SEAIR Framework Accounting for a Personalized Risk Prediction Score: Application to the Covid-19 Epidemic
Oliver Boulant, Mathilde Fekom, Camille Pouchol, Theodoros Evgeniou, Anton Ovchinnikov, Raphaël Porcher, Nicolas Vayatis
→ BibTeX
@article{ipol.2020.305,
    title   = {{SEAIR Framework Accounting for a Personalized Risk Prediction Score: Application to the Covid-19 Epidemic}},
    author  = {Boulant, Oliver and Fekom, Mathilde and Pouchol, Camille and Evgeniou, Theodoros and Ovchinnikov, Anton and Porcher, Raphaël and Vayatis, Nicolas},
    journal = {{Image Processing On Line}},
    volume  = {10},
    pages   = {150--166},
    year    = {2020},
    doi     = {10.5201/ipol.2020.305},
}
% if your bibliography style doesn't support doi fields:
    note    = {\url{https://doi.org/10.5201/ipol.2020.305}}
published
2020-11-14
reference
Oliver Boulant, Mathilde Fekom, Camille Pouchol, Theodoros Evgeniou, Anton Ovchinnikov, Raphaël Porcher, and Nicolas Vayatis, SEAIR Framework Accounting for a Personalized Risk Prediction Score: Application to the Covid-19 Epidemic, Image Processing On Line, 10 (2020), pp. 150–166. https://doi.org/10.5201/ipol.2020.305

Communicated by Gregory Randall
Demo edited by Olivier Boulant

Abstract

The aim of the present work is to provide an SEAIR framework which takes a personalized risk prediction score as an additional input. Each individual is categorized depending on his actual status with respect to the disease - moderate or severe symptoms -, and the level of risk predicted - low or high. This idea leads to a 4-fold extension of the ODE model in classical SEAIR. This model offers the possibility for policy-makers to explore differentiated containment strategies, by varying sizes for the low risk segment and varying dates for 'progressive release' of the population, while exploring the discriminative capacity of the risk score, for instance through its AUC. Differential contact rates for low-risk/high-risk compartments are also included in the model. The demo allows to select contact rates and time-depending exit strategies. The hard-coded parameters correspond to the data for the Covid-19 epidemic in France, and the risk refers to the probability of being admitted in ICU upon infection. Some examples of simulations are provided.

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