论文标题
数据驱动的方法用于监视，模型，预测和控制Covid-19大流行：利用数据科学，流行病学和控制理论
Data-Driven Methods to Monitor, Model, Forecast and Control Covid-19 Pandemic: Leveraging Data Science, Epidemiology and Control Theory
论文作者
论文摘要
该文档分析了数据驱动方法在Covid-19-19大流行中的作用。我们提供SWOT分析和路线图，该路线图从访问数据源到最终决策步骤。我们的目标是回顾可用的方法论，同时预见了制定数据驱动策略的困难和挑战，以打击COVID-19的大流行。提出了3M分析：监视，建模和决策。重点是众所周知的数据源方案解决大流行提出的不同挑战的潜力：i）监测和预测流行病的传播； （ii）评估政府决定的有效性； （iii）及时做出决定。路线图的每个步骤都通过对合并理论结果的综述及其在Covid-19环境中的潜在应用来详细介绍。如果可能的话，我们提供了他们在过去或现在流行病上的应用的示例。我们不提供对方法，算法和应用程序的详尽列举。我们确实试图作为为流行病提供整体方法所需的不同学科之间的桥梁：数据科学，流行病学，控制理论等。即，我们重点介绍了在其他情况下已被证明在其他情况下成功地应用的有效数据驱动的方法论，它们在所提出的路线图的不同步骤中具有潜在的应用。为了使该文档更具功能性并适应每个学科的细节，我们鼓励研究人员和从业人员提供反馈。我们将定期更新此文档。
This document analyzes the role of data-driven methodologies in Covid-19 pandemic. We provide a SWOT analysis and a roadmap that goes from the access to data sources to the final decision-making step. We aim to review the available methodologies while anticipating the difficulties and challenges in the development of data-driven strategies to combat the Covid-19 pandemic. A 3M-analysis is presented: Monitoring, Modelling and Making decisions. The focus is on the potential of well-known datadriven schemes to address different challenges raised by the pandemic: i) monitoring and forecasting the spread of the epidemic; (ii) assessing the effectiveness of government decisions; (iii) making timely decisions. Each step of the roadmap is detailed through a review of consolidated theoretical results and their potential application in the Covid-19 context. When possible, we provide examples of their applications on past or present epidemics. We do not provide an exhaustive enumeration of methodologies, algorithms and applications. We do try to serve as a bridge between different disciplines required to provide a holistic approach to the epidemic: data science, epidemiology, controltheory, etc. That is, we highlight effective data-driven methodologies that have been shown to be successful in other contexts and that have potential application in the different steps of the proposed roadmap. To make this document more functional and adapted to the specifics of each discipline, we encourage researchers and practitioners to provide feedback. We will update this document regularly.
