TY - JOUR
T1 - Data-driven methods for present and future pandemics
T2 - Monitoring, modelling and managing
AU - Alamo, Teodoro
AU - G. Reina, Daniel
AU - Millán Gata, Pablo
AU - Preciado, Victor M.
AU - Giordano, Giulia
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Epidemic control
KW - Epidemiological models
KW - Forecasting
KW - Machine learning
KW - Model predictive control
KW - Optimal control
KW - Pandemic control
KW - Surveillance systems
UR - http://www.scopus.com/inward/record.url?scp=85108971162&partnerID=8YFLogxK
U2 - 10.1016/j.arcontrol.2021.05.003
DO - 10.1016/j.arcontrol.2021.05.003
M3 - Review article
AN - SCOPUS:85108971162
SN - 1367-5788
VL - 52
SP - 448
EP - 464
JO - Annual Reviews in Control
JF - Annual Reviews in Control
ER -