Developing a resilient healthcare supply chain to prevent disruption in the wake of emergency health crisis

PurposeThe study aims to identify and analyse the drivers of resilient healthcare supply chain (HCSC) preparedness in emergency health outbreaks to prevent disruption in healthcare services delivery in the context of India.Design/methodology/approachThe present study has opted for the grey clustering method to identify and analyse the drivers of resilient HCSC preparedness during health outbreaks into high, moderate and low important grey classes based on Grey-Delphi, analytic hierarchy process (AHP) and Shannon's information entropy (IE) theory.FindingsThe drivers of the resilient HCSC are scrutinised using the Grey-Delphi technique. By implementing AHP and Shannon's IE theory and depending upon structure, process and outcome measures of HCSC, eleven drivers of a resilient HCSC preparedness are clustered as highly important, three drivers into moderately important, and two drivers into a low important group.Originality/valueThe analysis and insights developed in the present study would help to plan and execute a viable, resilient emergency HCSC preparedness during the emergence of any health outbreak along with the stakeholders' coordination. The results of the study offer information, rationality, constructiveness, and universality that enable the wider application of AHP-IE/Grey clustering analysis to HCSC resilience in the wake of pandemics.

COVID-19 Mythbusters in World Languages

This paper introduces a multi-lingual database containing translated texts of COVID-19 mythbusters. The database has translations into 115 languages as well as the original English texts, of which the original texts are published by World Health Organization (WHO). This paper then presents preliminary analyses on latin-alphabet-based texts to see the potential of the database as a resource for multilingual linguistic analyses. The analyses on latin-alphabet-based texts gave interesting insights into the resource. While the amount of translated texts in each language was small, character bi-grams with normalization (lowercasing and removal of diacritics) turned out to be an effective proxy for measuring the similarity of the languages, and the affinity ranking of language pairs could be obtained. Additionally, the hierarchical clustering analysis is performed using the character bigram overlap ratio of every possible pair of languages. The result shows the cluster of Germanic languages, Romance languages, and Southern Bantu languages. In sum, the multilingual database not only offers fixed set of materials in numerous languages, but also serves as a preliminary tool to identify the language family using text-based similarity measure of bigram overlap ratio.

Mathematical Analysis and Modeling of the Factors That Determine the Quality of Life in the City Councils of Chile

The quality of life index is an indicator published yearly since 2010 by the Institute on Urban and Territorial Studies and the Chilean Chamber of Construction, involving 99 municipalities and communes from the national territory. This research provides an approach to understanding how various dimensions and variables interact with quality of life in Chilean communes considering multiple factors and perspectives through information from public sources and social indicators. For the research, variables were analyzed considering demographic, sociodemographic, economics and urban indicators, where the model developed allows for an understanding of how the variables are related. In addition, it was discovered that education, own incomes, municipal spending and green areas directly relate to quality of life, while overcrowding and municipal funds negatively affect rates of communal welfare. Moreover, the variables chosen as explanatory variables allow for the development of an efficiency model. For this purpose, Cobb–Douglas and trans-logarithmic forms were tested, and it was found that Cobb–Douglas fits better to the data set and structures of the variables. The results of the efficiency model show that education, municipal funds and own incomes significantly affect efficiency, with a mean value of approximately 47%, minimum values close to 30% and maximum values of approximately 60%. Finally, a cluster analysis was developed through k-means, k-medoids and hierarchical clustering algorithms, where, in all cases, the results were similar, suggesting four groups with differences and variations in analyzed variables, especially in overcrowding, education, quality of life and wellness.

A Structural Validation of the Brief COPE Scale among Outpatients with Alcohol and Opioid Use Disorders.

Recovery from substance use disorder requires access to effective coping resources. The most widely self-reported questionnaire used to assess coping responses is the Brief COPE; however, different factorial structures were found in a variety of samples. This study aimed to examine across outpatients with substance use disorders the factor structure of the short dispositional French version of the Brief Coping Orientation to Problem Experienced (COPE) inventory. The French version of the Brief COPE was administered in a sample of 318 outpatients with alcohol or opioid substance use disorder. A clustering analysis on latent variables (CLV) followed by a confirmatory factorial analysis (CFA) was conducted to examine the factor structure of the scale. The internal consistency of the Brief COPE and its subscales were also studied. The analysis revealed a nine-factor structure with a revised 24-item version consisting of functional strategies (four items), problem-solving (four items), denial (two items), substance use (two items), social support seeking (four items), behavioral disengagement (two items), religion (two items), blame (two items), and humor (two items) that demonstrated a good fit to the data. This model explained 53% of the total variance with an overall McDonald's omega (ω) of 0.96 for the revised scale. The present work offers a robust and valid nine-factor structure for assessing coping strategies in French outpatients with opioid or alcohol substance use disorder. This structure tends to simplify its use and interpretation of results for both clinicians and researchers.

