The Impact of Socioeconomic and Demographic Factors on COVID-19 Forecasting Model

Background: COVID-19 has become a primary public health issue in various countries across the world. The main difficulty in managing outbreaks of infectious diseases is due to the difference in geographical, demographic, economic inequalities and people's behavior in each region. The spread of disease acts like a series of diverse regional outbreaks;each part has its disease transmission pattern. Objective: This study aims to assess the association of socioeconomic and demographic factors to COVID-19 cases through cluster analysis and forecast the daily cases of COVID-19 in each cluster using a predictive modeling technique. Methods: This study applies a hierarchical clustering approach to group regencies and cities based on their socioeconomic and demographic similarities. After that, a time-series forecasting model, Facebook Prophet, is developed in each cluster to assess the transmissibility risk of COVID-19 over a short period of time. Results: A high incidence of COVID-19 was found in clusters with better socioeconomic conditions and densely populated. The Prophet model forecasted the daily cases of COVID-19 in each cluster, with Mean Absolute Percentage Error (MAPE) of 0.0869;0.1513;and 0.1040, respectively, for cluster 1, cluster 2, and cluster 3. Conclusion: Socioeconomic and demographic factors were associated with different COVID-19 waves in a region. From the study, we found that considering socioeconomic and demographic factors to forecast COVID-19 cases played a crucial role in determining the risk in that area. © 2023 The Authors. Published by Universitas Airlangga.

Characteristics of Traditional Chinese Medicine Syndromes and Treatments of COVID-19 Patients from Two Hospitals Based on "Treatment of Disease in Accordance with Three Conditions".

Objective: To explore the guidance value of "treatment of disease in accordance with three conditions" theory in the prevention and treatment of corona virus disease 2019 (COVID-19) based on the differences of syndromes and traditional Chinese medicine (TCM) treatments in COVID-19 patients from Xingtai Hospital of Chinese Medicine of Hebei province and Ruili Hospital of Chinese Medicine and Dai Medicine of Yunnan province and discuss its significance in the prevention and treatment of the unexpected acute infectious diseases. Method(s): Demographics data and clinical characteristics of COVID-19 patients from the two hospitals were collected retrospectively and analyzed by SPSS 18.0. The information on formulas was obtained from the hospital information system (HIS) of the two hospitals and analyzed by the big data intelligent processing and knowledge service system of Guangdong Hospital of Chinese Medicine for frequency statistics and association rules analysis. Heat map-hierarchical clustering analysis was used to explore the correlation between clinical characteristics and formulas. Result(s): A total of 175 patients with COVID-19 were included in this study. The 70 patients in Xingtai, dominated by young and middle-aged males, had clinical symptoms of fever, abnormal sweating, and fatigue. The main pathogenesis is stagnant cold-dampness in the exterior and impaired yin by depressed heat, with manifest cold, dampness, and deficiency syndromes. The therapeutic methods highlight relieving exterior syndrome and resolving dampness, accompanied by draining depressed heat. The core Chinese medicines used are Poria, Armeniacae Semen Amarum, Gypsum Fibrosum, Citri Reticulatae Pericarpium, and Pogostemonis Herba. By contrast, the 105 patients in Ruili, dominated by young females, had atypical clinical symptoms, and most of them were asymptomatic patients or mild cases. The main pathogenesis is dampness obstructing the lung and the stomach, with obvious dampness and heat syndromes. The therapeutic methods are mainly invigorating the spleen, resolving dampness, and dispersing Qi with light drugs. The core Chinese medicines used are Poria, Atractylodis Macrocephalae Rhizoma, Glycyrrhizae Radix et Rhizoma, Coicis Semen, Platycodonis Radix, Lonicerae Japonicae Flos, and Pogostemonis Herba. Conclusion(s): The differences in clinical characteristics, TCM syndromes, and medication of COVID-19 patients from the two places may result from different regions, population characteristics, and the time point of the COVID-19 outbreak. The "treatment of disease in accordance with three conditions" theory can help to understand the internal correlation and guide the treatments.Copyright © 2022, China Academy of Chinese Medical Sciences Institute of Chinese Materia Medica. All rights reserved.

