Combining rank-size and k-means for clustering countries over the COVID-19 new deaths per million.

This paper deals with the cluster analysis of selected countries based on COVID-19 new deaths per million data. We implement a statistical procedure that combines a rank-size exploration and a k-means approach for clustering. Specifically, we first carry out a best-fit exercise on a suitable polynomial rank-size law at an individual country level; then, we cluster the considered countries by adopting a k-means clustering procedure based on the calibrated best-fit parameters. The investigated countries are selected considering those with a high value for the Healthcare Access and Quality Index to make a consistent analysis and reduce biases from the data collection phase. Interesting results emerge from the meaningful interpretation of the parameters of the best-fit curves; in particular, we show some relevant properties of the considered countries when dealing with the days with the highest number of new daily deaths per million and waves. Moreover, the exploration of the obtained clusters allows explaining some common countries' features.

Multiway clustering with time-varying parameters.

This paper proposes a clustering approach for multivariate time series with time-varying parameters in a multiway framework. Although clustering techniques based on time series distribution characteristics have been extensively studied, methods based on time-varying parameters have only recently been explored and are missing for multivariate time series. This paper fills the gap by proposing a multiway approach for distribution-based clustering of multivariate time series. To show the validity of the proposed clustering procedure, we provide both a simulation study and an application to real air quality time series data.

The complex interplay between COVID-19 and economic activity.

We introduce a dynamical system to model the complex interaction between COVID-19 and economic activity. The model introduces some novelties not accounted by SIR-like models. The equilibrium of the system is an unstable focus, with fluctuations having increasing size and periodicity. Numerical simulations of the model produce waves which reproduce the pandemic dynamics. In observing the stylized facts linking economics and pandemic and stating related reasonable assumptions, we obtain a Lotka-Volterra co-dynamics. This outcome is confirmed by extensive simulations. The outcomes obtained qualitatively replicate some important stylized facts deepening the knowledge about the role of some parameters in their origin and eventually in their shaping.

Predicting future cancer burden in the United States by artificial neural networks.

Aims: To capture the complex relationships between risk factors and cancer incidences in the US and predict future cancer burden. Materials & methods: Two artificial neural network (ANN) algorithms were adopted: a multilayer feed-forward network (MLFFNN) and a nonlinear autoregressive network with eXogenous inputs (NARX). Data on the incidence of the four most common tumors (breast, colorectal, lung and prostate) from 1992 to 2016 (available from National Cancer Institute online datasets) were used for training and validation, and data until 2050 were predicted. Results: The rapid decreasing trend of prostate cancer incidence started in 2010 will continue until 2018-2019; it will then slow down and reach a plateau after 2050, with several differences among ethnicities. The incidence of breast cancer will reach a plateau in 2030, whereas colorectal cancer incidence will reach a minimum value of 35 per 100,000 in 2030. As for lung cancer, the incidence will decrease from 50 per 100,000 (2017) to 31 per 100,000 in 2030 and 26 per 100,000 in 2050. Conclusion: This up-to-date prediction of cancer burden in the US could be a crucial resource for planning and evaluation of cancer-control programs.

Outdoor light pollution and COVID-19: The Italian case.

There is a wide debate on the connections between pollution and COVID-19 propagation. This note faces this problem by exploring the peculiar case of the correlation between outdoor light pollution and the ratio between infected people and population. We discuss the empirical case of Italian provinces (NUTS-3 level), which represent an interesting context for the noticeable entity of contagions and for the relevant level of outdoor light pollution. The empirical results, based on a multivariate cross section model controlling for income, density, population ageing and environmental pollution, show that there is a positive relation between outdoor light pollution per capita and the strength of COVID-19 infection. This effect is statistically more robust in a non linear specification than in a linear one. We interpret our findings as a piece of evidence related to the impact of outdoor light pollution on human health, thus suggesting policies aimed at reducing this important source of pollution.

Tsallis Entropy for Cross-Shareholding Network Configurations.

In this work, we develop the Tsallis entropy approach for examining the cross-shareholding network of companies traded on the Italian stock market. In such a network, the nodes represent the companies, and the links represent the ownership. Within this context, we introduce the out-degree of the nodes-which represents the diversification-and the in-degree of them-capturing the integration. Diversification and integration allow a clear description of the industrial structure that were formed by the considered companies. The stochastic dependence of diversification and integration is modeled through copulas. We argue that copulas are well suited for modelling the joint distribution. The analysis of the stochastic dependence between integration and diversification by means of the Tsallis entropy gives a crucial information on the reaction of the market structure to the external shocks-on the basis of some relevant cases of dependence between the considered variables. In this respect, the considered entropy framework provides insights on the relationship between in-degree and out-degree dependence structure and market polarisation or fairness. Moreover, the interpretation of the results in the light of the Tsallis entropy parameter gives relevant suggestions for policymakers who aim at shaping the industrial context for having high polarisation or fair joint distribution of diversification and integration. Furthermore, a discussion of possible parametrisations of the in-degree and out-degree marginal distribution-by means of power laws or exponential functions- is also carried out. An empirical experiment on a large dataset of Italian companies validates the theoretical framework.

