scholarly journals Statistical and Network-Based Analysis of Italian COVID-19 Data: Communities Detection and Temporal Evolution

2020 ◽  
Vol 17 (12) ◽  
pp. 4182 ◽  
Author(s):  
Marianna Milano ◽  
Mario Cannataro
Keyword(s):  
Temporal Evolution ◽  
Edge Length ◽  
Statistical Test ◽  
Regional Level ◽  
Analysis Pipeline ◽  
Death Rates ◽  
Detection Algorithms ◽  
Italian Regions ◽  
Similarity Relationships ◽  
Similarity Matrices

The coronavirus disease (COVID-19) outbreak started in Wuhan, China, and it has rapidly spread across the world. Italy is one of the European countries most affected by COVID-19, and it has registered high COVID-19 death rates and the death toll. In this article, we analyzed different Italian COVID-19 data at the regional level for the period 24 February to 29 March 2020. The analysis pipeline includes the following steps. After individuating groups of similar or dissimilar regions with respect to the ten types of available COVID-19 data using statistical test, we built several similarity matrices. Then, we mapped those similarity matrices into networks where nodes represent Italian regions and edges represent similarity relationships (edge length is inversely proportional to similarity). Then, network-based analysis was performed mainly discovering communities of regions that show similar behavior. In particular, network-based analysis was performed by running several community detection algorithms on those networks and by underlying communities of regions that show similar behavior. The network-based analysis of Italian COVID-19 data is able to elegantly show how regions form communities, i.e., how they join and leave them, along time and how community consistency changes along time and with respect to the different available data.

scholarly journals Statistical and network-based analysis of Italian COVID-19 data: communities detection and temporal evolution

2020 ◽  
Author(s):  
Marianna Milano ◽  
Mario Cannataro
Keyword(s):  
Temporal Evolution ◽  
Edge Length ◽  
Statistical Test ◽  
Supplementary File ◽  
Analysis Pipeline ◽  
Death Rates ◽  
Detection Algorithms ◽  
Italian Regions ◽  
Similarity Relationships ◽  
Similarity Matrices

AbstractCoronavirus disease (COVID-19) outbreak started at Wuhan, China, and it has rapidly spread across China and many other countries. Italy is one of the European countries most affected by the COVID-19 disease, and it has registered high COVID-19 death rates and the death toll. In this article, we analyzed different Italian COVID-19 data at the regional level for the period February 24 to March 29, 2020. The analysis pipeline includes the following steps. After individuating groups of similar or dissimilar regions with respect to the ten types of available COVID-19 data using statistical test, we built several similarity matrices (reported in Supplementary file). Then, we mapped those similarity matrices into networks where nodes represent Italian regions and edges represent similarity relationships (edge length is inversely proportional to similarity). Then, network-based analysis was performed mainly discovering communities of regions that show similar behaviour. Then, network-based analysis was performed by running several community detection algorithms on those networks and by underlying communities of regions that show similar behaviour. The network-based analysis of Italian COVID-19 data is able to elegantly show how regions form communities, i.e. how they join and leave them, along time and how community consistency changes along time and with respect to the different available data.

scholarly journals EXCESS MORTALITY FROM COVID-19. WEEKLY EXCESS DEATH RATES BY AGE AND SEX FOR SWEDEN AND ITS MOST AFFECTED REGION

2020 ◽  
Author(s):  
Karin Modig ◽  
Anders Ahlbom ◽  
Marcus Ebeling
Keyword(s):  
Causes Of Death ◽  
Excess Mortality ◽  
National Level ◽  
Mortality Rates ◽  
Regional Level ◽  
Death Rates ◽  
Excess Death ◽  
All Cause Mortality ◽  
Specific Mortality ◽  
Age And Sex