Machine learning uncovers blood test patterns subphenotypes at hospital admission discerning increased 30-day ICU mortality rates in COVID-19 elderly patients

Introduction: Since March 2020, a number of SARS-CoV-2 patients have frequently required intensive care unit (ICU) admission, associated with moderate survival outcomes and an increasing economic burden. Elderly patients are among the most numerous, due to previous comorbidities and complications they develop during hospitalization [1]. For this reason, a reliable early risk stratification tool could help estimate an early prognosis and allow for an appropriate resources allocation in favour of the most vulnerable and critically ill patients. Method(s): This retrospective study includes data from two Spanish hospitals, HU12O (Madrid) and HCUV (Valencia), from 193 patients aged > 64 with COVID-19 between February and November 2020 who were admitted to the ICU. Variables include demographics, full-blood-count (FBC) tests and clinical outcomes. Machine learning applied a non-linear dimensionality reduction by t-distributed stochastic neighbor embedding (t-SNE) [2];then hierarchical clustering on the t-SNE output was performed. The number of clinically relevant subphenotypes was chosen by combining silhouette and elbow coefficients, and validated through exploratory analysis. Result(s): We identified five subphenotypes with heterogeneous interclustering age and FBC patterns (Fig. 1). Cluster 1 was the 'healthiest' phenotype, with 2% 30-day mortality and characterized by moderate leukocytes and eosinophils. Cluster 5, the severe phenotype, showed 44% 30-day mortality and was characterized by the highest leukocyte, neutrophil and platelet count and minimal monocytes and lymphocyte count. Clusters 2-4 displayed intermediate mortality rates (20-28%). Conclusion(s): The findings of this preliminary report of Eld-ICUCOV19 patients suggest the patient's FBC and age can display discriminative patterns associated with disparate 30-day ICU mortality rates.

Cytokine Profile in Different Post-Covid-19 Condition Phenotypes

Background: At least 10% of SARS-CoV-2 infected patients suffer from persistent symptoms for >12 weeks, known as post-COVID-19 condition (PCC) or Long Covid. Reported symptomatology is diverse with >200 physical and neurological debilitating symptoms. Here, we analyzed pro-inflammatory cytokine levels as a potential mechanism underlying persistent symptomatology. Method(s): Clinical data and samples used belong to the KING cohort extension, which includes clinically well characterized PCC (N=358, 59 persistent symptoms evaluated), COVID-19 recovered and uninfected subjects. We used Gower distances to calculate symptom's similarity between PCC and Ward's hierarchical clustering method to identify different symptom patterns among PCC patients. Cytokine levels of randomly selected PCC, recovered and uninfected subjects (N=193) were measured on plasma samples collected >6 months after acute infection using the 30-Plex Panel for Luminex. Mann- Whitney t-test was used to compare PCC vs recovered groups and Kruskal-Wallis t-test for >2 groups comparisons (PCC vs recovered vs Uninfected and within PCC clusters). FDR correction was applied for statistical significance (p-adj). Result(s): Hierarchical clustering identified 5 different PCC clusters according to their symptomatology, where PCC3 and PCC5 clusters showed higher prevalence of women ( >80%) and more persistent symptoms, while acute COVID-19 was mild in >80% of the patients. We selected 91 PCC (belonging to each cluster), 57 recovered and 45 uninfected subjects for cytokine profiling (Table 1). 13 soluble markers were significantly elevated (IL-1beta, Eotaxin, MIP-1beta, MCP-1, IL-15, IL-5, HGF, IFN-alpha, IL-1RA, IL-7, MIG, IL-4 and IL-8) in PCC and recovered groups compared to uninfected subjects (all p-adj< 0.04). In addition, PCC subjects tended towards higher levels of IL-1RA compared to recovered group (padj= 0.071). Within PCC clusters, FGF-basic and RANTES were elevated while IL-2 and MIG were decreased in PCC3 and PCC5 compared to the other PCC clusters (all p-adj< 0.04). TNF-alpha, IP-10, G-CSF and MIP-1alpha were decreased in PCC3 and PCC5 not reaching statistical significance (all p-adj=0.07). Conclusion(s): Some cytokines remained altered in all SARS-CoV-2 infected subjects independently of persistent symptoms after 6 months from acute infection. Differences between PCC and recovered individuals are limited after correction. Importantly, PCC cytokine profiles showed differences between clusters, which suggests different PCC subsyndromes with distinct etiology. Subjects Characteristics (Table Presented).

