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1.
Clin Infect Dis ; 74(1): 169-170, 2022 01 07.
Article in English | MEDLINE | ID: covidwho-1806300
2.
Vet Rec ; 190(8): 303, 2022 Apr.
Article in English | MEDLINE | ID: covidwho-1797750
3.
Sci Rep ; 12(1): 5709, 2022 Apr 05.
Article in English | MEDLINE | ID: covidwho-1778628

ABSTRACT

This article presents a method for trend clustering from tweets about coronavirus disease (COVID-19) to help us objectively review the past and make decisions about future countermeasures. We aim to avoid detecting usual trends based on seasonal events while detecting essential trends caused by the influence of COVID-19. To this aim, we regard daily changes in the frequencies of each word in tweets as time series signals and define time series signals with single peaks as target trends. To successfully cluster the target trends, we propose graphical lasso-guided iterative principal component analysis (GLIPCA). GLIPCA enables us to remove trends with indirect correlations generated by other essential trends. Moreover, GLIPCA overcomes the difficulty in the quantitative evaluation of the accuracy of trend clustering. Thus, GLIPCA's parameters are easier to determine than those of other clustering methods. We conducted experiments using Japanese tweets about COVID-19 from March 8, 2020, to May 7, 2020. The results show that GLIPCA successfully distinguished trends before and after the declaration of a state of emergency on April 7, 2020. In addition, the results reveal the international argument about whether the Tokyo 2020 Summer Olympics should be held. The results suggest the tremendous social impact of the words and actions of Japanese celebrities. Furthermore, the results suggest that people's attention moved from worry and fear of an unknown novel pneumonia to the need for medical care and a new lifestyle as well as the scientific characteristics of COVID-19.


Subject(s)
COVID-19 , Social Media , COVID-19/epidemiology , Cluster Analysis , Humans , Principal Component Analysis , SARS-CoV-2
4.
Geospat Health ; 17(s1)2022 03 24.
Article in English | MEDLINE | ID: covidwho-1771331

ABSTRACT

spatio-temporal analysis of the first wave of the coronavirus (COVID-19) pandemic in Mexico (April to September 2020) was performed by state. Descriptive analyses through diagrams, mapping, animations and time series representations were carried out. Greater risks were observed at certain times in specific regions. Various trends and clusters were observed and analysed by fitting linear mixed models and time series clustering. The association of co-morbidities and other variables were studied by fitting a spatial panel data linear model (SPLM). On average, the greatest risks were observed in Baja California Norte, Chiapas and Sonora, while some other densely populated states, e.g., Mexico City, had lower values. The trends varied by state and a four-order polynomial, including fixed and random effects, was necessary to model them. The most common risk development was observed in states belonging to two clusters and consisted of an initial increase followed by a decrease. Some states presented cluster configurations with a retarded risk increase before the decrease, while the risk increased throughout the time of study in others. A cyclic behaviour with a second increasing trend was also observed in some states. The SPLM approach revealed a positive significant association with respect to case fatality risk between certain groups, such as males and individuals aged 50 years and more, and the prevalence of chronic kidney disease, cardiovascular disease, asthma and hypertension. The analysis may provide valuable insight into COVID-19 dynamics applicable in future outbreaks, as well as identify determinants signifying certain trends at the state level. The combination of spatial and temporal information may provide a better understanding of the fatalities due to COVID-19.


Subject(s)
COVID-19 , Aged , Cluster Analysis , Humans , Male , Mexico/epidemiology , Middle Aged , Pandemics , Spatio-Temporal Analysis
5.
Front Public Health ; 10: 755201, 2022.
Article in English | MEDLINE | ID: covidwho-1771115

ABSTRACT

At present, major public health emergencies frequently occur worldwide, and it is of great significance to analyze the research status and latest developments in this field to improve the ability of public health emergency management in various countries. This paper took 5,143 related studies from 2007 to 2020 from the Web of Science as research object and used CiteSpace, VOSviewer, and other software to perform co-word analysis, social network analysis, and cluster analysis. The results and conclusions were as follows: (1) the related research identified three periods: the exploration, growth, and outbreak period; (2) chronologically: the relevant research evolved from medical and health care for major diseases to emergency management and risk assessment of public health emergencies and then researched the novel coronavirus (COVID-19) pneumonia epidemic; (3) clustering analysis of high-frequency keywords, identifying three research hotspots: "disaster prevention and emergency medical services," "outbreak and management of infectious diseases in Africa," and "emergency management under the COVID-19 pneumonia epidemic." Finally, this study combined the data and literature analysis to point out possible future research directions: from the research of the COVID-19 pneumonia epidemic to the research of general major public health emergencies, thinking and remodeling of the national public health emergency management system, and exploring the establishment of an efficient international emergency management cooperation mechanism.


