2021
DOI: 10.1371/journal.pone.0250029
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The first wave of the SARS-CoV-2 epidemic in Tuscany (Italy): A SI2R2D compartmental model with uncertainty evaluation

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 (February-June 2020), we define a compartmental model that accounts for both detected and undetected infections and assumes that only notified cases can die. We estimate the infection fatality rate, the case fatality rate, 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 f… Show more

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Cited by 10 publications
(4 citation statements)
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“…The value p = 1.15% is the IFR estimate reported for the upper-income countries by the Imperial College COVID-19 response team (27). It is also consistent with the value of 1.14% estimated for Italy by the Italian Institute for International Political Studies (28) and used in a previous paper by the authors (29). Regarding T, the value of 14 days is in line with both the median time from symptoms onset to death reported by ISS for Italy (12 days) (30) and the estimated average time from infection onset to recovery of 13.4 days arisen from a meta-analysis (31).…”
Section: Regional Sird Modelssupporting
confidence: 89%
See 1 more Smart Citation
“…The value p = 1.15% is the IFR estimate reported for the upper-income countries by the Imperial College COVID-19 response team (27). It is also consistent with the value of 1.14% estimated for Italy by the Italian Institute for International Political Studies (28) and used in a previous paper by the authors (29). Regarding T, the value of 14 days is in line with both the median time from symptoms onset to death reported by ISS for Italy (12 days) (30) and the estimated average time from infection onset to recovery of 13.4 days arisen from a meta-analysis (31).…”
Section: Regional Sird Modelssupporting
confidence: 89%
“…We calibrated the SIRD models only on the observed COVID-19-related deaths, without exploiting the availability of other observed time series, like the ones of notified infected. Calibrating on the notified infected would have required the formulation of a more complex compartmental model with separate compartments for detected and undetected cases and the introduction of additional unknown transition parameters (2)(3)(4)29). On the other hand, this more complex model would have also allowed us to take into account the fact that, once detected and isolated, the infected individuals spread the contagion less than the undetected ones.…”
Section: Study Limitationsmentioning
confidence: 99%
“…Italy was the first European country to be severely affected by a high incidence of COVID-19 cases ( 1 , 2 ). During the first wave, from 1st March to 31 May 2020, Italy registered 227,972 cases and 34,079 deaths ( 3 ).…”
Section: Introductionmentioning
confidence: 99%
“…While some of the transition parameters in the model were assumed as fixed, we estimated via a two-step calibration the age-specific probabilities of starting and quitting smoking, modelled in a flexible way through cubic regression splines [27], the probability of relapsing smoking, modelled as a nonlinear function of the time from quitting [28], and the mortality rate. We calibrated the model on the observed prevalence of never, current, and former smokers for the years from 1993 to 2019, arising from yearly local surveys.…”
mentioning
confidence: 99%