Sorbonne Université, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique, F75012 Paris, France; Sorbonne Université, CNRS, Laboratoire de Probabilités, Statistique et Modélisation, F-75013 Paris, France.
Sorbonne Université, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique, F75012 Paris, France.
Int J Infect Dis. 2023 Mar;128:132-139. doi: 10.1016/j.ijid.2022.12.042. Epub 2023 Jan 3.
The influenza circulation reportedly declined during the COVID-19 pandemic in many countries. The occurrence of this change has not been studied worldwide nor its potential drivers.
The change in the proportion of positive influenza samples reported by country and trimester was computed relative to the 2014-2019 period using the FluNet database. Random forests were used to determine predictors of change from demographical, weather, pandemic preparedness, COVID-19 incidence, and pandemic response characteristics. Regression trees were used to classify observations according to these predictors.
During the COVID-19 pandemic, the influenza decline relative to prepandemic levels was global but heterogeneous across space and time. It was more than 50% for 311 of 376 trimesters-countries and even more than 99% for 135. COVID-19 incidence and pandemic preparedness were the two most important predictors of the decline. Europe and North America initially showed limited decline despite high COVID-19 restrictions; however, there was a strong decline afterward in most temperate countries, where pandemic preparedness, COVID-19 incidence, and social restrictions were high; the decline was limited in countries where these factors were low. The "zero-COVID" countries experienced the greatest decline.
Our findings set the stage for interpreting the resurgence of influenza worldwide.
据报道，在许多国家 COVID-19 大流行期间，流感的传播有所减少。这种变化在全球范围内尚未得到研究，也未研究其潜在驱动因素。
使用 FluNet 数据库，相对于 2014-2019 年期间，计算了各国和每三个月报告的阳性流感样本比例的变化。随机森林用于确定人口统计学、天气、大流行准备、COVID-19 发病率和大流行应对特征变化的预测因素。回归树用于根据这些预测因素对观察结果进行分类。
在 COVID-19 大流行期间，相对于大流行前水平，流感的下降是全球性的，但在空间和时间上存在异质性。在 376 个 trimester-countries 中有 311 个的下降幅度超过 50%，其中 135 个的下降幅度甚至超过 99%。COVID-19 发病率和大流行准备是下降的两个最重要的预测因素。尽管 COVID-19 限制很高，但欧洲和北美最初的下降幅度有限；然而，在大多数温带国家，大流行准备、COVID-19 发病率和社会限制很高的国家，后来的下降幅度很大；在这些因素较低的国家，下降幅度有限。“零 COVID”国家经历了最大的下降。
我们的研究结果为解释全球范围内流感的再次爆发奠定了基础。
