Computational Biology and Complex Systems Group, Department of Physics, Universitat Politècnica de Catalunya, Barcelona, Spain.
Microbiology Department, Vall D'Hebron Hospital Universitari, Vall D'Hebron Institut de Recerca, Vall D'Hebron Barcelona Hospital Campus, Barcelona, Spain.
Front Public Health. 2024 May 15;12:1339267. doi: 10.3389/fpubh.2024.1339267. eCollection 2024.
Countries across Europe have faced similar evolutions of SARS-CoV-2 variants of concern, including the Alpha, Delta, and Omicron variants.
We used data from GISAID and applied a robust, automated mathematical substitution model to study the dynamics of COVID-19 variants in Europe over a period of more than 2 years, from late 2020 to early 2023. This model identifies variant substitution patterns and distinguishes between residual and dominant behavior. We used weekly sequencing data from 19 European countries to estimate the increase in transmissibility between consecutive SARS-CoV-2 variants. In addition, we focused on large countries with separate regional outbreaks and complex scenarios of multiple competing variants.
Our model accurately reproduced the observed substitution patterns between the Alpha, Delta, and Omicron major variants. We estimated the daily variant prevalence and calculated between variants, revealing that: ( ) increased progressively from the Alpha to the Omicron variant; ( ) showed a high degree of variability within Omicron variants; ( ) a higher was associated with a later emergence of the variant within a country; ( ) a higher degree of immunization of the population against previous variants was associated with a higher for the Delta variant; ( ) larger countries exhibited smaller suggesting regionally diverse outbreaks within the same country; and finally ( ) the model reliably captures the dynamics of competing variants, even in complex scenarios.
The use of mathematical models allows for precise and reliable estimation of daily cases of each variant. By quantifying we have tracked the spread of the different variants across Europe, highlighting a robust increase in transmissibility trend from Alpha to Omicron. Additionally, we have shown that the geographical characteristics of a country, as well as the timing of new variant entrances, can explain some of the observed differences in variant substitution dynamics across countries.
欧洲各国都经历了 SARS-CoV-2 变异株的相似演变，包括 Alpha、Delta 和 Omicron 变异株。
我们使用 GISAID 数据，并应用稳健、自动的数学替代模型，研究了 2020 年末至 2023 年初超过 2 年期间欧洲 COVID-19 变异株的动态。该模型确定了变异株替代模式，并区分了残余和主导行为。我们使用来自 19 个欧洲国家的每周测序数据，估计了连续 SARS-CoV-2 变异株之间的传染性增加。此外，我们还关注了具有单独区域性暴发和多种竞争变异株复杂情况的大国。
我们的模型准确地再现了 Alpha、Delta 和 Omicron 主要变异株之间观察到的替代模式。我们估计了每日变异株流行率，并计算了变异株之间的 ，结果表明：( ) 从 Alpha 变异株到 Omicron 变异株逐渐增加；( ) Omicron 变异株内的 具有高度变异性；( ) 较高的 与该变异株在一个国家内的较晚出现相关；( ) 对先前变异株的更高程度免疫与对 Delta 变异株的更高 相关；( ) 较大的国家表现出较小的 表明同一国家内区域性不同暴发；最后（ ）即使在复杂情况下，该模型也能可靠地捕捉竞争变异株的动态。
使用数学模型可以精确可靠地估计每种变异株的每日病例数。通过量化 我们追踪了不同变异株在欧洲的传播情况，突出了从 Alpha 变异株到 Omicron 变异株的传染性增加趋势。此外，我们还表明，一个国家的地理特征以及新变异株进入的时间，可以解释一些国家间变异株替代动态观察到的差异。
