A Social Network Analysis Approach to COVID-19 Community Detection Techniques

Abstract

Machine learning techniques facilitate efficient analysis of complex networks, and can be used to discover communities. This study aimed use such approaches to raise awareness of the COVID-19. In this regard, social network analysis describes the clustering and classification processes for detecting communities. The background of this paper analyzed the geographical distribution of Tambaram, Chennai, and its public health care units. This study assessed the spatial distribution and presence of spatiotemporal clustering of public health care units in different geographical settings over four months in the Tambaram zone. To partition a homophily synthetic network of 100 nodes into clusters, an empirical evaluation of two search strategies was conducted for all IDs centrality of linkage is same. First, we analyzed the spatial information between the nodes for segmenting the sparse graph of the groups. Bipartite The structure of the sociograms 1–50 and 51–100 was taken into account while segmentation and divide them is based on the clustering coefficient values. The result of the cohesive block yielded 5.86 density values for cluster two, which received a percentage of 74.2. This research objective indicates that sub-communities have better access to influence, which might be leveraged to appropriately share information with the public could be used in the sharing of information accurately with the public.

Community Detection Algorithms in Healthcare Applications: A Systematic Review

“…Epidemic or widespread infections, such as COVID-19, Influenza, Swine flu, Ebola, and Cholera need systematic surveillance of clinical syndromes that affect the public's health where early warning of a rising trend in disease prevalence can help to prepare patients and increase efforts to distribute the appropriate vaccine or treatment. On the other hand, the knowledge of a declining trend can be a metric of success [176,177]. Classical public health monitoring relies on surveying and compiling primary data from healthcare providers and pharmacists.…”

“…They incorporated pre-existing disease information into their model and explicitly accounted for under-reporting. Moreover, the authors of [177] used machine learning techniques and social network analysis to raise awareness of COVID-19. In this study, node grouping and community detection on social networks were performed using some edge similarity, edge degree, and node clustering measures techniques.…”

Water-Based Rehabilitation in the Elderly: Data Science Approach to Support the Conduction of a Scoping Review

“…In data science, a large dataset is assessed to find the possible association among a large number of variables. This approach is common in many research fields, such as business intelligence and prevision analysis [15]. It is based on the application of software that is able to manage a dataset with more filters.…”