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Data-driven Simulation and Optimization for Covid-19 Exit Strategies
Data-driven Simulation and Optimization for Covid-19 Exit Strategies
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.
- University of Luxembourg Luxembourg
- UNIVERSITE DU LUXEMBOURG
Microsoft Academic Graph classification: 2019-20 coronavirus outbreak Coronavirus disease 2019 (COVID-19) Computer science Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Data-driven Order (exchange) Pandemic Economic impact analysis Set (psychology) Estimation Exit strategy Lift (data mining) Outbreak Risk analysis (engineering)
FOS: Computer and information sciences, Physics - Physics and Society, seir, FOS: Physical sciences, Physics and Society (physics.soc-ph), covid19, Computer Science - Computers and Society, exit strategies, Computers and Society (cs.CY), search-based optimization, Quantitative Biology - Populations and Evolution, : Computer science [C05] [Engineering, computing & technology], pandemic, Populations and Evolution (q-bio.PE), deep learning, : Sciences informatiques [C05] [Ingénierie, informatique & technologie], FOS: Biological sciences
FOS: Computer and information sciences, Physics - Physics and Society, seir, FOS: Physical sciences, Physics and Society (physics.soc-ph), covid19, Computer Science - Computers and Society, exit strategies, Computers and Society (cs.CY), search-based optimization, Quantitative Biology - Populations and Evolution, : Computer science [C05] [Engineering, computing & technology], pandemic, Populations and Evolution (q-bio.PE), deep learning, : Sciences informatiques [C05] [Ingénierie, informatique & technologie], FOS: Biological sciences
Microsoft Academic Graph classification: 2019-20 coronavirus outbreak Coronavirus disease 2019 (COVID-19) Computer science Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Data-driven Order (exchange) Pandemic Economic impact analysis Set (psychology) Estimation Exit strategy Lift (data mining) Outbreak Risk analysis (engineering)
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[1] Sina F Ardabili, Amir Mosavi, Pedram Ghamisi, Filip Ferdinand, Annamaria R Varkonyi-Koczy, Uwe Reuter, Timon Rabczuk, and Peter M Atkinson. 2020. Covid-19 outbreak prediction with machine learning. Available at SSRN 3580188 (2020).
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[4] Fumito Koike and Nobuo Morimoto. 2018. Supervised forecasting of the range expansion of novel non-indigenous organisms: Alien pest organisms and the 2009 H1N1 flu pandemic. Global Ecology and Biogeography (04 2018).
[5] Ying Liu, Albert A. Gayle, Annelies Wilder-Smith, and Joacim Rocklöv. 2020. The reproductive number of COVID-19 is higher compared to SARS coronavirus. Journal of Travel Medicine 27, 2 (mar 2020), 1-4. [OpenAIRE]
[6] Esteban Ortiz-Ospina Max Roser, Hannah Ritchie and Joe Hasell. 2020. Coronavirus Pandemic (COVID-19). Our World in Data (2020). https://ourworldindata.org/coronavirus.
[7] Christoph Molnar. 2019. Interpretable Machine Learning.
[8] Olav Titus Muurlink, Peter Stephenson, Mohammad Zahirul Islam, and Andrew W Taylor-Robinson. 2018. Long-term predictors of dengue outbreaks in Bangladesh: A data mining approach. Infectious Disease Modelling 3 (2018), 322 - 330.
[9] Gaurav Pandey, Poonam Chaudhary, Rajan Gupta, and Saibal Pal. 2020. SEIR and Regression Model based COVID-19 outbreak predictions in India. arXiv preprint (2020).
[10] T. Smith, N. Maire, A. Ross, M. Penny, N. Chitnis, A. Schapira, A. Studer, B. Genton, C. Lengeler, F. Tediosi, and et al. 2008. Towards a comprehensive simulation model of malaria epidemiology and control. Parasitology 135, 13 (2008), 1507-1516.
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Citations provided by BIP!Popularity provided by BIP!- University of Luxembourg Luxembourg
- UNIVERSITE DU LUXEMBOURG
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.