{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,22]],"date-time":"2025-11-22T11:23:58Z","timestamp":1763810638256,"version":"3.41.2"},"reference-count":17,"publisher":"World Scientific Pub Co Pte Ltd","issue":"01n02","funder":[{"name":"Data Analytics Research Center, Department of Medical and Surgical Sciences, University of Catanzaro"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Parallel Process. Lett."],"published-print":{"date-parts":[[2022,3]]},"abstract":"<jats:p> 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)\u00a0a parallel methodology to build similarity matrices that represent similar or dissimilar regions with respect to data; (ii)\u00a0an effective workload balancing function to improve performance; (iii)\u00a0the mapping of similarity matrices into networks where nodes represent Italian regions, and edges represent similarity relationships; (iv)\u00a0the 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. <\/jats:p>","DOI":"10.1142\/s0129626421420020","type":"journal-article","created":{"date-parts":[[2021,9,23]],"date-time":"2021-09-23T05:34:15Z","timestamp":1632375255000},"source":"Crossref","is-referenced-by-count":6,"title":["Parallel Network Analysis and Communities Detection (PANC) Pipeline for the Analysis and Visualization of COVID-19 Data"],"prefix":"10.1142","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2868-7732","authenticated-orcid":false,"given":"Giuseppe","family":"Agapito","sequence":"first","affiliation":[{"name":"Data Analytics Research Center, Department of Legal, Economic and Social Sciences, Magna Gr\u00e6cia University, Catanzaro Italy 88100, Italy"}]},{"given":"Marianna","family":"Milano","sequence":"additional","affiliation":[{"name":"Data Analytics Research Center, Department of Medical and Surgical Sciences, Magna Gr\u00e6cia University, Catanzaro Italy 88100, Italy"}]},{"given":"Mario","family":"Cannataro","sequence":"additional","affiliation":[{"name":"Data Analytics Research Center, Department of Medical and Surgical Sciences, Magna Gr\u00e6cia University, Catanzaro Italy 88100, Italy"}]}],"member":"219","published-online":{"date-parts":[[2021,9,22]]},"reference":[{"key":"S0129626421420020BIB001","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa2001017"},{"key":"S0129626421420020BIB002","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2020.2648"},{"journal-title":"Journal of Medical Virology","year":"2020","author":"Lai A.","key":"S0129626421420020BIB003"},{"key":"S0129626421420020BIB007","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph17124182"},{"key":"S0129626421420020BIB008","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-71593-9_26"},{"key":"S0129626421420020BIB009","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-020-05243-6"},{"key":"S0129626421420020BIB010","first-page":"405","volume-title":"Complex network analysis using parallel approximate motif counting","author":"Slota G. 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