Subject(s)
COVID-19 , Public Health , COVID-19/epidemiology , Cluster Analysis , Disease Outbreaks , Humans , Public Health Administration
6.
JMIR Public Health Surveill ; 8(3): e30032, 2022 Mar 30.
Article in English | MEDLINE | ID: covidwho-1770888

ABSTRACT

BACKGROUND: The COVID-19 pandemic has led to an unprecedented global health care challenge for both medical institutions and researchers. Recognizing different COVID-19 subphenotypes-the division of populations of patients into more meaningful subgroups driven by clinical features-and their severity characterization may assist clinicians during the clinical course, the vaccination process, research efforts, the surveillance system, and the allocation of limited resources. OBJECTIVE: We aimed to discover age-sex unbiased COVID-19 patient subphenotypes based on easily available phenotypical data before admission, such as pre-existing comorbidities, lifestyle habits, and demographic features, to study the potential early severity stratification capabilities of the discovered subgroups through characterizing their severity patterns, including prognostic, intensive care unit (ICU), and morbimortality outcomes. METHODS: We used the Mexican Government COVID-19 open data, including 778,692 SARS-CoV-2 population-based patient-level data as of September 2020. We applied a meta-clustering technique that consists of a 2-stage clustering approach combining dimensionality reduction (ie, principal components analysis and multiple correspondence analysis) and hierarchical clustering using the Ward minimum variance method with Euclidean squared distance. RESULTS: In the independent age-sex clustering analyses, 56 clusters supported 11 clinically distinguishable meta-clusters (MCs). MCs 1-3 showed high recovery rates (90.27%-95.22%), including healthy patients of all ages, children with comorbidities and priority in receiving medical resources (ie, higher rates of hospitalization, intubation, and ICU admission) compared with other adult subgroups that have similar conditions, and young obese smokers. MCs 4-5 showed moderate recovery rates (81.30%-82.81%), including patients with hypertension or diabetes of all ages and obese patients with pneumonia, hypertension, and diabetes. MCs 6-11 showed low recovery rates (53.96%-66.94%), including immunosuppressed patients with high comorbidity rates, patients with chronic kidney disease with a poor survival length and probability of recovery, older smokers with chronic obstructive pulmonary disease, older adults with severe diabetes and hypertension, and the oldest obese smokers with chronic obstructive pulmonary disease and mild cardiovascular disease. Group outcomes conformed to the recent literature on dedicated age-sex groups. Mexican states and several types of clinical institutions showed relevant heterogeneity regarding severity, potentially linked to socioeconomic or health inequalities. CONCLUSIONS: The proposed 2-stage cluster analysis methodology produced a discriminative characterization of the sample and explainability over age and sex. These results can potentially help in understanding the clinical patient and their stratification for automated early triage before further tests and laboratory results are available and even in locations where additional tests are not available or to help decide resource allocation among vulnerable subgroups such as to prioritize vaccination or treatments.


Subject(s)
COVID-19 , Aged , COVID-19/epidemiology , Child , Cluster Analysis , Humans , Intensive Care Units , Pandemics , SARS-CoV-2
7.
BMC Public Health ; 22(1): 632, 2022 Apr 01.
Article in English | MEDLINE | ID: covidwho-1770517

ABSTRACT

BACKGROUND: Two years after the beginning of the COVID-19 pandemic on December 29, 2021, there have been 281,808,270 confirmed cases of COVID-19, including 5,411,759 deaths. This information belongs to almost 216 Countries, areas, or territories facing COVID-19. The disease trend was not homogeneous across these locations, and studying this variation is a crucial source of information for policymakers and researchers. Therefore, we address different patterns in mortality and incidence of COVID-19 across countries using a clustering approach. METHODS: The daily records of new cases and deaths of 216 countries were available on the WHO online COVID-19 dashboard. We used a three-step approach for identifying longitudinal patterns of change in quantitative COVID-19 incidence and mortality rates. At the first, we calculated 27 summary measurements for each trajectory. Then we used factor analysis as a dimension reduction method to capture the correlation between measurements. Finally, we applied a K-means algorithm on the factor scores and clustered the trajectories. RESULTS: We determined three different patterns for the trajectories of COVID-19 incidence and the three different ones for mortality rates. According to incidence rates, among 206 countries the 133 (64.56) countries belong to the second cluster, and 15 (7.28%) and 58 (28.16%) belong to the first and 3rd clusters, respectively. All clusters seem to show an increased rate in the study period, but there are several different patterns. The first one exhibited a mild increasing trend; however, the 3rd and the second clusters followed the severe and moderate increasing trend. According to mortality clusters, the frequency of sets is 37 (18.22%) for the first cluster with moderate increases, 157 (77.34%) for the second one with a mild rise, and 9 (4.34%) for the 3rd one with severe increase. CONCLUSIONS: We determined that besides all variations within the countries, the pattern of a contagious disease follows three different trajectories. This variation looks to be a function of the government's health policies more than geographical distribution. Comparing this trajectory to others declares that death is highly related to the nature of epidemy.