Abstract Background Sweden has one of the highest numbers of COVID-19 deaths per inhabitant globally. However, absolute death counts can be misleading. Estimating age- and sex-specific mortality rates is necessary in order to account for the underlying population structure. Furthermore, given the difficulty of assigning causes of death, excess all-cause mortality should be estimated to assess the overall burden of the pandemic. Methods By estimating weekly age- and sex-specific death rates during 2020 and during the preceding five years, our aim is to get more accurate estimates of the excess mortality attributed to COVID-19 in Sweden, and in the most affected region Stockholm. Results Eight weeks after Sweden’s first confirmed case, the death rates at all ages above 60 were higher than for previous years. Persons above age 80 were disproportionally more affected, and men suffered greater excess mortality than women in ages up to 75 years. At older ages, the excess mortality was similar for men and women, with up to 1.5 times higher death rates for Sweden and up to 3 times higher for Stockholm. Life expectancy at age 50 declined by less than 1 year for Sweden and 1.5 years for Stockholm compared to 2019. Conclusions The excess mortality has been high in older ages during the pandemic, but it remains to be answered if this is because of age itself being a prognostic factor or a proxy for comorbidity. Only monitoring deaths at a national level may hide the effect of the pandemic on the regional level.

scholarly journals On the Verge of Institutionalisation? Participatory Budgeting Evidence in Five Italian Regions

2021 ◽  
Vol 13 (2) ◽  
pp. 25-45
Author(s):  
Giovanni Allegretti ◽  
Matteo Bassoli ◽  
Greta Colavolpe
Keyword(s):  
Regional Level ◽  
Added Value ◽  
Participatory Budgeting ◽  
The Past ◽  
Legal Provisions ◽  
Italian Regions ◽  
Participatory Practices ◽  
Legal Frameworks ◽  
Municipal Level ◽  
Different Parts

Over the past few years, Italy has been setting the stage for different democratic innovations, especially those that have been implemented at municipal (or sub-municipal) level in different parts of the country. The expansion of Participatory Budgeting has been a remarkable one, accompanied by the diffusion of regional laws that were adopted to promote a culture of more intense civic participation. Moving from an overview of the recent diffusion of Participatory Budgeting in different areas of the country, this article proposes a reflection on what kind of added value the existence of this legal provision has led to the promotion of participation at a regional level and what this may represent. The construction of the Italian branch of Participatory Budgeting’s World Atlas offers an opportunity to assess legal provisions’ contribution to the diffusion and enrootment of participatory practices, especially in smaller-scale municipalities. Through zooming into some cases (such as Sicily, Emilia Romagna, Apulia and Tuscany, or Lazio in the last few years) the authors argue that the formalisation of participatory practices into legal frameworks today is an important but not a sufficient factor that diffuses and enroots participatory culture in local territories, and that a supplement of monitoring structures and detailed studies would help make challenges and added values of regional law frameworks clearer.

Parallel Network Analysis and Communities Detection (PANC) Pipeline for the Analysis and Visualization of COVID-19 Data

2021 ◽  
pp. 2142002
Author(s):  
Giuseppe Agapito ◽  
Marianna Milano ◽  
Mario Cannataro
Keyword(s):  
Network Analysis ◽  
Real Data ◽  
Detection Algorithm ◽  
Data Sets ◽  
Data Set ◽  
Italian Regions ◽  
Initial Dataset ◽  
Parallel Network ◽  
Community Detection Algorithm ◽  
Similarity Matrices

