This survey analyses the role of data-driven methodologies for pandemic modelling and control. We provide a roadmap from the access to epidemiological data sources to the control of epidemic phenomena. We review the available methodologies and discuss the challenges in the development of data-driven strategies to combat the spreading of infectious diseases. Our aim is to bring together several different disciplines required to provide a holistic approach to epidemic analysis, such as data science, epidemiology, and systems-and-control theory. A 3M-analysis is presented, whose three pillars are: Monitoring, Modelling and Managing. The focus is on the potential of data-driven schemes to address three different challenges raised by a pandemic: (i) monitoring the epidemic evolution and assessing the effectiveness of the adopted countermeasures; (ii) modelling and forecasting the spread of the epidemic; (iii) making timely decisions to manage, mitigate and suppress the contagion. For each step of this roadmap, we review consolidated theoretical approaches (including data-driven methodologies that have been shown to be successful in other contexts) and discuss their application to past or present epidemics, such as Covid-19, as well as their potential application to future epidemics.

该调查分析了数据驱动方法在大流行建模和控制中的作用。我们提供了从获取流行病学数据源到控制流行病现象的路线图。我们回顾了可用的方法并讨论了开发数据驱动策略以对抗传染病传播的挑战。我们的目标是汇集提供流行病分析整体方法所需的几个不同学科，例如数据科学、流行病学和系统与控制理论。提出了 3M 分析，其三大支柱是：监控、建模和管理。重点是数据驱动方案在解决大流行带来的三个不同挑战方面的潜力：(i) 监测流行病的演变并评估所采取对策的有效性；(ii) 建模和预测流行病的传播；(iii) 及时做出管理、减轻和抑制蔓延的决定。对于本路线图的每一步，我们回顾综合理论方法（包括已证明在其他情况下成功的数据驱动方法）并讨论它们在过去或现在的流行病（例如 Covid-19）中的应用，以及它们的潜力应用于未来的流行病。