Subject(s)
COVID-19 , COVID-19/epidemiology , Cluster Analysis , Factor Analysis, Statistical , Humans , Incidence , Pandemics
8.
J Healthc Eng ; 2022: 9009406, 2022.
Article in English | MEDLINE | ID: covidwho-1770048

ABSTRACT

This article addresses automated segmentation and classification of COVID-19 and normal chest CT scan images. Segmentation is the preprocessing step for classification, and 12 DWT-PCA-based texture features extracted from the segmented image are utilized as input for the random forest machine-learning algorithm to classify COVID-19/non-COVID-19 disease. Diagnosing COVID-19 disease through an RT-PCR test is a time-consuming process. Sometimes, the RT-PCR test result is not accurate; that is, it has a false negative, which can cause a threat to the person's life due to delay in starting the specified treatment. At this moment, there is an urgent need to develop a reliable automatic COVID-19 detection tool that can detect COVID-19 disease from chest CT scan images within a shorter period and can help doctors to start COVID-19 treatment at the earliest. In this article, a variant of the whale optimization algorithm named improved whale optimization algorithm (IWOA) is introduced. The efficiency of the IWOA is tested for unimodal (F1-F7), multimodal (F8-F13), and fixed-dimension multimodal (F14-F23) benchmark functions and is compared with the whale optimization algorithm (WOA), salp swarm optimization (SSA), and sine cosine algorithm (SCA). The experiment is carried out in 30 trials and population size, and iterations are set as 30 and 100 under each trial. IWOA achieves faster convergence than WOA, SSA, and SCA and enhances the exploitation and exploration phases of WOA, avoiding local entrapment. IWOA, WOA, SSA, and SCA utilized Otsu's maximum between-class variance criteria as fitness function to compute optimal threshold values for multilevel medical CT scan image segmentation. Evaluation measures such as accuracy, specificity, precision, recall, Gmean, F_measure, SSIM, and 12 DWT-PCA-based texture features are computed. The experiment showed that the IWOA is efficient and achieved better segmentation evaluation measures and better segmentation mask in comparison with other methods. DWT-PCA-based texture features extracted from each of the 160 IWOA-, WOA-, SSA-, and SCA-based segmented images are fed into random forest for training, and random forest is tested with DWT-PCA-based texture features extracted from each of the 40 IWOA-, WOA-, SSA-, and SCA-based segmented images. Random forest has reported a promising classification accuracy of 97.49% for the DWT-PCA-based texture features, which are extracted from IWOA-based segmented images.


Subject(s)
COVID-19 , Algorithms , COVID-19/diagnostic imaging , COVID-19/drug therapy , Cluster Analysis , Humans , Tomography, X-Ray Computed/methods
9.
Front Public Health ; 9: 653337, 2021.
Article in English | MEDLINE | ID: covidwho-1760282

ABSTRACT

Background: While multiple studies have documented the impacts of mobile phone use on TB health outcomes for varied settings, it is not immediately clear what the spatial patterns of TB treatment completion rates among African countries are. This paper used Exploratory Spatial Data Analysis (ESDA) techniques to explore the clustering spatial patterns of TB treatment completion rates in 53 African countries and also their relationships with mobile phone use. Using an ESDA approach to identify countries with low TB treatment completion rates and reduced mobile phone use is the first step toward addressing issues related to poor TB outcomes. Methods: TB notifications and treatment data from 2000 through 2015 that were obtained from the World Bank database were used to illustrate a descriptive epidemiology of TB treatment completion rates among African health systems. Spatial clustering patterns of TB treatment completion rates were assessed using differential local Moran's I techniques, and local spatial analytics was performed using local Moran's I tests. Relationships between TB treatment completion rates and mobile phone use were evaluated using ESDA approach. Result: Spatial autocorrelation patterns generated were consistent with Low-Low and High-Low cluster patterns, and they were significant at different p-values. Algeria and Senegal had significant clusters across the study periods, while Democratic Republic of Congo, Niger, South Africa, and Cameroon had significant clusters in at least two time-periods. ESDA identified statistically significant associations between TB treatment completion rates and mobile phone use. Countries with higher rates of mobile phone use showed higher TB treatment completion rates overall, indicating enhanced program uptake (p < 0.05). Conclusion: Study findings provide systematic evidence to inform policy regarding investments in the use of mHealth to optimize TB health outcomes. African governments should identify turnaround strategies to strengthen mHealth technologies and improve outcomes.


