Ricerc@Sapienza

Background: In 2020, to limit the spread of Coronavirus (COVID-19), many countries, including Italy, have issued a lengthy quarantine period for the entire population. For this reason lifestyle has changed, bringing inevitable repercussions to the Quality of Life (QoL). The present study aims to identify which psychosocial variables predict behaviors capable of affecting the QoL during the lockdown period, potentially highlighting factors that might promote well-being and health in the Italian population during the epidemic.

COVID-19 has rapidly become a pandemic emergency, distressing health systems in each affected country. COVID-19 determines the need for healthcare in a large number of people in an extremely short time and, like a tsunami wave, overruns emergency, infectious diseases, and pneumology departments as well as intensive care units, choking healthcare services. Rehabilitation services are also affected by this epidemic which forces radical changes both in the organization and in the operating methods.

We exploit the provincial variability of COVID-19 cases registered in Italy to select the territorial predictors of the pandemic. Absent an established theoretical diffusion model, we apply machine learning to isolate, among 77 potential predictors, those that minimize the out-of-sample prediction error. We first estimate the model considering cumulative cases registered before the containment measures displayed their effects (i.e. at the peak of the epidemic in March 2020), then cases registered between the peak date and when containment measures were relaxed in early June.

Abstract – Based on high quality surgery and scientific data, scientists and surgeons are committed to protecting patients as well as healthcare staff and hereby provide this Guidance to address the special issues circumstances related to the exponential spread of the Coronavirus disease 2019 (COVID-19) during this
pandemic. As a basis, the authors used the British Intercollegiate General Surgery Guidance as well as recommendations from the USA, Asia, and Italy. The aim is to take responsibility and to provide guidance for surgery

In this paper we design an efficient algorithm, based on epidemic models, to dissemination messages in VANETs. The algorithm, named EPIC, is based on few hypothesis and it is very simple to be implemented. The main hypotheses are that each vehicle knows the position of its neighbours and can communicate with them only in broadcast. EPIC has been designed with the goal to reach the highest number of vehicles "infected" by the message, without overloading the network.