Real-time Control of the Air Volume in Ventilation Facilities by Limiting Concentration with Cluster Algorithms

Ventilation improves indoor air quality and reduces airborne infections. It is particularly important at present because of the COVID-19 pandemic. Commercially available ventilation facilities can only be instantly turned on/off or at a set time with adjustable air volumes (high, middle, and low). However, maintaining the indoor carbon dioxide concentration while reducing the energy consumption of these facilities is challenging. Hence, this study developed clustering algorithms to determine the carbon dioxide concentration limit thus enabling real-time air volume adjustment. These limit values were set using the existing energy recovery ventilation (ERV) controller. In the experiment, dual estimation was adopted, and the constructing building energy models from data were sampled at a low rate to compare that the ventilation facilities are only turned on/off. In addition, switching control is closely related to fuzzy control;that is, fuzzy control can be considered a smooth version of switching control. The experimental results indicated that the limits of 600 and 700 ppm were suitable to effectively control the real-time air volume based on the ERV operation. An ERV-based carbon dioxide concentration limit reduced the energy consumption of ventilation facilities by 11%implications of this study. IEEE

Clustering of COVID-19 Vaccination Recipients in DKI Jakarta Using the K-Medoids Algorithm

The COVID-19 pandemic has become a serious problem that has attacked various aspects of life such as social, economic, religious, and others. The government has held a COVID-19 vaccination program as an effort to deal with the COVID-19 problem since January 13, 2021. Many problems occurred due to difficulties in dividing the vaccination recipient areas. This is due to the large number of regions with different conditions for each region. One of the efforts to assist the process of processing large vaccination data is data mining techniques and using the clustering method with the K-medoids algorithm. In this study, data on COVID-19 vaccination was grouped in the East Jakarta area using the K-medoids algorithm clustering method. The calculation is carried out using the Euclidean Distance equation and the value of S > 0. The grouped area categories are at the kelurahan level which will then be divided into several clusters. The clustering process was carried out with RapidMiner on 267 kelurahan data on four main attributes, namely the number of targets, the number of vaccine doses 1, the number of vaccine doses 2, and the number of vaccine doses 3. The clustering process was carried out in 6 simulations with variations of k medoids as much as 2 to 7. The results of clustering show the best number of clusters obtained in the simulation is cluster 6 with the smallest Davies Bouldin Index (DBI) value of 0.209. The clusters obtained are clusters 0 to cluster 5. The cluster that is prioritized in giving vaccinations is cluster 2 with 67 items because its members are areas in DKI Jakarta and give a high score in cases of COVID-19 compared to other clusters. © 2022 IEEE.

Identifying Traffic Congestion Patterns of Urban Road Network Based on Traffic Performance Index

Urban congestion has become a global problem with urbanization and motorization. The analysis of time-varying traffic congestion patterns is necessary to formulate effective management strategies. The existing studies have focused on traffic flow patterns developed by the volume, speed and density of road sections in a limited district, while the long-time analysis of congestion patterns of the macro road network at the city level is inadequate. This paper aims to recognize traffic congestion patterns of the urban road network based on the traffic performance index (TPI) of 699 days in 2018, 2019 and 2021 in Beijing. The self-organizing maps (SOM) method improved by an automatic clustering number determination algorithm is proposed to cluster congestion patterns based on time-varying TPI. The traffic congestion of the macro road network is clustered into Mondays, Fridays, ordinary weekdays, congested weekdays, weekdays of winter and summer vacation, Saturdays, Sundays and festivals patterns. Patterns of Mondays and congested weekdays have a prominent morning peak, while patterns of Fridays, ordinary weekdays, and weekdays of winter and summer vacation have a prominent evening peak. Saturdays, Sundays and festivals are less congested than weekday patterns. It is verified that the SOM method proposed in this paper clusters traffic congestion into more detailed and accurate patterns, and it is applicable to TPI clustering in different years. The degree of congestion in 2021 increases by 7.15% in peak hours and decreases by 7.50% in off-peak hours compared with that in 2019 due to COVID-19. This method is helpful for traffic management in terms of making decisions according to different congestion patterns.

COVID-19 Deep Clustering: An Ontology construction clustering method with dynamic medical labeling

This paper introduces a novel clustering-based framework for COVID-19 ontology construction using Pubmed LitCovid scientific research articles data. Our study uses a semantic approach with hierarchical clustering to construct a more effective COVID-19 documents ontology with medical labeling and search. We believe this study may initiate a future development for an advanced COVID-19 domain-specific ontology. The significant contribution from this research addresses solving the limitations in manual classification tasks of the everyday fast-increasing number of scientific papers and the overloading of their unclassified knowledge. With this research, our provision will help scholars with a better search mechanism to retrieve highly relevant expert information about their favorite topics in the COVID-19-related literature. To our best knowledge, this approach is the first successful attempt to apply auto clustering with labeling and search on the COVID-19 research papers. Moreover, in text processing, we propose a systematical evaluation without dependence on standard data collection to evaluate our methodology. © 2022 ACM.

