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Data-driven simulation and optimization for covid-19 exit strategies
Ghamizi, Salah; Rwemalika, Renaud; Cordy, Maxime et al.
2020In Ghamizi, Salah; Rwemalika, Renaud; Cordy, Maxime et al. (Eds.) Data-driven simulation and optimization for covid-19 exit strategies
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Keywords :
search-based optimization; exit strategies; deep learning; covid19; pandemic; seir
Abstract :
[en] The rapid spread of the Coronavirus SARS-2 is a major challenge that led almost all governments worldwide to take drastic measures to respond to the tragedy. Chief among those measures is the massive lockdown of entire countries and cities, which beyond its global economic impact has created some deep social and psychological tensions within populations. While the adopted mitigation measures (including the lockdown) have generally proven useful, policymakers are now facing a critical question: how and when to lift the mitigation measures? A carefully-planned exit strategy is indeed necessary to recover from the pandemic without risking a new outbreak. Classically, exit strategies rely on mathematical modeling to predict the effect of public health interventions. Such models are unfortunately known to be sensitive to some key parameters, which are usually set based on rules-of-thumb.In this paper, we propose to augment epidemiological forecasting with actual data-driven models that will learn to fine-tune predictions for different contexts (e.g., per country). We have therefore built a pandemic simulation and forecasting toolkit that combines a deep learning estimation of the epidemiological parameters of the disease in order to predict the cases and deaths, and a genetic algorithm component searching for optimal trade-offs/policies between constraints and objectives set by decision-makers.Replaying pandemic evolution in various countries, we experimentally show that our approach yields predictions with much lower error rates than pure epidemiological models in 75% of the cases and achieves a 95% R² score when the learning is transferred and tested on unseen countries. When used for forecasting, this approach provides actionable insights into the impact of individual measures and strategies.
Research center :
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Security Design and Validation Research Group (SerVal)
Disciplines :
Computer science
Author, co-author :
Ghamizi, Salah ;  University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS)
Rwemalika, Renaud ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SerVal
Cordy, Maxime  ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SerVal
Veiber, Lisa ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > TruX
Bissyande, Tegawendé François D Assise  ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > TruX
Papadakis, Mike ;  University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS)
Klein, Jacques ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > TruX
Le Traon, Yves ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SerVal
External co-authors :
no
Language :
English
Title :
Data-driven simulation and optimization for covid-19 exit strategies
Publication date :
August 2020
Event name :
Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
Event organizer :
The Association for Computing Machinery's Special Interest Group on Knowledge Discovery and Data Mining
Event place :
San Diego (Online), United States
Event date :
from 23-08-2020 to 27-08-2020
Audience :
International
Main work title :
Data-driven simulation and optimization for covid-19 exit strategies
Publisher :
Association for Computing Machinery, New York, NY, United States
ISBN/EAN :
9781450379984
Collection name :
Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
Pages :
3434-3442
Peer reviewed :
Peer reviewed
Focus Area :
Computational Sciences
Name of the research project :
COVID-19/2020-2/14863123/PILOT/LeTraon
Funders :
FNR - Fonds National de la Recherche [LU]
Available on ORBilu :
since 20 January 2021

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