tetano
Editor, Senior Moderator
Biology (Basel)
. 2021 Apr 8;10(4):311.
doi: 10.3390/biology10040311.
A Retrospective Analysis of the COVID-19 Pandemic Evolution in Italy
Anna Fochesato[SUP] 1 2 [/SUP], Giulia Simoni[SUP] 1 [/SUP], Federico Reali[SUP] 1 [/SUP], Giulia Giordano[SUP] 3 [/SUP], Enrico Domenici[SUP] 1 4 [/SUP], Luca Marchetti[SUP] 1 [/SUP]
Affiliations
Abstract
Late 2019 saw the outbreak of COVID-19, a respiratory disease caused by the new coronavirus SARS-CoV-2, which rapidly turned into a pandemic, killing more than 2.77 million people and infecting more than 126 million as of late March 2021. Daily collected data on infection cases and hospitalizations informed decision makers on the ongoing pandemic emergency, enabling the design of diversified countermeasures, from behavioral policies to full lockdowns, to curb the virus spread. In this context, mechanistic models could represent valuable tools to optimize the timing and stringency of interventions, and to reveal non-trivial properties of the pandemic dynamics that could improve the design of suitable guidelines for future epidemics. We performed a retrospective analysis of the Italian epidemic evolution up to mid-December 2020 to gain insight into the main characteristics of the original strain of SARS-CoV-2, prior to the emergence of new mutations and the vaccination campaign. We defined a time-varying optimization procedure to calibrate a refined version of the SIDARTHE (Susceptible, Infected, Diagnosed, Ailing, Recognized, Threatened, Healed, Extinct) model and hence accurately reconstruct the epidemic trajectory. We then derived additional features of the COVID-19 pandemic in Italy not directly retrievable from reported data, such as the estimate of the day zero of infection in late November 2019 and the estimate of the spread of undetected infection. The present analysis contributes to a better understanding of the past pandemic waves, confirming the importance of epidemiological modeling to support an informed policy design against epidemics to come.
Keywords: COVID-19; SIDARTHE model; computational models; dise
. 2021 Apr 8;10(4):311.
doi: 10.3390/biology10040311.
A Retrospective Analysis of the COVID-19 Pandemic Evolution in Italy
Anna Fochesato[SUP] 1 2 [/SUP], Giulia Simoni[SUP] 1 [/SUP], Federico Reali[SUP] 1 [/SUP], Giulia Giordano[SUP] 3 [/SUP], Enrico Domenici[SUP] 1 4 [/SUP], Luca Marchetti[SUP] 1 [/SUP]
Affiliations
- PMID: 33917920
- DOI: 10.3390/biology10040311
Abstract
Late 2019 saw the outbreak of COVID-19, a respiratory disease caused by the new coronavirus SARS-CoV-2, which rapidly turned into a pandemic, killing more than 2.77 million people and infecting more than 126 million as of late March 2021. Daily collected data on infection cases and hospitalizations informed decision makers on the ongoing pandemic emergency, enabling the design of diversified countermeasures, from behavioral policies to full lockdowns, to curb the virus spread. In this context, mechanistic models could represent valuable tools to optimize the timing and stringency of interventions, and to reveal non-trivial properties of the pandemic dynamics that could improve the design of suitable guidelines for future epidemics. We performed a retrospective analysis of the Italian epidemic evolution up to mid-December 2020 to gain insight into the main characteristics of the original strain of SARS-CoV-2, prior to the emergence of new mutations and the vaccination campaign. We defined a time-varying optimization procedure to calibrate a refined version of the SIDARTHE (Susceptible, Infected, Diagnosed, Ailing, Recognized, Threatened, Healed, Extinct) model and hence accurately reconstruct the epidemic trajectory. We then derived additional features of the COVID-19 pandemic in Italy not directly retrievable from reported data, such as the estimate of the day zero of infection in late November 2019 and the estimate of the spread of undetected infection. The present analysis contributes to a better understanding of the past pandemic waves, confirming the importance of epidemiological modeling to support an informed policy design against epidemics to come.
Keywords: COVID-19; SIDARTHE model; computational models; dise