Distinct clinical phenotypes of critically ill COVID-19 patients: a cohort observational study

Introduction: COVID-19 presents a complex pathophysiology and evidence collected points towards an intricated interaction of viraldependent and individual immunological mechanisms. The identification of phenotypes, through clinical and biological markers, may provide a better understanding of the subjacent mechanisms and an early patient-tailored characterization of illness severity. Method(s): Multicenter prospective cohort study performed in 5 hospitals of Portugal and Brazil, during one year, between 2020-2021. All adult patients with an Intensive Care Unit admission with SARS-CoV-2 pneumonia were eligible. COVID-19 was diagnosed using clinical and radiologic criteria with a SARS-CoV-2 positive RT-PCR test. A two-step hierarchical cluster analysis was made using several class-defining variables. Result(s): 814 patients were included. The cluster analysis revealed a three-class model, allowing for the definition of three distinct COVID- 19 phenotypes: 244 patients in phenotype A, 163 patients in phenotype B, and 407 patients in phenotype C. Patients included in the phenotype C were significantly older, with higher baseline inflammatory biomarkers profile, and significantly higher requirement of organ support and mortality rate (Table 1 ( P062)). Phenotypes A and B demonstrated some overlapping clinical characteristics but different outcomes. Phenotype B patients presented a lower mortality rate, with consistently lower C-reactive protein, but higher procalcitonin and interleukin-6 serum levels, describing an immunological profile significantly different from phenotype A (Table 1). Conclusion(s): Severe COVID-19 patients exhibit three different clinical phenotypes with distinct profiles and outcomes. Their identification could have an impact in patients' care, justifying different therapy responses and inconsistencies identified across different randomized control trials results.

Exploring COVID-19 transmission patterns and key factors during epidemics caused by three major strains in Asia.

The COVID-19 epidemic has lasted for more than two years since the outbreak in late 2019. An urgent and challenging question is how to systematically evaluate epidemic developments in different countries, during different periods, and to determine which measures that could be implemented are key for successful epidemic prevention. In this study, SBD distance-based K-shape clustering and hierarchical clustering methods were used to analyse epidemics in Asian countries. For the hierarchical clustering, epidemic time series were divided into three periods (epidemics induced by the Original/Alpha, Delta and Omicron variants separately). Standard deviations, the Hurst index, mortality rates, peak value of confirmed cases per capita, average growth rates, and the control efficiency of each period were used to characterize the epidemics. In addition, the total numbers of cases in the different countries were analysed by correlation and regression in relation to 15 variables that could have impacts on COVID-19. Finally, some suggestions on prevention and control measures for each category of country are given. We found that the total numbers of cases per million of a population, total deaths per million and mortality rates were highly correlated with the proportion of people aged over 65 years, the prevalence of multiple diseases, and the national GDP. We also found significant associations between case numbers and vaccination rates, health expenditures, and stringency of control measures. Vaccinations have played a positive role in COVID-19, with a gradual decline in mortality rates in later periods, and are still playing protective roles against the Delta and Omicron strains. The stringency of control measures taken by a government is not an indicator of the appropriateness of a country's response to the outbreak, and a higher index does not necessarily mean more effective measures; a combination of factors such as national vaccination rates, the country's economic foundation and the availability of medical equipment is also needed. This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics".

Identifying responders to elamipretide in Barth syndrome: Hierarchical clustering for time series data.