Is the pandemic masking waste management?–A review on fallout of the COVID-19 viral contagion

The global outbreak of SARS-CoV-2 has spurred a reassessment of Municipal Solid Waste management strategies and approaches. A significant need for sanitation and hygiene was accentuated for disease prevention and control with the onset of the pandemic. With an alteration of the status quo in waste management system, an unprecedented amount of face masks, protective equipment, and other biological wastes was generated in the form of Municipal Solid Waste. This upsurge of potentially infected wastes originated a risk of transmission amongst frontline workers. Furthermore, the potential contamination of Municipal Solid Waste was rendered as a legitimate threat due to improper collection practices, disposal and handling of solid waste. Several novel waste disposal techniques and waste management policies were also introduced during this period. However, the sanitation-policy making-occupational safety nexus remains inadequately explored under the prevalent COVID-19 scenario. Through the prism of shifting waste composition, this review offers a global assessment of existing solid waste management systems during the COVID-19 pandemic. The physiological and psychological hazards faced by the frontline workers were explored and instances of best-case and worst-case policies on solid waste handling were recorded. Modern methods of waste disposal and latest trends of policymaking were evaluated. A model study of unsupervised learning via Partition Around Medoids cluster analysis was undertaken to reveal underlying patterns of waste management policies. Although, the clusters were formed devoid of any socio-economic parameters, this study strives to indicate proof of concept and can serve as a precursor to advanced clustering studies. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

COVID-19 Mythbusters in World Languages

This paper introduces a multi-lingual database containing translated texts of COVID-19 mythbusters. The database has translations into 115 languages as well as the original English texts, of which the original texts are published by World Health Organization (WHO). This paper then presents preliminary analyses on latin-alphabet-based texts to see the potential of the database as a resource for multilingual linguistic analyses. The analyses on latin-alphabet-based texts gave interesting insights into the resource. While the amount of translated texts in each language was small, character bi-grams with normalization (lowercasing and removal of diacritics) turned out to be an effective proxy for measuring the similarity of the languages, and the affinity ranking of language pairs could be obtained. Additionally, the hierarchical clustering analysis is performed using the character bigram overlap ratio of every possible pair of languages. The result shows the cluster of Germanic languages, Romance languages, and Southern Bantu languages. In sum, the multilingual database not only offers fixed set of materials in numerous languages, but also serves as a preliminary tool to identify the language family using text-based similarity measure of bigram overlap ratio. © European Language Resources Association (ELRA), licensed under CC-BY-NC-4.0.

Clustering analysis of geriatric and acute characteristics in a cohort of very old patients on admission to ICU.

PURPOSE: The biological and functional heterogeneity in very old patients constitutes a major challenge to prognostication and patient management in intensive care units (ICUs). In addition to the characteristics of acute diseases, geriatric conditions such as frailty, multimorbidity, cognitive impairment and functional disabilities were shown to influence outcome in that population. The goal of this study was to identify new and robust phenotypes based on the combination of these features to facilitate early outcome prediction. METHODS: Patients aged 80 years old or older with and without limitations of life-sustaining treatment and with complete data were recruited from the VIP2 study for phenotyping and from the COVIP study for external validation. The sequential organ failure assessment (SOFA) score and its sub-scores taken on admission to ICU as well as demographic and geriatric patient characteristics were subjected to clustering analysis. Phenotypes were identified after repeated bootstrapping and clustering runs. RESULTS: In patients from the VIP2 study without limitations of life-sustaining treatment (n = 1977), ICU mortality was 12% and 30-day mortality 19%. Seven phenotypes with distinct profiles of acute and geriatric characteristics were identified in that cohort. Phenotype-specific mortality within 30 days ranged from 3 to 57%. Among the patients assigned to a phenotype with pronounced geriatric features and high SOFA scores, 50% died in ICU and 57% within 30 days. Mortality differences between phenotypes were confirmed in the COVIP study cohort (n = 280). CONCLUSIONS: Phenotyping of very old patients on admission to ICU revealed new phenotypes with different mortality and potential need for anticipatory intervention.

