Characterization of Long COVID using text mining on narrative medicine texts

Through an adequate survey of the history of the disease, Narrative Medicine (NM) aims to allow the definition and implementation of an effective, appropriate, and shared treatment path. In the present study, standard text mining (TM) techniques are applied, as a Latent Dirichlet Allocation (LDA) model for topic modeling is used to characterize narrative medicine texts written on COVID-19. In particular, the focus was mainly on the writings of patients with Post-acute Sequelae of COVID-19, i.e., PASC, as opposed to writings by health professionals and general reflections on COVID-19. The results suggest that the testimonies of PASC patients can be used for identifying shared issues to focus on to be followed and supported appropriately, even from a psychological point of view. © 2021 IEEE.

A novel network-based methodology for analysis of COVID-19 data

The novel COVID-19 pandemic has posed unprecedented challenges to the society and the health sector all over the globe. Here, we present a new network-based methodology to analyze COVID-19 data measures and its application on a real dataset. 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 dataset, mapping such similarity information on a graph and then using community detection algorithm to visualize and analyze the initial dataset. The methodology and its implementation as R function are publicly available at https://github.com/mmilano87/analyzeC19D. We evaluated diverse Italian COVID-19 data made publicly available by the Italian Protezione Civile Department at https://github.com/pcm-dpc/COVID-19/. We considered the data provided for each Italian region in two periods February 24-April 26, 2020 (1st wave), and September 28-November 29, 2020 (2nd wave) and then we compared two periods. Similarity matrices of Italian regions for ten COVID-19 data measures are built by using statistical analysis;then they are mapped to undirected networks. Each node represents an Italian region and an edge connects statistically similar regions. Finally, clusters of regions with similar behaviour were found using network-based community detection algorithms. Experiments depict the communities formed by Italian regions over time and the communities change with respect to the ten data measures and time. © 2021 Copyright for this paper by its authors.

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

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 90.0MB, 180.0MB, and 360.0MB. 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. © 2021 World Scientific Publishing Company.