Investigating the Configurations in Cross-Shareholding: A Joint Copula-Entropy Approach.

The complex nature of the interlacement of economic actors is quite evident at the level of the Stock market, where any company may actually interact with the other companies buying and selling their shares. In this respect, the companies populating a Stock market, along with their connections, can be effectively modeled through a directed network, where the nodes represent the companies, and the links indicate the ownership. This paper deals with this theme and discusses the concentration of a market. A cross-shareholding matrix is considered, along with two key factors: the node out-degree distribution which represents the diversification of investments in terms of the number of involved companies, and the node in-degree distribution which reports the integration of a company due to the sales of its own shares to other companies. While diversification is widely explored in the literature, integration is most present in literature on contagions. This paper captures such quantities of interest in the two frameworks and studies the stochastic dependence of diversification and integration through a copula approach. We adopt entropies as measures for assessing the concentration in the market. The main question is to assess the dependence structure leading to a better description of the data or to market polarization (minimal entropy) or market fairness (maximal entropy). In so doing, we derive information on the way in which the in- and out-degrees should be connected in order to shape the market. The question is of interest to regulators bodies, as witnessed by specific alert threshold published on the US mergers guidelines for limiting the possibility of acquisitions and the prevalence of a single company on the market. Indeed, all countries and the EU have also rules or guidelines in order to limit concentrations, in a country or across borders, respectively. The calibration of copulas and model parameters on the basis of real data serves as an illustrative application of the theoretical proposal.

Improving the explainability of autoencoder factors for commodities through forecast-based Shapley values.

Autoencoders are dimension reduction models in the field of machine learning which can be thought of as a neural network counterpart of principal components analysis (PCA). Due to their flexibility and good performance, autoencoders have been recently used for estimating nonlinear factor models in finance. The main weakness of autoencoders is that the results are less explainable than those obtained with the PCA. In this paper, we propose the adoption of the Shapley value to improve the explainability of autoencoders in the context of nonlinear factor models. In particular, we measure the relevance of nonlinear latent factors using a forecast-based Shapley value approach that measures each latent factor's contributions in determining the out-of-sample accuracy in factor-augmented models. Considering the interesting empirical instance of the commodity market, we identify the most relevant latent factors for each commodity based on their out-of-sample forecasting ability.

Followers do not dictate the virality of news outlets on social media.

Initially conceived for entertainment, social media platforms have profoundly transformed the dissemination of information and consequently reshaped the dynamics of agenda-setting. In this scenario, understanding the factors that capture audience attention and drive viral content is crucial. Employing Gibrat's Law, which posits that an entity's growth rate is unrelated to its size, we examine the engagement growth dynamics of news outlets on social media. Our analysis includes the Facebook historical data of over a thousand news outlets, encompassing approximately 57 million posts in four European languages from 2008 to the end of 2022. We discover universal growth dynamics according to which news virality is independent of the traditional size of the outlet. Moreover, our analysis reveals a significant long-term impact of news source reliability on engagement growth, with engagement induced by unreliable sources decreasing over time. We conclude the article by presenting a statistical model replicating the observed growth dynamics.

Clustering networked funded European research activities through rank-size laws.

This paper treats a well-established public evaluation problem, which is the analysis of the funded research projects. We specifically deal with the collection of the research actions funded by the European Union over the 7th Framework Programme for Research and Technological Development and Horizon 2020. The reference period is 2007-2020. The study is developed through three methodological steps. First, we consider the networked scientific institutions by stating a link between two organizations when they are partners in the same funded project. In doing so, we build yearly complex networks. We compute four nodal centrality measures with relevant, informative content for each of them. Second, we implement a rank-size procedure on each network and each centrality measure by testing four meaningful classes of parametric curves to fit the ranked data. At the end of such a step, we derive the best fit curve and the calibrated parameters. Third, we perform a clustering procedure based on the best-fit curves of the ranked data for identifying regularities and deviations among years of research and scientific institutions. The joint employment of the three methodological approaches allows a clear view of the research activity in Europe in recent years.

Financial interbanking networks resilience under shocks propagation.

The concept of resilience-i.e., the ability of a unified structure to absorb shocks-is of high relevance in the context of network modelling and analysis, mainly when referred to finance. This paper starts from this premise, and deals with the resilience of a financial interbanking system. At this aim, we firstly introduce a new measure of the resilience of a network, by taking into full consideration the influence of the topology of the network and the weights of its links in the shocks propagation; then, we build one financial network model related to the quarterly-based interbanking sector, whose weights are calibrated on high quality empirical data; lastly, we compute the resilience measure of the considered networks. A discussion of the results is provided, by considering both finance and network theory perspectives.