Data Mining and Network Pharmacology on Tibetan Medicine Prescriptions for the Prevention and Treatment of COVID-19

In this paper, the compatibility rules and drug combinations of Tibetan medicine prescriptions are explored, and the possible anti-epidemic mechanism is analyzed from the perspectives of biological network and signaling pathway, so as to provide reference for scientifically elaborating the potential value of ancient Tibetan medicine in preventing epidemic diseases. Association rules and clustering analysis are used for Tibetan drug mining. A total of 18 prescriptions involving 113 Tibetan medicinal materials were included, and 26 high-frequency Tibetan medicinal materials with the statistical frequency ≥3 are included, most of which are medicinal materials for clearing heat, detoxicating, eliminating plague. Thirteen potential drug combinations are obtained through association rule analysis. The KMean clustering and hierarchical clustering were used for clustering analysis to obtain five drug clusters, and the "Bamusaeconcretiosilicea - Carthami Flos"combination was selected for network pharmacology research by comparing the two methods. After drug target and pathway analysis, "Bamusaeconcretiosilicea - Carthami Flos"through MAPK cascade, response to oxygen level, reactive oxygen species metabolism process, PI3K-Akt signaling pathway, NF- κ B signaling pathway, cytokine-cytokine Receptor interaction and calcium signaling pathways have certain feasibility for the treatment of immune disease fever in three aspects: immune response, inflammatory response, and oxidative stress. © 2022 IEEE.

Clustering Analysis in the Student Academic Activities on COVID-19 Pandemic in Mexico

The pandemic caused by the COVID-19 disease has affected all aspects of the life of the people in every region of the world. The academic activities at universities in Mexico have been particularly disturbed by two years of confinement;all activities were migrated to an online modality where improvised actions and prolonged isolation have implied a significant threat to the educational institutions. Amid this pandemic, some opportunities to use Artificial Intelligence tools for understanding the associated phenomena have been raised. In this sense, we use the K-means algorithm, a well-known unsupervised machine learning technique, to analyze the data obtained from questionaries applied to students in a Mexican university to understand their perception of how the confinement and online academic activities have affected their lives and their learning. Results indicate that the K-means algorithm has better results when the number of groups is bigger, leading to a lower error in the model. Also, the analysis helps to make evident that the lack of adequate computing equipment, internet connectivity, and suitable study spaces impact the quality of the education that students receive, causing other problems, including communication troubles with teachers and classmates, unproductive classes, and even accentuate psychological issues such as anxiety and depression. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Annotating Protein Structures for Understanding SARS-CoV-2 Interactome

Protein Contact Network (PCN) is an emerging paradigm for modelling protein structure. A common approach to interpreting such data is through network-based analyses. It has been shown that clustering analysis may discover allostery in PCN. Nevertheless Network Embedding has shown good performances in discovering hidden communities and structures in network. SARS-CoV-2 proteins, and in particular S protein, have a modular structure that need to be annotated to understand complex mechanism of infections. Such annotations, and in particular the highlighting of regions participating in the binding of human ACE2 and TMPRSS, may help the design of tailored strategy for preventing and blocking infection. In this work, we compare some approaches for graph embedding with respect to some classical clustering approaches for annotating protein structures. Results shows that embedding may reveal interesting structure that constitute the starting point for further analysis. © 2022 CEUR-WS. All rights reserved.

Spatiotemporal clusters and the socioeconomic determinants of COVID-19 in Toronto neighbourhoods, Canada.

The aim of this study is to identify spatiotemporal clusters and the socioeconomic drivers of COVID-19 in Toronto. Geographical, epidemiological, and socioeconomic data from the 140 neighbourhoods in Toronto were used in this study. We used local and global Moran's I, and space-time scan statistic to identify spatial and spatiotemporal clusters of COVID-19. We also used global (spatial regression models), and local geographically weighted regression (GWR) and Multiscale Geographically weighted regression (MGWR) models to identify the globally and locally varying socioeconomic drivers of COVID-19. The global regression model identified a lower percentage of educated people and a higher percentage of immigrants in the neighbourhoods as significant predictors of COVID-19. MGWR shows the best fit model to explain the variables affecting COVID-19. The findings imply that a single intervention package for the entire area would not be an effective strategy for controlling COVID-19; a locally adaptable intervention package would be beneficial.

