Duchesne Jean, Coubard Olivier A
Agrocampus Ouest, 2 rue André Le Nôtre, 49000, Angers, France.
The Neuropsychological Laboratory, CNS-Fed, 240 rue de Rivoli, 75001, Paris, France.
Infect Dis Model. 2022 Jun;7(2):64-74. doi: 10.1016/j.idm.2022.02.004. Epub 2022 Mar 11.
Modelling how a pandemic is spreading over time is a challenging issue. The new coronavirus disease called COVID-19 does not escape this rule as it has embraced over two hundred countries. As for previous pandemics, several studies have attempted to model the occurrence of cases caused by COVID-19. However, no study has succeeded in accurately modelling the impact of the infectious agent. Here we show that COVID-19 daily case distribution in humans obeys a Gamma law, which two new parameters can describe without any adjustment. Though the Gamma law has been exploited for nearly two centuries to describe the statistical distribution of spatial or temporal quantities, the goodness-of-fit rationale using two or three parameters has remained enigmatic. The new Gamma law approach we demonstrate here emerges from actual data and sheds light on the underlying mechanisms of the observed phenomenon. This finding has promising applicability in the epidemiological domain and in all disciplines involving branching systems, for which our Gamma law approach may bring a solution to hitherto unsolved problems.
对大流行病随时间的传播方式进行建模是一个具有挑战性的问题。名为COVID-19的新型冠状病毒病也不例外，它已蔓延至两百多个国家。与之前的大流行病一样，多项研究试图对由COVID-19导致的病例发生情况进行建模。然而，尚无研究成功准确模拟出这种传染源的影响。在此我们表明，人类中COVID-19的每日病例分布服从伽马分布律，有两个新参数无需任何调整即可对其进行描述。尽管伽马分布律已被用于描述空间或时间量的统计分布近两个世纪，但使用两三个参数的拟合优度原理一直令人费解。我们在此展示的新伽马分布律方法源自实际数据，并揭示了所观察现象的潜在机制。这一发现在流行病学领域以及所有涉及分支系统的学科中具有广阔的应用前景，我们的伽马分布律方法可能为这些领域中迄今尚未解决的问题带来解决方案。