Clustering SARS-CoV-2 Variants from Raw High-Throughput Sequencing Reads Data

The massive amount of genomic data appearing over the past two years for SARS-CoV-2 has challenged traditional methods for studying the dynamics of the COVID-19 pandemic. As a result, new methods, such as the Pangolin tool, have appeared which can scale to the millions of samples of SARS-CoV-2 currently available. Such a tool is tailored to take assembled, aligned and curated full-length sequences, such as those provided by GISAID, as input. As high-throughput sequencing technologies continue to advance, such assembly, alignment and curation may become a bottleneck, creating a need for methods which can process raw sequencing reads directly. In this paper, we propose several alignment-free embedding approaches, which can generate a fixed-length feature vector representation directly from the raw sequencing reads, without the need for assembly. Moreover, because such an embedding is a numerical representation, it can be passed to already highly optimized clustering methods such as k-means. We show that the clusterings we obtain with the proposed embeddings are more suited to this setting than the Pangolin tool, based on several internal clustering evaluation metrics. Moreover, we show that a disproportionate number of positions in the spike region of the SARS-CoV-2 genome are informing such clusterings (in terms of information gain), which is consistent with current biological knowledge of SARS-CoV-2. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Who is Tweeting about COVID-19

During the COVID-19 pandemic many people used social media to seek information about the disease. In addition, these platforms were used to share news and opinions on issues related to COVID-19, such as vaccines and isolation policies. Extracting useful information for public health from these platforms efficiently poses some challenges due to the characteristics of social networks. Therefore, this article presents a method combining clustering and natural language processing for extracting information about users and their posts on social networks about COVID-19. The results show that the combination of clustering methods and textual analysis can reveal valuable information for public health using data from social networks. Future studies can employ this proposed method in order to get real-time information during future epidemics © 2022 IEEE.

The Role of Collaborative Resource Sharing in Supply Chain Recovery During Disruptions: A Systematic Literature Review

The COVID-19 crisis has attracted attention worldwide to supply chain disruptions and resilience. Several supply chain risk management approaches have been revisited or reapplied in the literature;however, collaborative resource sharing is less researched. This study aimed to investigate the current academic state of the art and advances in using collaborative resource sharing as a reactive method to facilitate supply chain recovery in the presence of disruptive events. More specifically we considered the role of different collaborative resource-sharing strategies that organizations can adopt to support supply chain functionalities during times of disruption. We conducted a systematic literature review (SLR) to analyze academic articles that were published online from 2000 to 2022. In order to analyze the literature, we adopted a combination of text-mining, automatic and manual categorization of selected articles, and exploratory analyses such as cluster analysis and relational indicators. We also consider the machine learning classification algorithm i.e. agglomerative hierarchical clustering for the categorization of clusters. The findings show that, for disruptive risks, collaborative sharing of labour and material resources is effective for the recovery of supply chains. More so, labour resources tend to contribute more to the recovery of supply chains through the physical and mental recreation of recovery activities and experiences. Whilst information resources and a mix of information and material resources are highly important in reducing the impact of COVID-19 disruptive supply chain risk. In conclusion, collaborating on the three resources, namely labour, material, and information resources can be an effective post-disruption recovery strategy for supply chains.

Density based geospatial clustering: Methods, Applications and Future Directions

The emergence of the novel corona virus disease (COVID-19) since 2019 has been a cause of significant concern for people throughout the world. While tremendous effort has been put in to it by healthcare facilities, both public and private, it would not be a stretch to state that the resources allotted were not enough to handle the floods of covid and the non-covid patients at the same time. As the entire world was under lockdown, it was considerably tougher for people to move around. This meant getting check-ups for covid was fairly tough. Thus, building up many hospital camps around a city became important. In this article, the locations of different healthcare institutions and residential flats in and around the city of Bhubaneswar were analysed. Clusters were generated out of highly dense regions utilising a number of unsupervised learning density based clustering techniques and the best model was picked among them. Folium leaflet maps in Python were used to show the clusters created from the best performing clustering method. This would allow us to collect crucial information identifying areas in severe need of medical attention. Thus, resources can be divided evenly among the population with the information acquired. © 2022 ACM.

