Cluster-Based Analysis of Infectious Disease Occurrences Using Tensor Decomposition: A Case Study of South Korea

Abstract

<jats:p>For a long time, various epidemics, such as lower respiratory infections and diarrheal diseases, have caused serious social losses and costs. Various methods for analyzing infectious disease occurrences have been proposed for effective prevention and proactive response to reduce such losses and costs. However, the results of the occurrence analyses were limited because numerous factors affect the outbreak of infectious diseases and there are complex interactions between these factors. To alleviate this limitation, we propose a cluster-based analysis scheme of infectious disease occurrences that can discover commonalities or differences between clusters by grouping elements with similar occurrence patterns. To do this, we collect and preprocess infectious disease occurrence data according to time, region, and disease. Then, we construct a tensor for the data and apply Tucker decomposition to extract latent features in the dimensions of time, region, and disease. Based on these latent features, we conduct k-means clustering and analyze the results for each dimension. To demonstrate the effectiveness of this scheme, we conduct a case study on data from South Korea and report some of the results.</jats:p>

Simulation of Epidemic Processes: A Review of Modern Methods, Models and Approaches

“…Also, the researcher can determine the features according to which the sample needs to be divided independently, or the model will do it on its own. Investigating epidemic processes, models, and methods of clustering are used to solve such applied problems as the determination of geographic territories based on similar signs of the epidemic process [64], the determination of epidemic outbreaks [65], the determination of groups of carriers of infection [66], the determination of the phylogenetic characteristics of individuals of the population [67], determination of patterns of infection spread [68], etc.…”

Geo-temporal patterns to design cost-effective interventions for zoonotic diseases -the case of brucellosis in the country of Georgia

“…Because numerous (if not infinite) geo-temporal patterns may be found in disseminating infectious diseases, geo-referenced and temporal data may also inform on covariates, such as soil, elevation, meteorology, seasonality, and sociology ( 3 ). Because they can –visually– reveal interactions, geo-referenced data can inform more than tabular data ( 4 ).…”

Erratum: Jung, S., et al. Cluster-Based Analysis of Infectious Disease Occurrences Using Tensor Decomposition: A Case Study of South Korea. Int. J. Environ. Res. Public Health 2020, 17, 4872

“…The source of funding should be modified in our original article [ 1 ]. Therefore, in the section “Funding”, where it is stated: “This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health and Welfare, Republic of Korea (HG19C0682).”, we would like to add the following information:…”