Ecole Normale Supérieure de Lyon, Laboratoire de Physique CNRS UMR 5672, 46 allée d'Italie, F-69364 Lyon Cedex 7, France.
Chaos. 2023 Oct 1;33(10). doi: 10.1063/5.0161222.
We investigate the mechanisms behind quasi-periodic outbursts on the Covid-19 epidemics. Data for France and Germany show that the patterns of outbursts exhibit a qualitative change in early 2022, which appears in a change in their average period and which is confirmed by the time-frequency analysis. This provides a signal that can be used to discriminate among several mechanisms. Two main ideas have been proposed to explain periodicity in epidemics. One involves memory effects and another considers exchanges between epidemic clusters and a reservoir of population. We test these two approaches in the particular case of the Covid-19 epidemics and show that the "cluster model" is the only one that appears to be able to explain the observed pattern with realistic parameters. The last section discusses our results in the context of early studies of epidemics, and we stress the importance to work with models with a limited number of parameters, which moreover can be sufficiently well estimated, to draw conclusions on the general mechanisms behind the observations.
我们研究了新冠疫情准周期爆发背后的机制。来自法国和德国的数据表明，2022 年初爆发模式发生了定性变化，这表现为其平均周期的变化，这一点也得到了时频分析的证实。这提供了一个可以用来区分几种机制的信号。有两种主要的观点被提出来解释传染病的周期性。一种涉及记忆效应，另一种则考虑了传染病集群与人口储备之间的交换。我们在新冠疫情的特殊情况下测试了这两种方法，并表明“集群模型”是唯一一种似乎能够用实际参数来解释所观察到的模式的方法。最后一部分讨论了我们在传染病早期研究背景下的结果，并强调了使用参数数量有限且能够得到充分估计的模型来得出关于观察到的一般机制的结论的重要性。