BACKGROUND: Barth syndrome (BTHS) is a rare genetic disease that is characterized by cardiomyopathy, skeletal myopathy, neutropenia, and growth abnormalities and often leads to death in childhood. Recently, elamipretide has been tested as a potential first disease-modifying drug. This study aimed to identify patients with BTHS who may respond to elamipretide, based on continuous physiological measurements acquired through wearable devices. RESULTS: Data from a randomized, double-blind, placebo-controlled crossover trial of 12 patients with BTHS were used, including physiological time series data measured using a wearable device (heart rate, respiratory rate, activity, and posture) and functional scores. The latter included the 6-minute walk test (6MWT), Patient-Reported Outcomes Measurement Information System (PROMIS) fatigue score, SWAY Balance Mobile Application score (SWAY balance score), BTHS Symptom Assessment (BTHS-SA) Total Fatigue score, muscle strength by handheld dynamometry, 5 times sit-and-stand test (5XSST), and monolysocardiolipin to cardiolipin ratio (MLCL:CL). Groups were created through median split of the functional scores into "highest score" and "lowest score", and "best response to elamipretide" and "worst response to elamipretide". Agglomerative hierarchical clustering (AHC) models were implemented to assess whether physiological data could classify patients according to functional status and distinguish non-responders from responders to elamipretide. AHC models clustered patients according to their functional status with accuracies of 60-93%, with the greatest accuracies for 6MWT (93%), PROMIS (87%), and SWAY balance score (80%). Another set of AHC models clustered patients with respect to their response to treatment with elamipretide with perfect accuracy (all 100%). CONCLUSIONS: In this proof-of-concept study, we demonstrated that continuously acquired physiological measurements from wearable devices can be used to predict functional status and response to treatment among patients with BTHS.

Trends and factors associated with daily number of new cases of COVID-19 in the early stage of the pandemic: A worldwide opendata study

Objectives: We analyzed global trends in the daily number of new cases during the first wave of COVID-19 and factors associated with these trends. Methods: Data from 151 countries were analyzed. The index date for each country was set with consideration for a 7-day moving average (MA7) of ≥100 people. Data were collected for 60 and 90 days from the index date. Time-series hierarchical clustering was used to analyze the trends in the number of new cases in each country on the basis of their MA7 values. Multinomial logistic regression was performed to identify factors associated with these trends. Results: The trends in the daily number of new cases in the early stage of COVID-19 were classified into growth, declines, and smooth declines. The number of cases in countries with ≥25.60% residents with obesity (odds ratio = 6.69;p = 0.004) was more likely to exhibit growth than were those with obesity of 9.60-20.79%. The number in countries with a GDP of ≥US$34,341 (odds ratio = 0.10;p = 0.001) was more likely to exhibit a decline than were those with a GDP of US$5,277–14,932. Conclusions: COVID-19 epidemic prevention policies should account for country-specific characteristics such as the proportion of residents with obesity and GDP. © 2022, Taiwan Public Health Association. All rights reserved.

Trends and factors associated with daily number of new cases of COVID-19 in the early stage of the pandemic: A worldwide opendata study

Objectives: We analyzed global trends in the daily number of new cases during the first wave of COVID-19 and factors associated with these trends. Methods: Data from 151 countries were analyzed. The index date for each country was set with consideration for a 7-day moving average (MA7) of ≥100 people. Data were collected for 60 and 90 days from the index date. Time-series hierarchical clustering was used to analyze the trends in the number of new cases in each country on the basis of their MA7 values. Multinomial logistic regression was performed to identify factors associated with these trends. Results: The trends in the daily number of new cases in the early stage of COVID-19 were classified into growth, declines, and smooth declines. The number of cases in countries with ≥25.60% residents with obesity (odds ratio = 6.69;p = 0.004) was more likely to exhibit growth than were those with obesity of 9.60-20.79%. The number in countries with a GDP of ≥US$34,341 (odds ratio = 0.10;p = 0.001) was more likely to exhibit a decline than were those with a GDP of US$5,277–14,932. Conclusions: COVID-19 epidemic prevention policies should account for country-specific characteristics such as the proportion of residents with obesity and GDP. © 2022, Taiwan Public Health Association. All rights reserved.

Application of Machine Learning in the Fight against the COVID-19 Pandemic: A Review

