ABSTRACT
The spatial epidemic dynamics of COVID-19 outbreak in Italy were modelled by means of an Object-Oriented Bayesian Network in order to explore the dependence relationships, in a static and a dynamic way, among the weekly incidence rate, the intensive care units occupancy rate and that of deaths. Following an autoregressive approach, both spatial and time components have been embedded in the model by means of spatial and time lagged variables. The model could be a valid instrument to support or validate policy makers' decisions strategies.
ABSTRACT
The emergence of the novel betacoronavirus, recently renamed as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has raised serious concerns due to the virus's rapid dissemination worldwide. Nevertheless, there is limited information about the genomic epidemiology of SARS-CoV-2 circulating in Italy from surveillance studies. The shortage of complete genomic sequences available impairs our understanding of the SARS-CoV-2 introduction and establishment in the country. To better understand its dynamics in Italy, we analyzed complete genomes of SARS-CoV-2 isolates, obtained directly from clinical samples. Our phylogenetic reconstructions suggest possible multiple introduction of SARS-CoV-2. Continued genomic surveillance strategies are needed to improve monitoring and understanding of the current SARS-CoV-2 epidemics, which might help to attenuate public health impact of infectious diseases.