Survey on Highly Imbalanced Multi-class Data

Machine learning technology has a massive impact on society because it offers solutions to solve many complicated problems like classification, clustering analysis, and predictions, especially during the COVID-19 pandemic. Data distribution in machine learning has been an essential aspect in providing unbiased solutions. From the earliest literatures published on highly imbalanced data until recently, machine learning research has focused mostly on binary classification data problems. Research on highly imbalanced multi-class data is still greatly unexplored when the need for better analysis and predictions in handling Big Data is required. This study focuses on reviews related to the models or techniques in handling highly imbalanced multi-class data, along with their strengths and weaknesses and related domains. Furthermore, the paper uses the statistical method to explore a case study with a severely imbalanced dataset. This article aims to (1) understand the trend of highly imbalanced multi-class data through analysis of related literatures;(2) analyze the previous and current methods of handling highly imbalanced multi-class data;(3) construct a framework of highly imbalanced multi-class data. The chosen highly imbalanced multi-class dataset analysis will also be performed and adapted to the current methods or techniques in machine learning, followed by discussions on open challenges and the future direction of highly imbalanced multi-class data. Finally, for highly imbalanced multi-class data, this paper presents a novel framework. We hope this research can provide insights on the potential development of better methods or techniques to handle and manipulate highly imbalanced multi-class data. © 2022. International Journal of Advanced Computer Science and Applications.All Rights Reserved

Cluster Analysis of US COVID-19 Infected States for Vaccine Distribution.

Since December 2019, COVID-19 has been raging worldwide. To prevent the spread of COVID-19 infection, many countries have proposed epidemic prevention policies and quickly administered vaccines, However, under facing a shortage of vaccines, the United States did not put forward effective epidemic prevention policies in time to prevent the infection from expanding, resulting in the epidemic in the United States becoming more and more serious. Through "The COVID Tracking Project", this study collects medical indicators for each state in the United States from 2020 to 2021, and through feature selection, each state is clustered according to the epidemic's severity. Furthermore, through the confusion matrix of the classifier to verify the accuracy of the cluster analysis, the study results show that the Cascade K-means cluster analysis has the highest accuracy. This study also labeled the three clusters of the cluster analysis results as high, medium, and low infection levels. Policymakers could more objectively decide which states should prioritize vaccine allocation in a vaccine shortage to prevent the epidemic from continuing to expand. It is hoped that if there is a similar epidemic in the future, relevant policymakers can use the analysis procedure of this study to determine the allocation of relevant medical resources for epidemic prevention according to the severity of infection in each state to prevent the spread of infection.

Methods Used in the Spatial and Spatiotemporal Analysis of COVID-19 Epidemiology: A Systematic Review.

The spread of the COVID-19 pandemic was spatially heterogeneous around the world; the transmission of the disease is driven by complex spatial and temporal variations in socioenvironmental factors. Spatial tools are useful in supporting COVID-19 control programs. A substantive review of the merits of the methodological approaches used to understand the spatial epidemiology of the disease is hardly undertaken. In this study, we reviewed the methodological approaches used to identify the spatial and spatiotemporal variations of COVID-19 and the socioeconomic, demographic and climatic drivers of such variations. We conducted a systematic literature search of spatial studies of COVID-19 published in English from Embase, Scopus, Medline, and Web of Science databases from 1 January 2019 to 7 September 2021. Methodological quality assessments were also performed using the Joanna Briggs Institute (JBI) risk of bias tool. A total of 154 studies met the inclusion criteria that used frequentist (85%) and Bayesian (15%) modelling approaches to identify spatial clusters and the associated risk factors. Bayesian models in the studies incorporated various spatial, temporal and spatiotemporal effects into the modelling schemes. This review highlighted the need for more local-level advanced Bayesian spatiotemporal modelling through the multi-level framework for COVID-19 prevention and control strategies.

Developing a resilient healthcare supply chain to prevent disruption in the wake of emergency health crisis

Purpose: The study aims to identify and analyse the drivers of resilient healthcare supply chain (HCSC) preparedness in emergency health outbreaks to prevent disruption in healthcare services delivery in the context of India. Design/methodology/approach: The present study has opted for the grey clustering method to identify and analyse the drivers of resilient HCSC preparedness during health outbreaks into high, moderate and low important grey classes based on Grey-Delphi, analytic hierarchy process (AHP) and Shannon's information entropy (IE) theory. Findings: The drivers of the resilient HCSC are scrutinised using the Grey-Delphi technique. By implementing AHP and Shannon's IE theory and depending upon structure, process and outcome measures of HCSC, eleven drivers of a resilient HCSC preparedness are clustered as highly important, three drivers into moderately important, and two drivers into a low important group. Originality/value: The analysis and insights developed in the present study would help to plan and execute a viable, resilient emergency HCSC preparedness during the emergence of any health outbreak along with the stakeholders' coordination. The results of the study offer information, rationality, constructiveness, and universality that enable the wider application of AHP-IE/Grey clustering analysis to HCSC resilience in the wake of pandemics. © 2022, Emerald Publishing Limited.

Spatiotemporal Mapping of Online Interest in Cannabis and Popular Psychedelics before and during the COVID-19 Pandemic in Poland.