Using Data Mining to Estimate Patterns of Contagion-Risk Interactions in an Intercity Public Road Transport System

The COVID-19 pandemic has had very negative effects on public transport systems. These effects have compromised the role they should play as enablers of social equity and environmentally sustainable mobility and have caused serious economic losses for public transport operators. For this reason, in the context of pandemics, meaningful epidemiological information gathered in the specific framework of these systems is of great interest. This article presents the findings of an investigation into the risk of transmission of a respiratory infectious disease in an intercity road transport system that carries millions of passengers annually. To achieve this objective, a data mining methodology was used to generate the data required to ascertain the level of risk. Using this methodology, the occupancy of vehicle seats by passengers was simulated using two different strategies. The first is an empirical approach to the behaviour of passengers when occupying a free seat and the second attempts to minimise the risk of contagion. For each of these strategies, the interactions with risk of infection between passengers were estimated, the patterns of these interactions on the different routes of the transport system were obtained using k-means clustering technique, and the impact of the strategies was analysed.

Applications of Machine Learning in Battling Against Novel COVID-19

In this paper, is to strongly analyze the Coronavirus Diseases (Covid-19) via utilizing the machine learning depended on classification as well as clustering method. Researchers' prediction will not only allow detection and pipeline to predict how much money their detection method for COVID-19 will make, but it will also allow them to justify their characteristics, such as type of infection and choice of vaccine to reach a certain detection using machine learning based model. In this way, it overcomes the challenge of new COVID-19 forecasting: the lack of historical data. With the machine learning algorithm, researchers provide prediction at 15 to 20 different methods with an accuracy above 80% after training. The training is performed on 80% of data while the testing is done on remaining 20% of data. Such prediction will also allow other interested third parties to predict the success of a COVID-19 detection before it is released on open-source community. In the process of prediction, some researchers found the variables most associated with COVID-19 detection, and to see how the various prediction models are affected by them. Nevertheless, those machines learning based methods can greatly benefit from modern artificial intelligence techniques for this purpose that can handle complex features and give out great prediction results. Therefore, employing historical COVID-19 data and using them in machine learning algorithms to predict disease could save companies millions of dollars on rather unsuccessful detection. © 2022 IEEE.

Classification of Chileans public hospitals based on healthcare production using clustering techniques

Governments worldwide have adopted different strategies fronting the pandemic associated with the SARS-CoV-2. These measures include different approaches to assign and manage the resources, control the spread of the virus, and mitigate the contagious. The technical efficiency allows evaluating the success rate in government managing and health facilities performance. Since technical efficiency is a relative measure, experts must compare the hospitals by adjusting their production according to the type of patient treated: case-mix. The literature recommends using the Related Groups for Diagnosis system (DRG) to adjust hospitals' production. However, only 80 of the more than 195 public hospitals have implemented this system in Chile, limiting the evaluation of technical efficiency. The Ministry of Health of Chile (MINSAL) has proposed an administrative categorisation for the public hospitals: high, medium, and low complexity. Managers can use this classification to group the hospitals avoiding bias. However, how good is this classification according to the data science point of view? In this work, we evaluate the categorisation proposed by MINSAL by applying internal clustering indexes and input features associated with healthcare production related to case-mix, considering different clustering techniques and an ad-hoc NSGA-II strategy. After evaluating alternative partitions where the latter obtains the best quality scores, we propose a new classification for technical efficiency analyses.

Two-stage Clustering Method for Discovering People's Perceptions: A Case Study of the COVID-19 Vaccine from Twitter

Twitter is currently one of the most influential microblogging services on which users interact with messages. It is imperative to grasp the big picture of Twitter through analyzing its huge stream data. In this study, we develop a two-stage clustering method that automatically discovers coarse-grained topics from Twitter data. In the first stage, we use graph clustering to extract micro-clusters from the word co-occurrence graph. All the tweets in a micro-cluster share a fine-grained topic. We then obtain the time series of each micro-cluster by counting the number of tweets posted in a time window. In the second stage, we use time series clustering to identify the clusters corresponding to coarse-grained topics. We evaluate the computational efficacy of the proposed method and demonstrate its systematic improvement in scalability as the data volume increases. Next, we apply the proposed method to large-scale Twitter data (26 million tweets) about the COVID-19 Vaccination in Japan. The proposed method separately identifies the reactions to news and the reactions to tweets. © 2021 IEEE.

