Author: Giovani L. Vasconcelos; Antônio M. S. Macêdo; Raydonal Ospina; Francisco A. G. Almeida; Gerson C. Duarte-Filho; Inês C. L. Souza
Title: Modelling fatality curves of COVID-19 and the effectiveness of intervention strategies Document date: 2020_4_6
ID: 35b3efom_11
Snippet: where C(t) is the cumulative number of cases at time t, r is the growth rate at the early stage, K is the final epidemic size, and the parameter α measures the asymmetry with respect to the s-shaped dynamics of the standard logistic model, which is recovered for α = 1. It is worth to point out that the Richards model has an intrinsic connection to the SIR epidemic model, see, e.g., [20] , with the advantage that it allows for an exact solution .....
Document: where C(t) is the cumulative number of cases at time t, r is the growth rate at the early stage, K is the final epidemic size, and the parameter α measures the asymmetry with respect to the s-shaped dynamics of the standard logistic model, which is recovered for α = 1. It is worth to point out that the Richards model has an intrinsic connection to the SIR epidemic model, see, e.g., [20] , with the advantage that it allows for an exact solution (see below), which makes the data analysis much simpler. As already mentioned, here we shall apply the RGM to the fatality curves of COVID-19, so that C(t) will represent the cumulative numbers of deaths in a given country at time t, where t will be counted in days from the first death. Equation (1) must be supplemented with a boundary condition. Here it is convenient to choose
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