Tag: parametric boostrap


A SIRD model calibrated on deaths to investigate the second wave of the SARS-CoV-2 epidemic in Italy

Publication date: 05/12/2020 – E&P Code: repo.epiprev.it/2052
Authors: Giulia Cereda1, Cecilia Viscardi1, Luca Gherardini1, Fabrizia Mealli1, Michela Baccini1

Abstract: After the SARS-CoV-2 outbreak during the 2020 spring, Italy faced a second epidemic wave during the autumn. Using a SIRD model calibrated on the COVID19-related deaths, we describe the epidemic dynamics from August 1st to November 30th 2020, region by region. We explore the behavior of the contagion in terms of time-varying reproductive number R0 and estimated number of circulating infections. This number, when compared to the number of notified positives, provides an evaluation of the submerged portion of contagion. The results indicate that in Italy, during the second SARS-CoV-2 epidemic wave, the reproductive number changed over time heterogeneously across regions, but with some important common elements including a mid-October peak and a decline during the month of November, which are visible in most regions. Ad hoc studies should be performed to investigate the causal effect that specific events (e.g. schools reopening, regional elections) and restrictions of different degree have had on inflating or deflating the rate of contagion. Despite the decline of R0(t) in most regions, the prevalence of circulating infections estimated at the end of the study period is not negligible, in particular in the North of the country. This suggests that even small future increases of R0(t) may lead in a short time to levels of contagion spread that could be unsustainable, depending on the regional supply of hospital and intensive care unit beds and the organizations of healthcare services throughout the territory.

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The first wave of the SARS-CoV-2 epidemic in Tuscany (Italy): a SI²R²D compartmental model with uncertainty evaluation

Publication date: 29/10/2020 – E&P Code: repo.epiprev.it/2003
Authors: Michela Baccini1, Giulia Cereda1, Cecilia Viscardi1

Abstract: With the aim of studying the spread of the SARS-CoV-2 infection in the Tuscany region of Italy during the first epidemic wave (from February to June 2020), we define a compartmental model which accounts for both detected and undetected infections and assumes that only notified cases can die. We estimate the initial infection and case fatality rates and the basic reproduction number, modeled as a time-varying function, by calibrating on the cumulative daily number of observed deaths and notified infected, after fixing to plausible values the other model parameters to assure identifiability. The uncertainty of the estimates is quantified by a parametric bootstrap procedure and a Global Sensitivity Analysis (GSA) based on the Sobol’s variance decomposition is performed to assess the sensitivity of the estimates to changes in the values of the fixed parameters. According to our results, the basic reproduction number drops from an initial value of 6.055 to 0 at the end of the national lockdown, then it grows again, but remaining under 1. At the beginning of the epidemic, the case and the infection fatality rates are estimated to be 13.1% and 2.3%, respectively. Among the parameters considered as fixed, the average time from infection to recovery for the not notified infected appears to be the most impacting one on the model estimates. The probability for an infected to be notified has a relevant impact on the infection fatality rate and on the shape of the epidemic curve. This stresses the need of collecting information on these parameters to better understand the phenomenon and get reliable predictions.

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