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The COVID-19 epidemic is the last of a long list of pandemics that haveaffected humankind in the last century. In this paper, we propose a novelmathematical epidemiological model named SUIHTER from the names of the sevencompartments that it comprises: susceptible uninfected individuals (S),undetected (both asymptomatic and symptomatic) infected (U), isolated (I),hospitalized (H), threatened (T), extinct (E), and recovered (R). A suitableparameter calibration that is based on the combined use of least squares methodand Markov Chain Monte Carlo (MCMC) method is proposed with the aim ofreproducing the past history of the epidemic in Italy, surfaced in lateFebruary and still ongoing to date, and of validating SUIHTER in terms of itspredicting capabilities. A distinctive feature of the new model is that itallows a one-to-one calibration strategy between the model compartments and thedata that are daily made available from the Italian Civil Protection. The newmodel is then applied to the analysis of the Italian epidemic with emphasis onthe second outbreak emerged in Fall 2020. In particular, we show that theepidemiological model SUIHTER can be suitably used in a predictive manner toperform scenario analysis at national level.

SUIHTER: A new mathematical model for COVID-19. Application to the analysis of the second epidemic outbreak in Italy