SEAIR Framework Accounting for a Personalized Risk Prediction Score: Application to the Covid-19 Epidemic

Boulant, Olivier; Fekom, Mathilde; Pouchol, Camille; Evgeniou, Theodoros; Овчінніков, Антон; Porcher, Raphaël; Vayatis, Nicolas

doi:10.5201/ipol.2020.305

Cited by 6 publications

(8 citation statements)

References 4 publications

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“…The full set of ODEs and the algorithmic details of their solution are presented in the “code appendix”, Boulant et al. ( 2020 ).…”

Section: Modelmentioning

confidence: 99%

“…First, given the large body of literature developed at the beginning of the COVID-19 pandemic, when multiple research articles were produced daily, we also present the detailed review in a "literature appendix", Garin et al (2021) in addition to the papers discussed above. Second, because our findings are based on numerical simulations, the code is available via GitHub at https://reine.cmla.ens-cachan.fr/boulant/seair, and the algorithmic details for how our model is implemented in the code are provided in Boulant et al (2020).…”

Section: Production and Operations Managementmentioning

confidence: 99%

“…Second, because our findings are based on numerical simulations, the code is available via GitHub at https://reine.cmla.ens‐cachan.fr/boulant/seair , and the algorithmic details for how our model is implemented in the code are provided in Boulant et al. ( 2020 ). This forms a “code appendix” to our paper, following the highest standards of reproducible research.…”

Section: Introductionmentioning

confidence: 99%

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Pandemic lockdown, isolation, and exit policies based on machine learning predictions

Evgeniou

Fekom

Овчінніков

et al. 2023

Production and Operations Management

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The widespread lockdowns imposed in many countries at the beginning of the COVID‐19 pandemic elevated the importance of research on pandemic management when medical solutions such as vaccines are unavailable. We present a framework that combines a standard epidemiological SEIR (susceptible–exposed–infected–removed) model with an equally standard machine learning classification model for clinical severity risk, defined as an individual's risk of needing intensive care unit (ICU) treatment if infected. Using COVID‐19–related data and estimates for France as of spring 2020, we then simulate isolation and exit policies. Our simulations show that policies considering clinical risk predictions could relax isolation restrictions for millions of the lowest risk population months earlier while consistently abiding by ICU capacity restrictions. Exit policies without risk predictions, meanwhile, would considerably exceed ICU capacity or require the isolation of a substantial portion of population for over a year in order to not overwhelm the medical system. Sensitivity analyses further decompose the impact of various elements of our models on the observed effects. Our work indicates that predictive modeling based on machine learning and artificial intelligence could bring significant value to managing pandemics. Such a strategy, however, requires governments to develop policies and invest in infrastructure to operationalize personalized isolation and exit policies based on risk predictions at scale. This includes health data policies to train predictive models and apply them to all residents, as well as policies for targeted resource allocation to maintain strict isolation for high‐risk individuals.

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“…The full set of ODEs and the algorithmic details of their solution are presented in the “code appendix”, Boulant et al. ( 2020 ).…”

Section: Modelmentioning

confidence: 99%

Section: Production and Operations Managementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Pandemic lockdown, isolation, and exit policies based on machine learning predictions

Evgeniou

Fekom

Овчінніков

et al. 2023

Production and Operations Management

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“…The full set of ODEs and the algorithmic details of their solution are presented in the "code appendix" Boulant et al (2020).…”

Section: Compartmentalized Epidemiological Model: Extended Seairmentioning

confidence: 99%

“…First, given the large body of literature developed at the beginning of the COVID-19 pandemic, when multiple research articles were produced daily, we also present the detailed review in a "literature appendix" Garin et al (2021) in addition to the papers discussed above. Second, because our findings are based on numerical simulations, the code is available via GitHub at https://reine.cmla.ens-cachan.fr/boulant/seair, and the algorithmic details for how our model is implemented in the code are provided in Boulant et al (2020). This forms a "code appendix" to our paper, following the highest standards of reproducible research.…”

Section: Introductionmentioning

confidence: 99%

Pandemic Lock-down, Isolation, and Exit Policies Based on Machine Learning Predictions

Evgeniou

Fekom

Овчінніков

et al. 2020

Preprint

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Background: In early May 2020, following social distancing measures due to COVID-19, governments consider relaxing lock-down. We combined individual clinical risk predictions with epidemic modelling to examine simulations of riskbased differential isolation and exit policies. Methods:We extended a standard susceptible-exposed-infected-removed (SEIR) model to account for personalised predictions of severity, defined by the risk of an individual needing intensive care if infected, and simulated differential isolation policies using COVID-19 data and estimates in France as of early May 2020. We also performed sensitivity analyses. The framework may be used with other epidemic models, with other risk predictions, and for other epidemic outbreaks.Findings: Simulations indicated that, assuming everything else the same, an exit policy considering clinical risk predictions starting on May 11, as planned by the French government, could enable to immediately relax restrictions for an extra 10% (6 700 000 people) or more of the lowest-risk population, and con- * Corresponding author

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Predicting the dynamical behavior of COVID-19 epidemic and the effect of control strategies

Shakhany

Salimifard

2021

Chaos, Solitons & Fractals

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SEAIR Framework Accounting for a Personalized Risk Prediction Score: Application to the Covid-19 Epidemic

Cited by 6 publications

References 4 publications

Pandemic lockdown, isolation, and exit policies based on machine learning predictions

Pandemic lockdown, isolation, and exit policies based on machine learning predictions

Pandemic Lock-down, Isolation, and Exit Policies Based on Machine Learning Predictions

Predicting the dynamical behavior of COVID-19 epidemic and the effect of control strategies

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