| Título |
Data-driven methods for present and future pandemics: Monitoring, modelling and managing |
| Autores |
Alamo T. , G. Reina D. , MILLÁN GATA, PABLO, Preciado V.M. , Giordano G. |
| Publicación externa |
No |
| Medio |
Annu Rev Control |
| Alcance |
Review |
| Naturaleza |
Científica |
| Cuartil JCR |
1 |
| Cuartil SJR |
1 |
| Impacto JCR |
10.69900 |
| Impacto SJR |
3.74200 |
| Web |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108971162&doi=10.1016%2fj.arcontrol.2021.05.003&partnerID=40&md5=c11a4c44019c36b1e743b0faaf866bbe |
| Fecha de publicacion |
01/01/2021 |
| ISI |
000734449000024 |
| Scopus Id |
2-s2.0-85108971162 |
| DOI |
10.1016/j.arcontrol.2021.05.003 |
| 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. © 2021 The Authors |
| Palabras clave |
Data Science; Data-driven methods; Holistic approach; Infectious disease; Modelling and controls; Modelling and forecasting; Survey analysis; Systems and control theory; Theoretical approach; Disease |
| Miembros de la Universidad Loyola |
|
Gestionar cookies