论文标题
利用概念漂移来衡量政策干预措施的有效性：COVID-19的案例大流行
Utilizing Concept Drift for Measuring the Effectiveness of Policy Interventions: The Case of the COVID-19 Pandemic
论文作者
论文摘要
为了对COVID-19病毒的高传染性和致死性的反应，全世界各国采取了严厉的政策措施来遏制大流行。但是，尚不清楚这些措施对所谓的非药物干预措施（NPI）对病毒的扩散具有哪些影响。在本文中，我们使用机器学习并以一种新颖的方式应用了漂移检测方法，以预测在9个欧洲国家和28个美国州的Covid-19的日常案例数量的发展中的政策干预措施的时间滞后。我们的分析表明，在NPI颁布和案例数中漂移之间，平均而言有超过两周的时间。
As a reaction to the high infectiousness and lethality of the COVID-19 virus, countries around the world have adopted drastic policy measures to contain the pandemic. However, it remains unclear which effect these measures, so-called non-pharmaceutical interventions (NPIs), have on the spread of the virus. In this article, we use machine learning and apply drift detection methods in a novel way to predict the time lag of policy interventions with respect to the development of daily case numbers of COVID-19 across 9 European countries and 28 US states. Our analysis shows that there are, on average, more than two weeks between NPI enactment and a drift in the case numbers.
