Spatio-temporal spread pattern of Covid-19 in Italy

This paper investigates the spatio-temporal spread pattern of Covid-19 in Italy, during the first wave of infections, from February to October 2020. Disease mappings of the virus infections by using the Besag-York-Molliè model and some spatio-temporal extensions are provided. This modelling framework, which includes a temporal component, allows to study the time evolution of the spread pattern among the 107 Italian provinces. The focus is on the effect of citizens’ mobility patterns, represented here by the three distinct phases of the Italian virus first wave, identified by the Italian government, also characterised by the lockdown period. Results show the effectiveness of the lockdown action and an inhomogeneous spatial trend that characterises the virus spread during the first wave. Furthermore, the results suggest that the temporal evolution of each province’s cases is independent of the temporal evolution of the other ones, meaning that the contagions and temporal trend may be caused by some province-specific aspects rather than by the subjects’ spatial movements

Chapter Determinants of spatial intensity of stop locations on cruise passengers tracking data

This paper aims at analyzing the spatial intensity in the distribution of stop locations of cruise passengers during their visit at the destination through a stochastic point process modelling approach on a linear network. Data collected through the integration of GPS tracking technology and questionnaire-based survey on cruise passengers visiting the city of Palermo are used, to identify the main determinants which characterize their stop locations pattern. The spatial intensity of stop locations is estimated through a Gibbs point process model, taking into account for both individual-related variables, contextual-level information, and for spatial interaction among stop points. The Berman-Turner device for maximum pseudolikelihood is considered, by using a quadrature scheme generated on the network. The approach used allows taking into account the linear network determined by the street configuration of the destination under analysis. The results show an influence of both socio-demographic and trip-related characteristics on the stop location patterns, as well as the relevance of distance from the main attractions, and potential interactions among cruise passengers in stop configuration. The proposed approach represents both improvements from the methodological perspective, related to the modelling of spatial point process on a linear network, and from the applied perspective, given that better knowledge of the determinants of spatial intensity of visitors’ stop locations in urban contexts may orient destination management policy

Association of USF1 and APOA5 polymorphisms with familial combined hyperlipidemia in an Italian population.

Familial combined hyperlipidemia (FCH) is a polygenic and multifactorial disease characterized by a variable phenotype showing increased levels of triglycerides and/or cholesterol. The aim of this study was to identify single nucleotides (SNPs) in lipid-related genes associated with FCH. METHODS AND RESULTS: Twenty SNPs in lipid-related genes were studied in 142 control subjects and 165 FCH patients after excluding patients with mutations in the LDLR gene and patients with the E2/E2 genotype of APOE. In particular, we studied the 9996G > A (rs2073658) and 11235C > T (rs3737787) variants in the Upstream Stimulatory Factor 1 gene (USF1), and the -1131T > C (rs662799) and S19W (rs3135506) variants in the Apolipoprotein A-V gene (APOA5). We found that the frequencies of these variants differed between patients and controls and that are associated with different lipid profiles. At multivariate logistic regression SNP S19W in APOA5 remained significantly associated with FCH independently of age, sex, BMI, cholesterol and triglycerides. CONCLUSIONS: Our results show that the USF1 and APOA5 polymorphisms are associated with FCH and that the S19W SNP in the APOA5 gene is associated to the disease independently of total cholesterol, triglycerides and BMI. However, more extensive studies including other SNPs such as rs2516839 in USF1, are required

Estimating the number of changepoints in segmented regression models: comparative study and application

This paper deals with the problem of selecting the number of changepoints in segmented regression models. The aim is to review selection criteria, namely information criteria and hypothesis testing, and to propose a novel application in the context of students' careers in higher education. The performance of the selection criteria is assessed through simulation studies. Furthermore, we investigate the relationship between University students' performance and one of its main determinants, finding out that this relationship is actually broken-line

Selecting the Kth nearest-neighbour for clutter removal in spatial point processes through segmented regression models

We consider the problem of feature detection, in the presence of clutter in spatial point processes. A previous study addresses the issue of the selection of the best nearest neighbour for clutter removal. We outline a simple workflow to automatically estimate the number of nearest neighbours by means of segmented regression models applied to an entropy measure of cluster separation. The method is suitable for a feature with clutter as two superimposed Poisson processes on any twodimensional space, including linear networks. We present simulations to illustrate the method and an application to the problem of seismic fault detection