Your browser doesn't support javascript.
Prediction of evolution of the second wave of Covid-19 pandemic in Italy (preprint)
medrxiv; 2020.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2020.11.24.20238139
ABSTRACT
A relevant problem in the study of the Covid-19 pandemic is the study of its temporal evolution. Such evolution depends on a number of factors, among which the average rate of contacts between susceptible and infected individuals, the duration of infectiousness and the transmissibility, that is the probability of infection after a contact between susceptible and infected individuals. In a previous study, we analyzed the potentiality of a number of distributions to describe the evolution of the pandemic and the potentiality of each distribution to mathematically predict the evolution of the pandemic in Italy. Since the number of daily tests was changing and increasing with time, we used the ratio of the new daily cases per swab. We considered distributions of the type of Gauss (normal), Gamma, Beta, Weibull, Lognormal and in addition of the type of the Planck blackbody radiation law. The Planck law, describing the amount of energy of the electromagnetic radiation emitted by a black body at each wavelength or at each frequency, marked in 1900 the beginning of Quantum Mechanics. The result of our analysis was that, among the considered distributions, the Planck law has the best potentiality to mathematically predict the evolution of the pandemic and the best fitting capability. In this paper, we analyze the time evolution of this second wave of Covid-19 pandemic in Italy. In this study there is also an attempt to account for the effects of the governmental containment measures.
Subject(s)

Full text: Available Collection: Preprints Database: medRxiv Main subject: Radiation Injuries / COVID-19 Language: English Year: 2020 Document Type: Preprint

Similar

MEDLINE

...
LILACS

LIS


Full text: Available Collection: Preprints Database: medRxiv Main subject: Radiation Injuries / COVID-19 Language: English Year: 2020 Document Type: Preprint