Introduction: Machine learning (ML) plays a significant role in the fight against the COVID-19 (officially known as SARS-CoV-2) pandemic. ML techniques enable the rapid detection of patterns and trends in large datasets. Therefore, ML provides efficient methods to generate knowledge from structured and unstructured data. This potential is particularly significant when the pandemic affects all aspects of human life. It is necessary to collect a large amount of data to identify methods to prevent the spread of infection, early detection, reduction of consequences, and finding appropriate medicine. Modern information and communication technologies (ICT) such as the Internet of Things (IoT) allow the collection of large amounts of data from various sources. Thus, we can create predictive ML-based models for assessments, predictions, and decisions. Method(s): This is a review article based on previous studies and scientifically proven knowledge. In this paper, bibliometric data from authoritative databases of research publications (Web of Science, Scopus, PubMed) are combined for bibliometric analyses in the context of ML applications for COVID-19. Aim(s): This paper reviews some ML-based applications used for mitigating COVID-19. We aimed to identify and review ML potentials and solutions for mitigating the COVID-19 pandemic as well as to present some of the most commonly used ML techniques, algorithms, and datasets applied in the context of COVID-19. Also, we provided some insights into specific emerging ideas and open issues to facilitate future research. Conclusion(s): ML is an effective tool for diagnosing and early detection of symptoms, predicting the spread of a pandemic, developing medicines and vaccines, etc.Copyright © 2022 Sciendo. All rights reserved.

A segmented machine learning modeling approach of social media for predicting occupancy

Purpose: This study aims to predict hotel demand through text analysis by investigating keyword series to increase demand predictions' precision. To do so, this paper presents a framework for modeling hotel demand that incorporates machine learning techniques. Design/methodology/approach: The empirical forecasting is conducted by introducing a segmented machine learning approach of leveraging hierarchical clustering tied to machine learning and deep learning techniques. These features allow the model to yield more precise estimates. This study evaluates an extensive range of social media-derived words with the most significant probability of gradually establishing an understanding of an optimal outcome. Analyzes were performed on a major hotel chain in an urban market setting within the USA. Findings: The findings indicate that while traditional methods, being the naive approach and ARIMA models, struggled with forecasting accuracy, segmented boosting methods (XGBoost) leveraging social media predict hotel occupancy with greater precision for all examined time horizons. Additionally, the segmented learning approach improved the forecasts' stability and robustness while mitigating common overfitting issues within a highly dimensional data set. Research limitations/implications: Incorporating social media into a segmented learning framework can augment the current generation of forecasting methods' accuracy. Moreover, the segmented learning approach mitigates the negative effects of market shifts (e.g. COVID-19) that can reduce in-production forecasts' life-cycles. The ability to be more robust to market deviations will allow hospitality firms to minimize development time. Originality/value: The results are expected to generate insights by providing revenue managers with an instrument for predicting demand. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Analytic hierarchy clustering research of all cargo airlines based on safety information

Due to the COVID-19, air passenger transport industry is sluggishm. At the same time, based on the strong demand for freight business. Airlines focus on developing cargo sector. All-cargo airlines are gradually being established. The number of registered cargo aircraft has gradually increased in recent years,and cargo transport flight hours are gradually increasing. This leads to an increase in cargo aviation security incidents. The safety information analysis of cargo aircraft needs to be solved urgently. In this paper, a hierarchical analysis and clustering research on all-cargo airlines based on China aviaiton safety information data is carried out. The results show that all-cargo airlines security incidents are divided into three categories. The paper provides security recommendations for different incident categories. © 2022 IEEE.

Demonstration of stable clusters of symptoms in long COVID

Background. Long COVID is a heterogenous condition. We previously demonstrated distinct phenotypes of long COVID, but the impact of later waves caused by SARS-CoV-2 variants on long COVID presentations has not been described. Methods. We selected individuals with ongoing symptoms > 4 weeks from PCR-confirmed COVID-19 in a multicentre, prospective cohort study. We used multiple correspondence analysis and hierarchical clustering on self-reported symptoms to identify symptom clusters, in individuals recruited during two periods;cohort 1 from March 2020 to April 2021, and cohort 2 from April 2021 to March 2022. We explored differences in symptoms by mapping acute infection to one of four COVID-19 waves in Ireland (table 1) as well as vaccination status, and used Chi2 test to compare symptoms frequencies. Results. Demographics are shown in Table 2. Cluster analysis of each cohort demonstrated 3 distinct clusters in both cohorts, which shared similar clinical characteristics;a musculoskeletal/pain symptom cluster, a cardiorespiratory cluster and a third less symptomatic cluster (Figure 1). While symptoms within clusters were similar across both periods, in the cardiorespiratory cluster, the frequency of palpitations decreased (56% vs 16%) and cough increased (14% vs 45%) between reporting periods (both P< 0.01). Furthermore, a greater proportion of palpitations were reported in those with COVID-19 from waves 1 and 2 (35% and 28%) compared to 3 and 4 (both 12%, P< 0.001), and a greater proportion of chest pain in waves 1, 2 and 4 compared to wave 3. There were no differences in other symptoms (Table 3). Additionally there were significantly less palpitations reported in those vaccinated at the time of review (17% vs 31% P=0.002), but not chest pain (30% vs 39% P=0.13). In multivariate analysis, infection in wave 3 and 4 but not vaccination status remained significantly associated with lower reported palpitations (OR (95% CI) 0.28 (0.13-0.97) and 0.5 (0.06-0.87) for waves 3 and 4, both P< 0.05), and wave 3 infection remained independently associated with lower reported chest pain (OR 0.3 (0.25-0.7)). Conclusion. Three symptom clusters define long COVID across the two cohorts, but characteristics of the cardiorespiratory phenotype have evolved over time with evolution of SARS-CoV-2 variants. (Table Presented).

