We propose a way to model the underdetection of infected and removed individuals in a compartmental model for estimating the COVID-19 epidemic. The proposed approach is demonstrated on a stochastic SIR model, specified as a system of stochastic differential equations, to analyse data from the Italian COVID-19 epidemic. We find that a correct assessment of the amount of underdetection is important to obtain reliable estimates of the critical model parameters. The adaptation of the model in each time interval between relevant government decrees implementing contagion mitigation measures provides short-term predictions and a continuously updated assessment of the basic reproduction number.
Keywords: Basic reproduction number; Identifiability; Infection fatality rate; State-space SDE; Susceptible-infected-removed; Underdetection.
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021.