Berestycki Henri, Desjardins Benoît, Heintz Bruno, Oury Jean-Marc
École des Hautes Études en Sciences Sociales and CNRS, CAMS, Paris, France.
Institute for Advanced Study, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong.
Sci Rep. 2021 Sep 15;11(1):18339. doi: 10.1038/s41598-021-97077-x.
Plateaus and rebounds of various epidemiological indicators are widely reported in Covid-19 pandemics studies but have not been explained so far. Here, we address this problem and explain the appearance of these patterns. We start with an empirical study of an original dataset obtained from highly precise measurements of SARS-CoV-2 concentration in wastewater over nine months in several treatment plants around the Thau lagoon in France. Among various features, we observe that the concentration displays plateaus at different dates in various locations but at the same level. In order to understand these facts, we introduce a new mathematical model that takes into account the heterogeneity and the natural variability of individual behaviours. Our model shows that the distribution of risky behaviours appears as the key ingredient for understanding the observed temporal patterns of epidemics.
在新冠疫情研究中,各种流行病学指标的平台期和反弹现象被广泛报道,但至今尚未得到解释。在此,我们解决这一问题并解释这些模式的出现。我们首先对一个原始数据集进行实证研究,该数据集来自法国陶湖周边几个污水处理厂九个月来对新冠病毒浓度的高精度测量。在各种特征中,我们观察到该浓度在不同地点的不同日期呈现出处于同一水平的平台期。为了理解这些事实,我们引入了一个新的数学模型,该模型考虑了个体行为的异质性和自然变异性。我们的模型表明,危险行为的分布似乎是理解所观察到的疫情时间模式的关键因素。