Laboratoire de Physique de l'Ecole Normale Supérieure (LPENS), CNRS UMR 8023, Ecole Normale Supérieure, Université PSL, Sorbonne Université, and Université de Paris, 75005, Paris, France.
Institut de Biologie de l'ENS (IBENS), École Normale Supérieure, CNRS, INSERM, Université PSL, 75005, Paris, France; INRAE, Micalis Institute, 78350, Jouy-en-Josas, France.
Biochimie. 2023 Oct;213:54-65. doi: 10.1016/j.biochi.2023.03.006. Epub 2023 Mar 16.
The COVID-19 pandemic has given rise to numerous articles from different scientific fields (epidemiology, virology, immunology, airflow physics …) without any effort to link these different insights. In this review, we aim to establish relationships between epidemiological data and the characteristics of the virus strain responsible for the epidemic wave concerned. We have carried out this study on the Wuhan, Alpha, Delta and Omicron strains allowing us to illustrate the evolution of the relationships we have highlighted according to these different viral strains. We addressed the following questions. 1) How can the mean infectious dose (one quantum, by definition in epidemiology) be measured and expressed as an amount of viral RNA molecules (in genome units, GU) or as a number of replicative viral particles (in plaque-forming units, PFU)? 2) How many infectious quanta are exhaled by an infected person per unit of time? 3) How many infectious quanta are exhaled, on average, integrated over the whole contagious period? 4) How do these quantities relate to the epidemic reproduction rate R as measured in epidemiology, and to the viral load, as measured by molecular biological methods? 5) How has the infectious dose evolved with the different strains of SARS-CoV-2? We make use of state-of-the-art modelling, reviewed and explained in the appendix of the article (Supplemental Information, SI), to answer these questions using data from the literature in both epidemiology and virology. We have considered the modification of these relationships according to the vaccination status of the population.
COVID-19 大流行引发了来自不同科学领域(流行病学、病毒学、免疫学、气流物理学等)的大量文章,但没有努力将这些不同的观点联系起来。在这篇综述中,我们旨在建立流行病学数据与引发相关疫情波的病毒株特征之间的关系。我们对武汉、阿尔法、德尔塔和奥密克戎株进行了这项研究,使我们能够根据这些不同的病毒株说明我们所强调的关系的演变。我们提出了以下问题。1)如何测量平均感染剂量(定义为流行病学中的一个量子)并表示为病毒 RNA 分子的量(基因组单位,GU)或复制性病毒颗粒的数量(噬菌斑形成单位,PFU)?2)感染者每单位时间呼出多少个感染剂量?3)在整个传染期内平均呼出多少个感染剂量?4)这些数量与流行病学中测量的传染病再生率 R 和分子生物学方法测量的病毒载量有何关系?5)不同的 SARS-CoV-2 株的感染剂量是如何演变的?我们利用最先进的模型,在文章的附录(补充信息,SI)中进行了综述和解释,使用流行病学和病毒学文献中的数据回答了这些问题。我们考虑了根据人群的疫苗接种状态对这些关系进行修正。