ArticleNetwork modeling and analysis in health informatics and bioinformatics2021
COVID-19 Community Temporal Visualizer: a new methodology for the network-based analysis and visualization of COVID-19 data.
Article in Network modeling and analysis in health informatics and bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 11 citations in OpenAlex.
- Modified Early Warning Score: Clinical Deterioration of Mexican Patients Hospitalized with COVID-19 and Chronic Disease.Healthcare (Basel, Switzerland) · 2023Article
- Bioinformatics and High-Performance Computing Methods for Deciphering and Fighting COVID-19-Editorial.Biotech (Basel (Switzerland)) · 2022Article
- A statistical network pre-processing method to improve relevance and significance of gene lists in microarray gene expression studies.BMC bioinformatics · 2022Article
- Application of CCTV Methodology to Analyze COVID-19 Evolution in Italy.Biotech (Basel (Switzerland)) · 2022Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
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Abstract
Understanding the evolution of the spread of the COVID-19 pandemic requires the analysis of several data at the spatial and temporal levels. Here, we present a new network-based methodology to analyze COVID-19 data measures containing spatial and temporal features and its application on a real dataset. The goal of the methodology is to analyze sets of homogeneous datasets (i.e. COVID-19 data taken in different periods and in several regions) using a statistical test to find similar/dissimilar datasets, mapping such similarity information on a graph and then using a community detection algorithm to visualize and analyze the spatio-temporal evolution of data. We evaluated diverse Italian COVID-19 data made publicly available by the Italian Protezione Civile Department at https://github.com/pcm-dpc/COVID-19/. Furthermore, we considered the climate data related to two periods and we integrated them with COVID-19 data measures to detect new communities related to climate changes. In conclusion, the application of the proposed methodology provides a network-based representation of the COVID-19 measures by highlighting the different behaviour of regions with respect to pandemics data released by Protezione Civile and climate data. The methodology and its implementation as R function are publicly available at https://github.com/mmilano87/analyzeC19D.
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Registered trials
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