Modelling the downhill of the Sars-Cov-2 in Italy and a general forecast for a future epidemic of the virus

TLDR: In this article, a generalized logistic model was proposed to capture both the peaks of the total infected and the deaths of the Sars-Cov-2 epidemic in the time window February 24-April 1.

Abstract: In a previous article [1] we have described the temporal evolution of the Sars-Cov-2 in Italy in the time window February 24-April 1. As we can see in [1] a generalized logistic equation captures both the peaks of the total infected and the deaths. In this article our goal is to study the missing peak, i.e. the currently infected one (or total currently positive). After the April 7 the large increase in the number of swabs meant that the logistical behavior of the infected curve no longer worked. So we decided to generalize the model, introducing new parameters. Moreover, we adopt a similar approach used in [1] (for the estimation of deaths) in order to evaluate the recoveries. In this way, introducing a simple conservation law, we define a model with 4 populations: total infected, currently positives, recoveries and deaths. Therefore, we propose an alternative method to a classical SIRD model for the evaluation of the Sars-Cov-2 epidemic. However, the method is general and thus applicable to other diseases. Finally we study the behavior of the ratio infected over swabs, and we show as studying this parameter we recover the generalized Logistic model used in [1]. We think that this trend could be useful for a future epidemic of this coronavirus.