BACKGROUND: Psychedelics represent a unique subset of psychoactive substances that can induce an aberrant state of consciousness principally via the neuronal 5-HT2A receptor. There is limited knowledge concerning the interest in these chemicals in Poland and how they changed during the pandemic. Nonetheless, these interests can be surveyed indirectly via the web. OBJECTIVES: We aim to conduct a spatial-temporal mapping of online information-seeking behavior concerning cannabis and the most popular psychedelics before and during the pandemic. METHODS: We retrieved online information search data via Google Trends concerning twenty of the most popular psychedelics from 1 January 2017 to 1 January 2022 in Poland. We conducted Holt-Winters exponential smoothing for time series analysis to infer potential seasonality. We utilized hierarchical clustering analysis based on Ward's method to find similarities of psychedelics' interest within Poland's voivodships before and during the pandemic. RESULTS: Twelve (60%) psychedelics had significant seasonality; we proved that psilocybin and ayahuasca had annual seasonality (p-value = 0.0120 and p = 0.0003, respectively), and four substances-LSD, AL-LAD, DXM, and DOB-exhibited a half-yearly seasonality, while six psychedelics had a quarterly seasonal pattern, including cannabis, dronabinol, ergine, NBOMe, phencyclidine, and salvinorin A. Further, the pandemic influenced a significant positive change in the trends for three substances, including psilocybin, ergine, and DXM. CONCLUSIONS: Different seasonal patterns exist for psychedelics, and some might correlate with school breaks or holidays in Poland. The pandemic induced some changes in the temporal and spatial trends. The spatial-temporal trends could be valuable information to health authorities and policymakers responsible for monitoring and preventing addictions.

COVID-19 CLUSTERING BY PROVINCE: A CASE STUDY OF COVID-19 CASES IN INDONESIA

The 2019 novel coronavirus disease (COVID-19) pandemic in Indonesia has caused issues in many sectors such as health, economy, and education. Several actions had been taken by the government to prevent and forestall the spread of the coronavirus infection. However, right now there are still many new cases emerging especially in cities with dense population. In the meantime, actions taken from the government are based on the classification of the severity of new cases;there are red zone, yellow zone and green zone. Therefore, mapping cities into zone is critical because it concerns the right decision to be implemented. This paper aimed to cluster the severity of each province in Indonesia based on the number of cases, recovered, and casualties using 3 clustering methods namely K-Means, K-Medoids, and Gaussian mixture model. The result shows that the most optimal clustering method is the Gaussian mixture model, while the least optimal method for clustering is the K-Means. Furthermore, it is also discovered that the cluster always changes overtime, and the cluster can shift depending on the corresponding parameter. © 2022 ICIC International.

Digital Phenotyping for Spinal Cord Injury: Smartphone-based monitors for clinical utility

Spinal Cord injury (SCI) significantly affects all parts of life, and mental illness and social isolation are common and often undetected after discharge from traditional care. Mobile health and sensor monitoring have emerged as convenient and beneficial supplements to clinical care, even more so with restricted in-person health care during COVID-19. We apply these in SCI to collect and analyze in-situ active self-report as well as passive sensor data from personal smartphones to infer results and correlations between their psychosocial and physical well-being. We have applied Autoregressive Integrated Moving Average (ARIMA) to understand time dependent relationships between depression severity, social interaction, and community mobility, and explored clustering analysis and parallel predictive models to inform just-in-time adaptive interventions. Preliminary analyses suggest that smartphones, as a symptom monitoring tool and to deliver an in-situ individualized intervention have potential to positively impact depression severity and community participation after SCI.

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.

Benchmarking the recovery of air travel demands for US airports during the COVID-19 Pandemic.

The aviation industry has gone through numerous ups and downs in the past decades. Despite the devastating damage caused by the COVID-19 Pandemic, the aviation industry worldwide still manages to bounce back from the abyss of Q2, 2020, though the speed of recovery is less than satisfactory for most regions. Being aware of the existing literature on air travel demands published since March 2020, this study aims to provide US Primary Hub airports with benchmarks that can help airports predict the recovery of air travel demand during the COVID-19 Pandemic. This study uses the passenger numbers going through airport security checkpoints as the input data and the k-shape clustering algorithm to group airports by their travel demand recovery patterns. The clustering analysis results are presented in a circular dendrogram so that any of the 118 subject airports can quickly locate their benchmarking airports. In this process, the geographic location and hub category of an airport are found to play important roles in determining how local outbound traffic recovers during the Pandemic. We also test if state political preference in the 2020 Presidential Election affects local airport traffic but cannot find any convincing results. The method used by this study can be fed with up-to-date data to produce more timely and reliable results to guide airports and other stakeholders through the recovery journey.