[IPOL announce] new article: SEAIR Framework Accounting for a Personalized Risk Prediction Score: Application to the Covid-19 Epidemic

[announcements about the IPOL journal]

A new article is available in IPOL: http://www.ipol.im/pub/art/2020/305/

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


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.