A new coronavirus, causing a severe acute respiratory syndrome (COVID-19), was started at Wuhan, China, in December 2019. The epidemic has rapidly spread across the world becoming a pandemic that, as of today, has affected more than 70 million people causing over 2 million deaths. To better understand the evolution of spread of the COVID-19 pandemic, we developed PANC (Parallel Network Analysis and Communities Detection), a new parallel preprocessing methodology for network-based analysis and communities detection on Italian COVID-19 data. The goal of the methodology is to analyze set of homogeneous datasets (i.e. COVID-19 data in several regions) using a statistical test to find similar/dissimilar behaviours, mapping such similarity information on a graph and then using community detection algorithm to visualize and analyze the initial dataset. The methodology includes the following steps: (i) a parallel methodology to build similarity matrices that represent similar or dissimilar regions with respect to data; (ii) an effective workload balancing function to improve performance; (iii) the mapping of similarity matrices into networks where nodes represent Italian regions, and edges represent similarity relationships; (iv) the discovering and visualization of communities of regions that show similar behaviour. The methodology is general and can be applied to world-wide data about COVID-19, as well as to all types of data sets in tabular and matrix format. To estimate the scalability with increasing workloads, we analyzed three synthetic COVID-19 datasets with the size of 90.0[Formula: see text]MB, 180.0[Formula: see text]MB, and 360.0[Formula: see text]MB. Experiments was performed on showing the amount of data that can be analyzed in a given amount of time increases almost linearly with the number of computing resources available. Instead, to perform communities detection, we employed the real data set.

scholarly journals Spatio-Temporal Resource Mapping for Intensive Care Units at Regional Level for COVID-19 Emergency in Italy

2020 ◽  
Vol 17 (10) ◽  
pp. 3344 ◽  
Author(s):  
Pietro Hiram Guzzi ◽  
Giuseppe Tradigo ◽  
Pierangelo Veltri
Keyword(s):  
Intensive Care ◽  
Intensive Care Units ◽  
Regional Scale ◽  
Critical Issue ◽  
Regional Level ◽  
Decision Makers ◽  
Proposed Model ◽  
Italian Regions ◽  
Spatio Temporal ◽  
Almost All

COVID-19 is a worldwide emergency since it has rapidly spread from China to almost all the countries worldwide. Italy has been one of the most affected countries after China. North Italian regions, such as Lombardia and Veneto, had an abnormally large number of cases. COVID-19 patients management requires availability of sufficiently large number of Intensive Care Units (ICUs) beds. Resources shortening is a critical issue when the number of COVID-19 severe cases are higher than the available resources. This is also the case at a regional scale. We analysed Italian data at regional level with the aim to: (i) support health and government decision-makers in gathering rapid and efficient decisions on increasing health structures capacities (in terms of ICU slots) and (ii) define a geographic model to plan emergency and future COVID-19 patients management using reallocating them among health structures. Finally, we retain that the here proposed model can be also used in other countries.

scholarly journals Regional financialisation and financial systems convergence: Evidence from Italy

2016 ◽  
Vol 49 (1) ◽  
pp. 141-167 ◽  
Author(s):  
Philip Arestis ◽  
Giuseppe Fontana ◽  
Peter Phelps
Keyword(s):  
Panel Data ◽  
Spatial Scale ◽  
Temporal Evolution ◽  
Financial Instability ◽  
International Level ◽  
Financial Systems ◽  
Financial Policies ◽  
Policy Makers ◽  
Italian Regions ◽  
Primary Contribution

The term ‘financialisation’ has now entered the lexicon of academics and policy makers, though there is still no agreement on its meaning and significance. One of the earlier definitions was in relation to the growing weight of financial motives, financial actors and markets in the operation of modern economies, both at the national and international level, from the early 1980s until today. Building on this definition, this paper sheds further light on the implications of spatial financialisation, which has been associated with the over and under-extension of credit across and within countries and evolving financial instability. The paper’s primary contribution is to extend in a robust manner a powerful panel data convergence testing methodology to analyse the spatial scale and temporal evolution of Italian regional lending conditions. The paper concludes that financial divergence has broadly increased in Italian regions. Furthermore, we are able to link regional financialisation to the growing north–south divide in a significant and meaningful way. As a result, the ability of southern regions in Italy to absorb adverse macroeconomic and financial shocks has been weakened. Relevant regional financial policies have thereby become very important.

scholarly journals Forecasting national and regional level intensive care unit bed demand during COVID-19: The case of Italy