Solving the problem of antibody grouping based on cross-inhibition index using hierarchical clustering methods

Due to increasing number of viral diseases (including Covid-19) rapid research with the purpose of their detection, prevention, and treatment is crucial. This article considers a problem of finding two optimal antibodies to any virus that is important for detection of disease and development of tests but not for creation of vaccine. It is worth noting that the target protein (nucleoprotein), described in this article, is the only generally established target for SARSCoV-2 diagnostics, using antigen rapid tests or any other antigen detection tools. Possible ways of solving the aforementioned problem were described using hierarchical clustering algorithm with different linkage methods. Affirmative results of dividing antibodies into groups were achieved. © 2022 Copyright for this paper by its authors.

Game Popularity Level During Covid-19 Pandemic Using Agglomerative Hierarchical Clustering

During the COVID-19 pandemic, various activities of people outside the home were disrupted and made people move more indoors. For some companies take advantage of this pandemic period as their advantage, especially digital game industry companies. Various games have been released and promoted, these games are published on various game platforms. Currently, Steam is one of the biggest gaming platforms. On this platform, there are a lot of games offered by game developers and provide game pages that are currently popular. However, the website does not provide the popularity level of the currently popular games. This causes ambiguity in determining which games have high, medium, or low popularity. This study tries to create a machine learning model to cluster these games into groups using Agglomerative Hierarchical Clusterin. The distance measure used is euclidean, cosine and manhattan/cityblock and uses single, average, complete and ward linkage. Based on the evaluation results, the best cluster results are the silhouette value of 0.639 and the calinski-harabasz value of 90.192. © 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.

INVESTIGATING HETEROGENEITY OF TREATMENT EFFECT FOR CONVALESCENT PLASMA IN SEVERE COVID-19: SECONDARY ANALYSIS OF A RANDOMIZED CONTROLLED TRIAL