Unsupervised clustering analysis of SARS-Cov-2 population structure reveals six major subtypes at early stage across the world

the population structure of the newly emerged coronavirus SARS-CoV-2 has significant potential to inform public health management and diagnosis. As SARS-CoV-2 sequencing data accrued, grouping them into clusters is important for organizing the landscape of the population structure of the virus. Due to the limited prior information on the newly emerged coronavirus, we utilized four different clustering algorithms to group 16, S73 SARS-CoV-2 strains, which automatically enables the identification of spatial structure for SARS-CoV-2. A total of six distinct genomic clusters were identified using mutation profiles as input features. Comparison of the clustering results reveals that the four algorithms produced highly consistent results, but the state-of-the-art unsupervised deep learning clustering algorithm performed best and produced the smallest intra-cluster pairwise genetic distances. The varied proportions of the six clusters within different continents revealed specific geographical distributions. In particular, our analysis found that Oceania was the only continent on which the strains were dispersively distributed into six clusters. In summary, this study provides a concrete framework for the use of clustering methods to study the global population structure of SARS-CoV-2. In addition, clustering methods can be used for future studies of variant population structures in specific regions of these fast-growing viruses. © 2021 IEEE.

Hybrid clustering and classification methods to find out the pattern of the spread of covid-19 in East Java province

Covid-19 entered Indonesia in March 2020, which had an extraordinary impact on all aspects of life. The real impact is the economic and social sectors. Various efforts and policies from the Indonesian government were made to suppress the increase in the rate of Covid-19 cases. The spread of Covid-19 cases in Indonesia has been evenly distributed in various provinces, one of which is East Java. East Java has 38 districts/cities, where the case of each district is different, there are green, yellow, red, and black zones. To find out the pattern of spread of each district/city zone, clustering is used. The method used in this study was to use the hybrid method of clustering with K-means and classification with its algorithm Decision Tree. The purpose of this study to prepare mitigation measures to inhibit the rate of spread of Covid-19 in East Java Province. The variables used include the number of positive, the number of dead, the number of recovered, the number of suspects, and the number of probable. With this grouped data, it is expected to help make the right decision in reducing the spread and minimizing the number of positive patients. Based on the results of the study, there will be cluster results, namely 7 very high-risk areas (C0), 1 high risk district (C1), 29 moderate risk districts (C2), and 1 low-risk district (C3). It also has a validity index level of 0.356 as measured by the Davies-Bouldin Index. © 2022 Institute of Physics Publishing. All rights reserved.

Word Clustering Using Graphical Lasso-Guided PCA for Trend Analysis of COVID-19

This paper proposes a word clustering method using graphical lasso-guided principal component analysis (PCA) for trend analysis of coronavirus disease (COVID-19). We define changes in daily frequencies of words on Twitter as trends, and clustering denotes to find similar trends. There is a problem that trends based on indirect correlations degrade the clustering performance. To address this problem, we newly develop graphical lasso-guided PCA. Specifically, graphical lasso is able to obtain a partial correlation matrix (a graph that represents direct correlations between trends). By calculating loadings of PCA to the partial correlation matrix (authority scores calculated by a hyperlink-induced topic search algorithm), accurate clustering becomes feasible. We conducted experiments by collecting Japanese tweets about COVID-19 from March 1, 2020 to April 30, 2020. The results show that our graphical lasso-guided PCA can distinguish two clusters before and after a state of emergency, unlike comparative method using indirect correlations. © 2021 IEEE.

Clustering of countries for COVID-19 cases based on disease prevalence, health systems and environmental indicators.

The coronavirus has a high basic reproduction number ( R 0 ) and has caused the global COVID-19 pandemic. Governments are implementing lockdowns that are leading to economic fallout in many countries. Policy makers can take better decisions if provided with the indicators connected with the disease spread. This study is aimed to cluster the countries using social, economic, health and environmental related metrics affecting the disease spread so as to implement the policies to control the widespread of disease. Thus, countries with similar factors can take proactive steps to fight against the pandemic. The data is acquired for 79 countries and 18 different feature variables (the factors that are associated with COVID-19 spread) are selected. Pearson Product Moment Correlation Analysis is performed between all the feature variables with cumulative death cases and cumulative confirmed cases individually to get an insight of relation of these factors with the spread of COVID-19. Unsupervised k-means algorithm is used and the feature set includes economic, environmental indicators and disease prevalence along with COVID-19 variables. The learning model is able to group the countries into 4 clusters on the basis of relation with all 18 feature variables. We also present an analysis of correlation between the selected feature variables, and COVID-19 confirmed cases and deaths. Prevalence of underlying diseases shows strong correlation with COVID-19 whereas environmental health indicators are weakly correlated with COVID-19.