- Ignazio Lazzizzera: Department of Physics, University of Trento, via Sommarive 14, Povo (Trento), Italy. ORCID
In this work, the SIR epidemiological model is reformulated so to highlight the important , as well as to account for the , the inverse of the , and the (or ), the inverse of the . The aim is to check whether the relationships the model poses among the various observables are actually found in the data. The study case of the second through the third wave of the Covid-19 pandemic in Italy is taken. Given its scale invariance, initially the model is tested with reference to the curve of swab-confirmed infectious individuals only. It is found to match the data, if the curve of the (that is healed or deceased) individuals is assumed underestimated by a factor of about 3 together with other related curves. Contextually, the and the , as well as the , are obtained fitting the SIR equations to the data; the outcomes prove to be in good agreement with those of other works. Then, using knowledge of the proportion of Covid-19 transmissions likely occurring from individuals who didn't develop symptoms, thus mainly undetected, an estimate of the of the epidemic is obtained, looking also in good agreement with results from other, completely different works. The line of this work is new, and the procedures, computationally really inexpensive, can be applied to any other national or regional case besides Italy's study case here.