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A new regression model for the forecasting of COVID-19 outbreak evolution: an application to Italian data

A new regression model for the forecasting of COVID-19 outbreak evolution: an application to... 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Biostatistics & Epidemiology Taylor & Francis

A new regression model for the forecasting of COVID-19 outbreak evolution: an application to Italian data

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A new regression model for the forecasting of COVID-19 outbreak evolution: an application to Italian data

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...
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Publisher
Taylor & Francis
Copyright
© 2021 International Biometric Society – Chinese Region
ISSN
2470-9379
eISSN
2470-9360
DOI
10.1080/24709360.2021.1978270
Publisher site
See Article on Publisher Site

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.

Journal

Biostatistics & Epidemiology – Taylor & Francis

Published: Jan 2, 2022

Keywords: Novel coronavirus (SARS-CoV-2); COVID-19; curve growing; epidemic modeling; forecasting

References

  • The SARS, MERS and novel coronavirus (COVID-19) epidemics, the newest and biggest global health threats: what lessons have we learned?
  • Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China
  • Forecasting epidemiological and evolutionary dynamics of infectious diseases
  • The Western Africa ebola virus disease epidemic exhibits both global exponential and local polynomial growth rates
  • A generalized-growth model to characterize the early ascending phase of infectious disease outbreaks
  • A new real-time PCR method to overcome significant quantitative inaccuracy due to slight amplification inhibition