An SEAIR model with personalised risk prediction scores and application to the Covid-19 epidemic
Oliver Boulant, Theodoros Evgeniou, Mathilde Fekom, Anton Ovchinnikov, Raphaël Porcher, Camille Pouchol, Nicolas Vayatis
⚠ This is a preprint. It may change before it is accepted for publication.


The aim of the present work is to provide an SEAIR framework which takes a personalised risk prediction score as an additional input. Each individual is categorised depending on his actual status with respect to the disease - mild 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 taking into account the discriminative capacity of the risk score 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.