论文标题
19009年大流行期间的非药物干预措施：快速审查
Non-pharmaceutical interventions during the COVID-19 pandemic: a rapid review
论文作者
论文摘要
传染病和人类行为交织在一起。一方面，我们的运动和互动是传输的引擎。另一方面，病毒的展开可能会导致我们的日常活动变化。尽管直观，但我们对此类反馈循环的理解仍然有限。在COVID-19之前，有关该主题的文献主要是理论上的，并且在很大程度上错过了验证。主要问题是缺乏捕获疾病引起的行为改变的经验数据。在2020年，情况发生了巨大变化。非药物干预措施（NPI）是针对SARS-COV-2病毒的关键武器，几乎影响了任何社会过程。不幸的是，不幸的是，不幸的是，不幸的是，不幸的是，不幸的是，不幸的是，不幸的是，不幸的是，不幸的是，不幸的是，事件取消，社交距离，宵禁和锁定已经非常熟悉。最新技术保证的紧急情况，调查的易度性以及众包部署，由科技巨头，主要手机提供商和其他公司开发的良好计划的几个数据允许对描述大流行引起的行为变化的数据访问。在这里，我的目标是回顾一些关于NPI的大量文献，期间在COVID-19大流行期间。在此过程中，我分析了347篇文章，由2518多名作者在过去的$ 12 $月中撰写。尽管大部分样品是通过查询PubMed获得的，但它还包括手工策划的列表。考虑到焦点和方法，我将样本分为七个主要类别：流行病模型，调查，评论/观点，旨在量化NPIS效果的论文，使用数据代理来衡量NPI的效果，文章以及描述NPIS的公开可用数据集。我总结了每个类别中文章的方法，数据的发现，并提供了展望，强调了未来的挑战和机会
Infectious diseases and human behavior are intertwined. On one side, our movements and interactions are the engines of transmission. On the other, the unfolding of viruses might induce changes to our daily activities. While intuitive, our understanding of such feedback loop is still limited. Before COVID-19 the literature on the subject was mainly theoretical and largely missed validation. The main issue was the lack of empirical data capturing behavioral change induced by diseases. Things have dramatically changed in 2020. Non-pharmaceutical interventions (NPIs) have been the key weapon against the SARS-CoV-2 virus and affected virtually any societal process. Travels bans, events cancellation, social distancing, curfews, and lockdowns have become unfortunately very familiar. The scale of the emergency, the ease of survey as well as crowdsourcing deployment guaranteed by the latest technology, several Data for Good programs developed by tech giants, major mobile phone providers, and other companies have allowed unprecedented access to data describing behavioral changes induced by the pandemic. Here, I aim to review some of the vast literature written on the subject of NPIs during the COVID-19 pandemic. In doing so, I analyze 347 articles written by more than 2518 of authors in the last $12$ months. While the large majority of the sample was obtained by querying PubMed, it includes also a hand-curated list. Considering the focus, and methodology I have classified the sample into seven main categories: epidemic models, surveys, comments/perspectives, papers aiming to quantify the effects of NPIs, reviews, articles using data proxies to measure NPIs, and publicly available datasets describing NPIs. I summarize the methodology, data used, findings of the articles in each category and provide an outlook highlighting future challenges as well as opportunities
