To date, official data on the number of people infected with the SARS-CoV-2 -responsible for the CoViD-19 -have been released by the Italian Government just on the basis of a non representative sample of population which tested positive for the swab. However a reliable estimation of the number of infected, including asymptomatic people, turns out to be crucial in the preparation of operational schemes and to estimate the future number of people, who will require, to different extents, medical attentions. In order to overcome the current data shortcoming, this paper proposes a bootstrap-driven, estimation procedure for the number of people infected with the SARS-CoV-2. This method is designed to be robust, automatic and suitable to generate estimations at regional level. Obtained results show that, while official data at March the 12th report 12.839 cases in Italy, people infected wiyh the SARS-CoV-2 could be as high as 105.789.

A comparison was made between factors influencing survival in patients operated on for acute spontaneous subdural hematomas (ASSH) and other groups of patients operated for acute, post-traumatic, subdural hematoma reported in the literature. The data of 17 patients operated on for ASSH were collected. Four variables: early surgical treatment, high Glasgow Coma Scale score on admission, pupillary reactivity and age were statistically analyzed. The most significant factors for good outcome, in order of importance, were early surgical treatment, a high Glasgow Come Scale score on admission, good pupillary reactivity and younger age. The prognostic factors in non-traumatic and traumatic acute subdural hematomas were found to be identical.

COVID-19 infections can spread silently, due to the simultaneous presence of significant numbers of both critical and asymptomatic to mild cases. While, for the former reliable data are available (in the form of number of hospitalization and/or beds in intensive care units), this is not the case of the latter. Hence, analytical tools designed to generate reliable forecast and future scenarios, should be implemented to help decision-makers to plan ahead (e.g., medical structures and equipment). Previous work of one of the authors shows that an alternative formulation of the Test Positivity Rate (TPR), i.e., the proportion of the number of persons tested positive in a given day, exhibits a strong correlation with the number of patients admitted in hospitals and intensive care units. In this paper, we investigate the lagged correlation structure between the newly defined TPR and the hospitalized people time series, exploiting a rigorous statistical model, the Seasonal Auto Regressive Moving Average (SARIMA). The rigorous analytical framework chosen, i.e., the stochastic processes theory, allowed for a reliable forecasting about 12 days ahead of those quantities. The proposed approach would also allow decision-makers to forecast the number of beds in hospitals and intensive care units needed 12 days ahead. The obtained results show that a standardized TPR index is a valuable metric to monitor the growth of the COVID-19 epidemic. The index can be computed on daily basis and it is probably one of the best forecasting tools available today for predicting hospital and intensive care units overload, being an optimal compromise between simplicity of calculation and accuracy.

Modelling the underlying stochastic process is one of the main goals in the study of many dynamic phenomena, such as signal processing, system identification and time series. The issue is often addressed within the framework of ARMA (Autoregressive Moving Average) paradigm, so that the related task of identification of the 'true' order is crucial. As it is well known, the effectiveness of such an approach may be seriously compromised by misspecification errors since they may affect model capabilities in capturing dynamic structures of the process. As a result, inference and empirical outcomes may be heavily misleading. Despite the big number of available approaches aimed at determining the order of an ARMA model, the issue is still open. In this paper, we bring the problem in the framework of bootstrap theory in conjunction with the information-based criterion of Akaike (AIC), and a new method for ARMA model selection will be presented. A theoretical justification for the proposed approach as well as an evaluation of its small sample performances, via simulation study, are given.

COVID-19 infections can spread silently, due to the simultaneous presence of significant numbers of both critical and asymptomatic to mild cases. While for the former reliable data are available (in the form of number of hospitalization and/or beds in intensive care units), this is not the case of the latter. Hence, analytical tools designed to generate reliable forecast and future scenarios, should be implemented to help decision makers planning ahead (e.g. medical structures and equipment). Previous work of one of the authors shows that an alternative formulation of the Test Positivity Rate (TPR), i.e. the proportion of the number of persons tested positive in a given day, exhibits a strong correlation with the number of patients admitted in hospital and intensive care units. In this paper, we investigate the lagged correlation structure between the newly defined TPR and the hospitalized people time series, exploiting a rigorous statistical model, the Seasonal Auto Regressive Moving Average (SARIM A). The rigorous analytical framework chosen, i.e. the stochastic processes theory, allowed for a reliable forecasting about 12 days ahead, of those quantities. The proposed approach would also allow decision makers to forecast the number of beds in hospitals and intensive care units needed 12 days ahead. The obtained results show that a standardized TPR index is a valuable metric to monitor the growth of the COVID-19 epidemic. The index can be computed on daily basis and it is probably one of the best forecasting tools available today for predicting hospital and intensive care units overload, being an optimal compromise between simplicity of calculation and accuracy.