步分析逐月的病例轨迹之间的关系。我们的算法表明,31 个州正在经历二次激增,而 10 个最大的州中有 4 个仍处于第一次激增,病例数从未减少。这种分析可以帮助突出对 COVID-19 反应最成功和最不成功的州。