MODELLING PRESYMPTOMATIC INFECTIOUSNESS IN COVID-19

Cheng, R.; Dye, C.; Dagpunar, J.; Williams, B.

This paper considers SEPIR, the extension of an existing parametric SEIR continuous simulation compartment model. Both models can be fitted to real data as they include parameters that can simply be estimated from the data. However SEPIR deploys an additional presymptomatic (also called asymptomatic) infectious stage that is not included in SEIR but which is known to exist in COVID-19. This stage is also parametrised and so can be fitted to data. Both SEPIR and the existing SEIR model assume a homogeneous mixing population, an idealisation that is unrealistic in practice when dynamically varying control strategies are deployed against virus. This means that if either model is to represent more than just a single period in the behaviour of the epidemic, then the parameters of the model will have to be time dependent. This issue is also discussed in this paper. A numerical example using Swiss observational data is given.