A new regression model for the forecasting of COVID-19 outbreak evolution: an application to Italian data. Issue 1 (2nd January 2022)

• Record Type: Journal Article
• Authors: Sisti, Davide; Rocchi, Ettore; Peluso, Sara; Amatori, Stefano; Carletti, Margherita
• Is Part Of: Biostatistics & epidemiology. Volume 6:Issue 1(2022)
• Page Start: 48
• Page End: 56
• Publication Date: 2022-01-02
• Subjects: Novel coronavirus (SARS-CoV-2) -- COVID-19 -- curve growing -- epidemic modeling -- forecasting; Epidemiology -- Periodicals; Biometry -- Periodicals; 614.42
• DOI: 10.1080/24709360.2021.1978270
• Languages: English
• ISSNs: 2470-9360

Abstract

Abstract : The novel coronavirus SARS-CoV-2 was first identified in China in December 2019. In just over five months, the virus affected over 4 million people and caused about 300, 000 deaths. This study aimed to model new COVID-19 cases in Italian regions using a new curve. A new empirical curve is proposed to model the number of new cases of COVID-19. It resembles a known exponential growth curve, which has a straight line as an exponent, but in the growth curve proposed, the exponent is a logistic curve multiplied for a straight line. This curve shows an initial phase, the expected exponential growth, then rises to the maximum value and finally reaches zero. We characterized the epidemic growth patterns for the entire Italian nation and each of the 20 Italian regions. The estimated growth curve has been used to calculate the expected time of the beginning, the time related to peak, and the end of the epidemics. Our analysis explores the development of the outbreaks in Italy and the impact of the containment measures. Data obtained are useful to forecast future scenarios and the possible end of the epidemic.