a:1:{s:5:"en_US";s:72:"1. Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy";}.
.
Acta Biomed. 2022 Aug 31;93(4):e2022212. doi: 10.23750/abm.v93i4.12645.
Restrictions to human mobility had a significant role in limiting SARS-CoV-2 spread. It has been suggested that seasonality might affect viral transmissibility. Our study retrospectively investigates the combined effect that seasonal environmental factors and human mobility played on transmissibility of SARS-CoV-2 in Lombardy, Italy, in 2020. Environmental data were collected from accredited open-source web services. Aggregated mobility data for different points of interests were collected from Google Community Reports. The Reproduction number (Rt), based on the weekly counts of confirmed symptomatic COVID-19, non-imported cases, was used as a proxy for SARS-CoV-2 transmissibility. Assuming a non-linear correlation between selected variables, we used a Generalized Additive Model (GAM) to investigate with univariate and multivariate analyses the association between seasonal environmental factors (UV-index, temperature, humidity, and atmospheric pressure), location-specific mobility indices, and Rt. UV-index was the most effective environmental variable in predicting Rt. An optimal two-week lag-effect between changes in explanatory variables and Rt was selected. The association between Rt variations and individually taken mobility indices differed: Grocery & Pharmacy, Transit Station and Workplaces displayed the best performances in predicting Rt when individually added to the multivariate model together with UV-index, accounting for 85.0%, 85.5% and 82.6% of Rt variance, respectively. According to our results, both seasonality and social interaction policies played a significant role in curbing the pandemic. Non-linear models including UV-index and location-specific mobility indices can predict a considerable amount of SARS-CoV-2 transmissibility in Lombardy during 2020, emphasizing the importance of social distancing policies to keep viral transmissibility under control, especially during colder months.
人类流动性的限制在限制 SARS-CoV-2 传播方面发挥了重要作用。有人认为季节性因素可能会影响病毒的传染性。我们的研究回顾性地调查了季节性环境因素和人类流动性对 2020 年意大利伦巴第地区 SARS-CoV-2 传染性的综合影响。环境数据从认可的开源网络服务收集。从谷歌社区报告中收集了不同兴趣点的聚合流动性数据。基于每周确诊有症状的 COVID-19、非输入性病例的繁殖数 (Rt) 被用作 SARS-CoV-2 传染性的替代指标。假设选定变量之间存在非线性相关，我们使用广义加性模型 (GAM) 进行单变量和多变量分析，调查季节性环境因素（紫外线指数、温度、湿度和大气压力）、特定地点的流动性指数与 Rt 之间的关联。紫外线指数是预测 Rt 的最有效环境变量。选择了一个最佳的两周滞后效应，即解释变量变化与 Rt 之间的滞后效应。Rt 变化与单独采取的流动性指数之间的关联不同：杂货店和药店、交通枢纽和工作场所，当与紫外线指数一起单独添加到多变量模型中时，分别能够解释 Rt 方差的 85.0%、85.5%和 82.6%，对 Rt 的预测性能最好。根据我们的结果，季节性和社会互动政策都在遏制大流行方面发挥了重要作用。包括紫外线指数和特定地点的流动性指数在内的非线性模型可以预测 2020 年伦巴第地区 SARS-CoV-2 相当大的传染性，强调了社交距离政策的重要性，以控制病毒的传染性，特别是在寒冷的月份。
