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Claudio Bagaini

Publications and source records attributed to Claudio Bagaini.

2 recordsLinked to original sources

Representations for multidimensional down-down deconvolution of ocean-bottom seismic data: theory and practical implications

Multidimensional up-down deconvolution effectively eliminates surface-related multiples from ocean-bottom seismic data. Recently, several down-down deconvolution methods have been introduced as attractive alternatives. Whereas multidimensional up-down deconvolution fully accounts for lateral variations of the medium parameters, the underlying theory of some of the down-down deconvolution methods is essentially based on the assumption that the medium is horizontally layered. Using reciprocity theory, this assumption is circumvented. This leads to representations for either receiver-side or source-side multidimensional down-down deconvolution. Compared with multidimensional up-down deconvolution, receiver-side down-down deconvolution only utilizes the downgoing part of the wavefield that better samples the shallow subsurface, but it is not entirely data-driven. Source-side down-down deconvolution benefits from the better sampled source array, but in the presence of sparsely sampled receivers it requires solving an underdetermined system of linear equations.

physics.geo-ph

A simulation of a COVID-19 epidemic based on a deterministic SEIR model

An epidemic disease caused by a new coronavirus has spread in Northern Italy with a strong contagion rate. We implement an SEIR model to compute the infected population and number of casualties of this epidemic. The example may ideally regard the situation in the Italian Region of Lombardy, where the epidemic started on February 25. We calibrate the model with the number of dead individuals to date (May 5, 2020) and constraint the parameters on the basis of values reported in the literature. The peak occurs at day 37 (March 31) approximately, when there is a rapid decrease, with a reproduction ratio R0 = 3 initially, 1.36 at day 22 and 0.8 after day 35, indicating different degrees of lockdown. The predicted death toll is approximately 15600 casualties, with 2.7 million infected individuals at the end of the epidemic. The incubation period providing a better fit of the dead individuals is 4.25 days and the infection period is 4 days, with a fatality rate of 0.00144/day [values based on the reported (official) number of casualties]. The infection fatality rate (IFR) is 0.57 %, and 2.36 % if twice the reported number of casualties is assumed. However, these rates depend on the initially exposed individuals. If approximately nine times more individuals are exposed, there are three times more infected people at the end of the epidemic and IFR = 0.47 %. If we relax these constraints and use a wider range of lower and upper bounds for the incubation and infection periods, we observe that a higher incubation period (13 versus 4.25 days) gives the same IFR (0.6 versus 0.57 %), but nine times more exposed individuals in the first case. Therefore, a precise determination of the fatality rate is subject to the knowledge of the characteristics of the epidemic.

q-bio.PE