Laboratoire de Mathématiques de Besançon, University of Bourgogne Franche-Comté, UFR ST 16 route de GRAY, 25030 Besançon, France.
Laboratoire Chrono-environnement, University of Bourgogne Franche-Comté, UFR ST 16 route de GRAY, 25030 Besançon, France.
J Theor Biol. 2022 Jul 21;545:111117. doi: 10.1016/j.jtbi.2022.111117. Epub 2022 May 2.
Many SARS-CoV-2 variants have appeared over the last months, and many more will continue to appear. Understanding the competition between these different variants could help make future predictions on the evolution of epidemics. In this study we use a mathematical model to investigate the impact of three different SARS-CoV-2 variants on the spread of COVID-19 across France, between January-May 2021 (before vaccination was extended to the full population). To this end, we use the data from Geodes (produced by Public Health France) and a particle swarm optimisation algorithm, to estimate the model parameters and further calculate a value for the basic reproduction number R. Sensitivity and uncertainty analysis is then used to better understand the impact of estimated parameter values on the number of infections leading to both symptomatic and asymptomatic individuals. The results confirmed that, as expected, the alpha, beta and gamma variants are more transmissible than the original viral strain. In addition, the sensitivity results showed that the beta/gamma variants could have lead to a larger number of infections in France (of both symptomatic and asymptomatic people).
过去几个月出现了许多 SARS-CoV-2 变体,而且还会有更多变体不断出现。了解这些不同变体之间的竞争,有助于我们对传染病的未来演变做出预测。在这项研究中,我们使用一个数学模型来研究三种不同的 SARS-CoV-2 变体对 2021 年 1 月至 5 月期间(在疫苗接种普及到全体人群之前)法国 COVID-19 传播的影响。为此,我们使用 Geodes(由法国公共卫生署提供)的数据和粒子群优化算法来估计模型参数,并进一步计算基本繁殖数 R 的值。然后进行敏感性和不确定性分析,以更好地了解估计参数值对导致有症状和无症状个体感染人数的影响。结果证实,正如预期的那样,阿尔法、贝塔和伽马变体比原始病毒株更具传染性。此外,敏感性结果表明,贝塔/伽马变体可能导致法国出现更多感染(包括有症状和无症状感染者)。