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0247726
Author(s):  
Simone Gitto ◽  
Carmela Di Mauro ◽  
Alessandro Ancarani ◽  
Paolo Mancuso
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Growth Models ◽  
Regional Level ◽  
Percentage Error ◽  
Short Time Series ◽  
Hospital Capacity ◽  
Italian Regions ◽  
Past Data ◽  
Short Time

Given the pressure on healthcare authorities to assess whether hospital capacity allows properly responding to outbreaks such as COVID-19, there is a need for simple, data-driven methods that may provide accurate forecasts of hospital bed demand. This study applies growth models to forecast the demand for Intensive Care Unit admissions in Italy during COVID-19. We show that, with only some mild assumptions on the functional form and using short time-series, the model fits past data well and can accurately forecast demand fourteen days ahead (the mean absolute percentage error (MAPE) of the cumulative fourteen days forecasts is 7.64). The model is then applied to derive regional-level forecasts by adopting hierarchical methods that ensure the consistency between national and regional level forecasts. Predictions are compared with current hospital capacity in the different Italian regions, with the aim to evaluate the adequacy of the expansion in the number of beds implemented during the COVID-19 crisis.

scholarly journals An Extended Regional Gender Gaps Index (eRGGI): Comparative Measurement of Gender Equality at Different Levels of Regionality

2021 ◽  
Author(s):  
C. Cascella ◽  
J. Williams ◽  
M. Pampaka
Keyword(s):  
Gender Equality ◽  
Developed Countries ◽  
Regional Level ◽  
Policy And Practice ◽  
Comparative Measurement ◽  
Gender Gaps ◽  
Aggregated Data ◽  
Italian Regions ◽  
Regional Comparisons ◽  
Different Levels

AbstractGender equality has been widely explored, but there is limited research investigating its variability at regional level. This paper aims to fill this gap by developing and validating a new, regional gender gaps index, compatible with previous indices used to compare gender equality across nations but now fit for the purpose of measuring gender equality across regions, within nations. To this end, we (i) reviewed existing indicators of gender equality; (ii) assessed the contribution of the indicators most frequently used in previous research to measure gender equality; (iii) developed an extended, regional version of the gender gaps index (eRGGI), by extending it to include new indicators able to capture female empowerment in developed countries, like Italy; and, (iv) explored the variability of gender equality across Italian regions. In developing our eRGGI, some indicators traditionally used to measure gender equality were removed and others were introduced to capture new dimensions of gender equality to suit modern conditions regarding contexts where equality is considered important, in contemporary Europe. Results showed that gender equality varies dramatically across regions, also confirming the relevance of the new indicators we proposed to add. Such results call for more caution in interpreting results based on nationally aggregated data to inform policy and practice, arguing for regional comparisons to become more prominent.

scholarly journals Response to a population bottleneck can be used to infer recessive selection

2014 ◽  
Author(s):  
Daniel J Balick ◽  
Ron Do ◽  
David Reich ◽  
Shamil R Sunyaev
Keyword(s):  
Transient Response ◽  
Functional Dependence ◽  
Population Bottleneck ◽  
Statistical Test ◽  
Regional Level ◽  
Large Set ◽  
Deleterious Mutations ◽  
Demographic Differences ◽  
Genome Wide ◽  
Different Populations

Here we present the first genome wide statistical test for recessive selection. This test uses explicitly non-equilibrium demographic differences between populations to infer the mode of selection. By analyzing the transient response to a population bottleneck and subsequent re-expansion, we qualitatively distinguish between alleles under additive and recessive selection. We analyze the response of the average number of deleterious mutations per haploid individual and describe time dependence of this quantity. We introduce a statistic, BR, to compare the number of mutations in different populations and detail its functional dependence on the strength of selection and the intensity of the population bottleneck. This test can be used to detect the predominant mode of selection on the genome wide or regional level, as well as among a sufficiently large set of medically or functionally relevant alleles.

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