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The presence of a large number of infected individuals with few or nosymptoms is an important epidemiological difficulty and the main mathematicalfeature of COVID-19. The A-SIR model, i.e. a SIR (Susceptible-Infected-Removed)model with a compartment for infected individuals with no symptoms or fewsymptoms was proposed by Giuseppe Gaeta, arXiv:2003.08720 [q-bio.PE] (2020). Inthis paper we investigate a slightly generalized version of the same model andpropose a scheme for fitting the parameters of the model to real data using thetime series only of the deceased individuals. The scheme is applied to theconcrete cases of Lombardy, Italy and S\~ao Paulo state, Brazil, showingdifferent aspects of the epidemics. For each case we show that we may have goodfits to the data up to the present, but with very large differences in thefuture behavior. The reasons behind such disparate outcomes are the uncertaintyon the value of a key parameter, the probability that an infected individual isfully symptomatic, and on the intensity of the social distancing measuresadopted. This conclusion enforces the necessity of trying to determine the realnumber of infected individuals in a population, symptomatic or asymptomatic.

Predicting the evolution of the COVID-19 epidemic with the A-SIR model: Lombardy, Italy and S\~ao Paulo state, Brazil