In this paper, we develop a forecasting model for the spread of COVID-19 infection at a provincial (i.e., EU NUTS-3) level in Italy by using official data from the Italian Ministry of Health integrated with data extracted from daily official press conferences of regional authorities and local newspaper websites. This data integration is needed as COVID-19 death data are not available at the NUTS-3 level from official open data channels. An adjusted time-dependent SIRD model is used to predict the behavior of the epidemic; specifically, the number of susceptible, infected, deceased, recovered people and epidemiological parameters. Predictive model performance is evaluated using comparison with real data.

Background:To our knowledge, no study assessed the association between dietary patterns and nasopharyngeal carcinoma (NPC) in low-incidence areas.Methods:We examined this association in a hospital-based case–control study carried out in Italy between 1992 and 2008, including 198 incident NPC cases and 594 controls. A posteriori dietary patterns were identified through principal component factor analysis performed on 28 nutrients and minerals derived from a 78-item food-frequency questionnaire. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated using unconditional multiple logistic regression models on tertiles of factor scores.Results:We identified five dietary patterns named Animal products, Starch-rich, Vitamins and fibre, Animal unsaturated fatty acids (AUFAs), and Vegetable unsaturated fatty acids (VUFAs). The Animal product (OR=2.62, 95% CI=1.67–4.13, for the highest vs lowest score tertile), Starch-rich (OR=2.05, 95% CI=1.27–3.33), and VUFA (OR=1.90, 95% CI=1.22–2.96) patterns were positively associated with NPC. The AUFA pattern showed a positive association of borderline significance, whereas the Vitamins and fibre pattern was nonsignificantly but inversely associated with NPC.Conclusions:These findings suggest that diets rich in animal products, starch, and fats are positively related to NPC risk in this low-incidence country.

The reliability of risk measures for financial portfolios crucially rests on the availability of sound representations of the involved random variables. The trade-off between adherence to reality and specification parsimony can find a fitting balance in a technique that "adjust" the moments of a density function by making use of its associated orthogonal polynomials. This approach rests on the Gram-Charlier expansion of a Gaussian law which, allowing for leptokurtosis to an appreciable extent, makes the resulting random variable a tail-sensitive density function. In this paper we determine the density of sums of leptokurtic normal variables duly adjusted for excess kurtosis by means of their Gram-Charlier expansions based on Hermite polynomials. The resultant density can be effectively used to represent a portfolio return and as such proves suitable for computing some risk measures such as Value at Risk and expected short fall. An application to a portfolio of financial returns is used to provide evidence of the effectiveness of the proposed approach.

Marginal or conditional independencies are well known relationships among variables involved in a contingency table. In this paper we handle with categorical (ordinal) variables and we focus on the (in)dependence relationships under this marginal and conditional perspective in addition to context-specific point of view. The last statement concerns independencies holding only in a subspace of the outcome space. We take advantage from the Hierarchical Multinomial Marginal models environment and we provide several original results about the representation of context-specific independencies through these models. An application about the innovation degree of the Italian enterprises is provided.

The Coronavirus Disease 19 epidemic is an infectious disease which was declared as a pandemic and hit all the Countries, all over the world, from the beginning of the year 2020. Despite the emergency vigilance plans, in all the Countries, Health Systems experienced a different ratio of lethality, admissions to intensive care units and managing quarantine of positive patients. The aim of this study is to investigate if some health indicators might have been useful to understand the capacity of Italian National Health Service to manage the COVID 19 epidemic. We will compare data in two different Italian regions in the Northern part of Italy (Lombardy and Veneto) with the national data to understand if different health strategies might be significant to explain different patterns of COVID 19 epidemic in Italy. The two regions have two different health policies to face CoViD-2019 epidemic. To face epidemic like this one the answer should be outside hospitals but this means to have general practitioners well-trained and enough healthcare personnel working outside hospitals.

The first case of Coronavirus Disease 2019 in Italy was detected on February the 20th in Lombardy region. Since that date, Lombardy has been the most affected Italian region by the epidemic, and its healthcare system underwent a severe overload during the outbreak. From a public health point of view, therefore, it is fundamental to provide healthcare services with tools that can reveal possible new health system stress periods with a certain time anticipation, which is the main aim of the present study. Moreover, the sequence of law decrees to face the epidemic and the large amount of news generated in the population feelings of anxiety and suspicion. Considering this whole complex context, it is easily understandable how people “overcrowded” social media with messages dealing with the pandemic, and emergency numbers were overwhelmed by the calls. Thus, in order to find potential predictors of possible new health system overloads, we analysed data both from Twitter and emergency services comparing them to the daily infected time series at a regional level. Particularly, we performed a wavelet analysis in the time-frequency plane, to finely discriminate over time the anticipation capability of the considered potential predictors. In addition, a cross-correlation analysis has been performed to find a synthetic indicator of the time delay between the predictor and the infected time series. Our results show that Twitter data are more related to social and political dynamics, while the emergency calls trends can be further evaluated as a powerful tool to potentially forecast new stress periods. Since we analysed aggregated regional data, and taking into account also the huge geographical heterogeneity of the epidemic spread, a future perspective would be to conduct the same analysis on a more local basis.

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