SESSION TITLE: Acute COVID-19 and Beyond: from Hospital to Homebound SESSION TYPE: Original Investigations PRESENTED ON: 10/18/2022 02:45 pm - 03:45 pm PURPOSE: Minimally-biased clustering (MBC) has identified hypoinflammatory (hypo-I) and hyperinflammatory (hyper-I) subphenotypes in ARDS. The hyper-I type exhibits higher inflammatory markers, clinical severity, and mortality. Similar subphenotypes were recently identified in COVID-19-related ARDS. Lower PCR cycle threshold was associated with higher mortality in the hypo-I type, implying an association between viral load (VL) and clinical outcomes in patients with dampened inflammatory responses. In a recent randomized clinical trial (RCT), convalescent plasma (CP) improved survival in severe COVID-19. We hypothesized that the anti-viral effect of CP would more significantly benefit patients without acute hyperinflammation, for whom VL may be associated with mortality. METHODS: From 4/2020-11/2020, 223 adults >18 years of age in New York and Rio de Janeiro with laboratory-confirmed severe COVID-19 were enrolled in a double-blind RCT evaluating the efficacy of CP. 150 patients received CP;73 received control plasma. Hierarchical clustering (HC) of clinical and laboratory data was used to identify sub-groups in the study population. Primary and secondary outcomes were clinical status at 28 days by modified WHO ordinal score (higher scores indicating worse status) and 28-day mortality. Welch’s t-tests, chi-squared tests, and Fisher’s exact tests were used to compare clinical and laboratory data across clusters. Proportional odds and logistic regression were used to assess the association between cluster-derived subgroups and outcome and the interaction between subgroups and randomized treatment assignment. RESULTS: HC identified two clusters (C1;N=156 and C2;N=67) in the population. Patients in C2 had significantly higher markers of inflammation (sedimentation rate, C-reactive protein, interleukin-6), coagulation (D-dimer), and cardiac injury (cardiac troponin) as well as relative lymphopenia, hypoalbuminemia, and lower bicarbonate. At 28 days, patients in C2 had significantly worse clinical status (OR of 1-pt ordinal score increase 3.10, 95% CI 1.72-5.60, p=0.0002) and higher mortality (28.4% vs. 11.5%, OR 3.03, 95% CI 1.47-6.26, p=0.003). There was no significant between-cluster heterogeneity of CP treatment effect on either ordinal score (OR 0.56, 95% CI 0.16-1.95, p=0.36) or mortality (OR 0.52, 95% CI 0.12-2.30, p=0.38). CONCLUSIONS: C2 exhibited elevated inflammatory markers and lymphopenia indicative of an acute hyperinflammatory response. C2 exhibited poorer clinical status and higher mortality at 28 days. There was no evidence of significant heterogeneity of CP treatment effect on 28-day clinical outcomes. CLINICAL IMPLICATIONS: The previously shown mortality benefit of CP in severe COVID may not differ based on inflammatory state. Using MBC methods on larger samples, e.g., patient data from a meta-analysis of CP trials, may reveal a significant impact of inflammatory state on CP effect. DISCLOSURES: No relevant relationships by Matthew Cummings Received a grant sub-award from Amazon relationship with Amazon Please note: 4/2020 -12/2020 Added 03/10/2022 by Max O'Donnell, value=Grant/Research Support No relevant relationships by Tejus Satish No relevant relationships by Allison Wolf

ATTITUDES OF HIGHER EDUCATION STUDENTS ABOUT THE USE OF DISTANCE EDUCATION ENVIRONMENTS DURING THE COVID-19 OUTBREAK PROCESS

The aim of this research is to examine the attitudes of students studying in undergraduate departments of universities towards the use of distance education environments due to the COVID-19 epidemic detected in the city of Wuhan, Hubei province of the People's Republic of China. The method of the research;mixed method. The population of the research consists of 152 undergraduate students. Data collection tools of the research;the first vehicle;"Attitude scale regarding the use of distance education environments in the pandemic process" is the second tool in the research;it consists of a semi-structured interview form prepared by the researcher. Analysis of data;the data were analyzed in two ways. Firstly, ward technique, one of the hierarchical clustering analysis methods, was used and square Euclidean distance was chosen as a distance measure. Differences in attitudes towards the use of distance education environments according to ward's cluster analysis technique;it is classified into three clusters: Effectiveness and Satisfaction, Motivation and Dissatisfaction. After the classification, a random semi-structured interview form was applied to two students in each cluster formed. As a result;it has been concluded that students have positive and negative opinions about distance education activities. (English) [ FROM AUTHOR] Bu araştırmanın amacı, Çin Halk Cumhuriyeti'nin Hubei eyaletine bağlı Wuhan şehrinde tespit edilen COVID-19 salgın nedeniyle üniversitelerin lisans bölümlerinde okuyan öğrencilerin uzaktan eğitim ortamlarının kullanımına ilişkin tutumlarını incelemektir. Araştırmanın yöntemi;karma yöntemdir. Araştırmanın evrenini 152 lisans öğrencisi oluşturmaktadır. Araştırmanın veri toplama araçları;Ílk araç;Araştırmada "Pandemi sürecinde uzaktan eğitim ortamlarının kullanımına ilişkin tutum ölçeği" ikinci araç;araştırmacı tarafından hazırlanan yarı yapılandırılmış görüşme formundan oluşmaktadır. Verilerin analizi;veriler iki şekilde analiz edilmiştir. Ílk olarak hiyerarşik kümeleme analiz yöntemlerinden ward tekniği kullanılmış ve uzaklık ölçüsü olarak kare öklid uzaklığı seçilmiştir. ward'ın kümeleme analizi tekniğine göre uzaktan eğitim ortamlarının kullanımına yönelik tutum farklılıkları;Üç kümede sınıflandırılmıştır: Etkililik ve Memnuniyet, Motivasyon ve Memnuniyetsizliktir. Sınıflandırmadan sonra oluşturulan her kümede ikişer öğrenciye rastgele yarı yapılandırılmış görüşme formu uygulanmıştır. Sonuç olarak;öğrencilerin uzaktan eğitim etkinlikleri hakkında olumlu ve olumsuz görüşlere sahip oldukları sonucuna ulaşılmıştır. (Turkish) [ FROM AUTHOR] Copyright of Firat University Journal of Social Sciences / Sosyal Bilimler Dergisi is the property of Firat University, Social Sciences Institute and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Analysis of Factors Influencing COVID-19 Mortality among Vaccinated People in Peru

