Data-Driven Methods for Present and Future Pandemics: Monitoring, Modelling and Managing

TL;DR

This survey explores data-driven methods for pandemic monitoring, modelling, and management, emphasizing interdisciplinary approaches and discussing challenges and applications to COVID-19 and future epidemics.

Contribution

It provides a comprehensive roadmap integrating data science, epidemiology, and control theory for pandemic analysis and response strategies.

Findings

Data-driven schemes can effectively monitor epidemic evolution.

Modelling approaches improve forecasting accuracy.

Timely decision-making is crucial for epidemic control.

Abstract

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.