Municipal waste management: A complex network approach with an application to Italy.

The paper contributes to the debate concerning the management of municipal solid waste by providing an analysis of two key aspects of waste management - namely, waste separation and dispatch to treatment plants. Our analysis aims at detecting the extent to which actual behavior in (close-by) municipalities is similar with respect to those two aspects. To pursue our scope, a complex network approach is followed. In particular, we conceptualize, explore and compare two networks, whose nodes are the municipalities, while weights synthesize in one network the percentage of sorted waste that is collected at a municipal level, and in the other one the distance from the waste processing plants used by each municipality. The theoretical network models are implemented through an empirical study based on a high quality dataset referred to Italian municipalities. In this regard, the detection of communities of municipalities and their geospatial contextualization are introduced as devices for a complete description of current practices of municipal waste separation and transfers in Italy.

Genitourinary Tumors: Update on Molecular Biomarkers for Diagnosis, Prognosis and Prediction of Response to Therapy.

BACKGROUND: Research of biomarkers in genitourinary tumors goes along with the development of complex emerging techniques ranging from next generation sequencing platforms, applied to archival pathology specimens, cytological samples, liquid biopsies, and to patient-derived tumor models. METHODS: This contribution is an update on molecular biomarkers for diagnosis, prognosis and prediction of response to therapy in genitourinary tumors. The following major topics are dealt with: Immunological biomarkers, including the microbiome, and their potential role and caveats in renal cell carcinoma, bladder and prostate cancers and testicular germ cell tumors; Tissue biomarkers for imaging and therapy, with emphasis on Prostate-specific membrane antigen in prostate cancer; Liquid biomarkers in prostate cancer, including circulating tumor cell isolation and characterization in renal cell carcinoma, bladder cancer with emphasis on biomarkers detectable in the urine and testicular germ cell tumors; and Biomarkers and economic sustainability. CONCLUSION: The identification of effective biomarkers has become a major focus in cancer research, mainly due to the necessity of selecting potentially responsive patients in order to improve their outcomes, as well as to reduce the toxicity and costs related to ineffective treatments.

Data on the annual aggregated income taxes of the Italian municipalities over the quinquennium 2007-2011.

This dataset contains the annual aggregated income taxes of all the Italian municipalities over the years 2007-2011. Data are clustered over the Italian regions and provinces. The source of the data is the Italian Ministry of Economics and Finance. The administrative variations in Italy over the quinquennium have been taken into account. Data are useful to understand the economic structure of Italy at the microscopic level of municipalities. They can serve also for making comparisons between economical aspects and other features of the Italian cities.

Assessing the relationship between toxicity and economic cost of oncological target agents: A systematic review of clinical trials.

Target agents are peculiar oncological drugs which differ from the traditional therapies in their ability of recognizing specific molecules expressed by tumor cells and microenvironment. Thus, their toxicity is generally lower than that associated to chemotherapy, and they represent nowadays a new standard of care in a number of tumors. This paper deals with the relationship between economic costs and toxicity of target agents. At this aim, a cluster analysis-based exploration of the main features of a large collection of them is carried out, with a specific focus on the variables leading to the identification of their toxicity and related costs. The analysis of the toxicity is based on the Severe Adverse Events (SAE) and Discontinuation (D) rates of each target agent considering data published on PubMed from 1965 to 2016 in the phase II and III studies that have led to the approval of these drugs for cancer patients by US Food and Drug Administration. The construction of the dataset represents a key step of the research, and is grounded on the critical analysis of a wide set of clinical studies. In order to capture different evaluation strategies of the toxicity, clustering is performed according to three different criteria (including Voronoi tessellation). Our procedure allows us to identify 5 different groups of target agents pooled by similar SAE and D rates and, at the same time, 3 groups based on target agents' costs for 1 month and for the median whole duration of therapy. Results highlight several specific regularities for toxicity and costs. This study present several limitations, being realized starting from clinical trials and not from individual patients' data. However, a macroscopic perspective suggests that costs are rather heterogeneous, and they do not clearly follow the clustering based on SAE and D rates.

A Universal Rank-Size Law.

A mere hyperbolic law, like the Zipf's law power function, is often inadequate to describe rank-size relationships. An alternative theoretical distribution is proposed based on theoretical physics arguments starting from the Yule-Simon distribution. A modeling is proposed leading to a universal form. A theoretical suggestion for the "best (or optimal) distribution", is provided through an entropy argument. The ranking of areas through the number of cities in various countries and some sport competition ranking serves for the present illustrations.