We are currently facing the global pandemic caused by COVID-19, in the case of Peru, this disease has caused the death of approximately 200,000 people (September 2021), being one of the countries with the most deaths per thousand people. Due to this, progress is being made in the vaccination process, of which it has been possible to immunize more than 72% of the population with two doses. However, according to data collected by the Peruvian government, the deaths of people who would have been inoculated with at least one dose have been recorded. The present work proposes to apply machine learning models (Machine Learning), where the factors that influence the death of people are analyzed despite having been vaccinated with at least one dose, to achieve this goal, unsupervised learning techniques such as Kmeans, Spectral Clustering, Gaussian Mixture, Hierarchical Clustering, as well as data visualization techniques were applied. The results obtained reveal that the main factors that led to death are elderly people, mostly men, and that their health centers are also far from their homes, in addition to not having had access to hospitalization for adequate treatment. © 2022 WCSE. All Rights Reserved.

Time Series Analysis of SARS-CoV-2 Genomes and Correlations among Highly Prevalent Mutations.

The efforts of the scientific community to tame the recent pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seem to have been diluted by the emergence of new viral strains. Therefore, it is imperative to understand the effect of mutations on viral evolution. We performed a time series analysis on 59,541 SARS-CoV-2 genomic sequences from around the world to gain insights into the kinetics of the mutations arising in the viral genomes. These 59,541 genomes were grouped according to month (January 2020 to March 2021) based on the collection date. Meta-analysis of these data led us to identify significant mutations in viral genomes. Pearson correlation of these mutations led us to the identification of 16 comutations. Among these comutations, some of the individual mutations have been shown to contribute to viral replication and fitness, suggesting a possible role of other unexplored mutations in viral evolution. We observed that the mutations 241C>T in the 5' untranslated region (UTR), 3037C>T in nsp3, 14408C>T in the RNA-dependent RNA polymerase (RdRp), and 23403A>G in spike are correlated with each other and were grouped in a single cluster by hierarchical clustering. These mutations have replaced the wild-type nucleotides in SARS-CoV-2 sequences. Additionally, we employed a suite of computational tools to investigate the effects of T85I (1059C>T), P323L (14408C>T), and Q57H (25563G>T) mutations in nsp2, RdRp, and the ORF3a protein of SARS-CoV-2, respectively. We observed that the mutations T85I and Q57H tend to be deleterious and destabilize the respective wild-type protein, whereas P323L in RdRp tends to be neutral and has a stabilizing effect. IMPORTANCE We performed a meta-analysis on SARS-CoV-2 genomes categorized by collection month and identified several significant mutations. Pearson correlation analysis of these significant mutations identified 16 comutations having absolute correlation coefficients of >0.4 and a frequency of >30% in the genomes used in this study. The correlation results were further validated by another statistical tool called hierarchical clustering, where mutations were grouped in clusters on the basis of their similarity. We identified several positive and negative correlations among comutations in SARS-CoV-2 isolates from around the world which might contribute to viral pathogenesis. The negative correlations among some of the mutations in SARS-CoV-2 identified in this study warrant further investigations. Further analysis of mutations such as T85I in nsp2 and Q57H in ORF3a protein revealed that these mutations tend to destabilize the protein relative to the wild type, whereas P323L in RdRp is neutral and has a stabilizing effect. Thus, we have identified several comutations which can be further characterized to gain insights into SARS-CoV-2 evolution.