Subject(s)
Cell Phone Use , Tuberculosis , Cluster Analysis , Humans , Outcome Assessment, Health Care , South Africa/epidemiology , Tuberculosis/epidemiology
10.
Genome Biol ; 22(1): 324, 2021 11 29.
Article in English | MEDLINE | ID: covidwho-1745431

ABSTRACT

High-throughput single-cell technologies hold the promise of discovering novel cellular relationships with disease. However, analytical workflows constructed for these technologies to associate cell proportions with disease often employ unsupervised clustering techniques that overlook the valuable hierarchical structures that have been used to define cell types. We present treekoR, a framework that empirically recapitulates these structures, facilitating multiple quantifications and comparisons of cell type proportions. Our results from twelve case studies reinforce the importance of quantifying proportions relative to parent populations in the analyses of cytometry data - as failing to do so can lead to missing important biological insights.


Subject(s)
Flow Cytometry/methods , Phenotype , CD8 Antigens , CD8-Positive T-Lymphocytes , COVID-19 , Cluster Analysis , Gene Expression Profiling , High-Throughput Nucleotide Sequencing , Humans , Single-Cell Analysis/methods
11.
Sci Rep ; 12(1): 4380, 2022 03 14.
Article in English | MEDLINE | ID: covidwho-1740484

ABSTRACT

To analyze the spatio-temporal aggregation of COVID-19 in mainland China within 20 days after the closure of Wuhan city, and provide a theoretical basis for formulating scientific prevention measures in similar major public health events in the future. Draw a distribution map of the cumulative number of COVID-19 by inverse distance weighted interpolation; analyze the spatio-temporal characteristics of the daily number of COVID-19 in mainland China by spatio-temporal autocorrelation analysis; use the spatio-temporal scanning statistics to detect the spatio-temporal clustering area of the daily number of new diagnosed cases. The cumulative number of diagnosed cases obeyed the characteristics of geographical proximity and network proximity to Hubei. Hubei and its neighboring provinces were most affected, and the impact in the eastern China was more dramatic than the impact in the western; the global spatio-temporal Moran's I index showed an overall downward trend. Since the 10th day of the closure of Wuhan, the epidemic in China had been under effective control, and more provinces had shifted into low-incidence areas. The number of new diagnosed cases had gradually decreased, showing a random distribution in time and space (P< 0.1), and no clusters were formed. Conclusion: the spread of COVID-19 had obvious spatial-temporal aggregation. China's experience shows that isolation city strategy can greatly contain the spread of the COVID-19 epidemic.


Subject(s)
COVID-19 , COVID-19/epidemiology , China/epidemiology , Cluster Analysis , Humans , Incidence , Spatio-Temporal Analysis
12.
Epidemiol Infect ; 150: e19, 2022 01 07.
Article in English | MEDLINE | ID: covidwho-1735173

ABSTRACT

This study investigated the characteristics of transmission routes of COVID-19 cluster infections (⩾10 linked cases within a short period) in Gangwon Province between 22 February 2020 and 31 May 2021. Transmission routes were divided into five major categories and 35 sub-categories according to the relationship between the infector and the infectee and the location of transmission. A total of 61 clusters occurred during the study period, including 1741 confirmed cases (55.7% of all confirmed cases (n = 3125)). The the five major routes of transmission were as follows: 'using (staying in) the same facility (50.7%), 'cohabiting family members' (23.3%), 'social gatherings with acquaintances' (10.8%), 'other transmission routes' (7.0%), and 'social gatherings with non-cohabiting family members/relatives' (5.5%). For transmission caused by using (staying in) the same facility, the highest number of confirmed cases was associated with churches, followed by medical institutions (inpatient), sports facilities, military bases, offices, nightlife businesses, schools, restaurants, day-care centres and kindergarten, and service businesses. Our analysis highlights specific locations with frequent transmission of infections, and transmission routes that should be targeted in situations where adherence to disease control rules is difficult.


Subject(s)
COVID-19/transmission , Adolescent , Adult , Aged , Aged, 80 and over , COVID-19/epidemiology , COVID-19/mortality , Child , Child, Preschool , Cluster Analysis , Family , Female , Humans , Infant , Male , Middle Aged , Occupations/statistics & numerical data , Republic of Korea/epidemiology , Risk Factors , Young Adult
13.
PLoS One ; 17(3): e0264640, 2022.
Article in English | MEDLINE | ID: covidwho-1731599

ABSTRACT

The SARS-CoV-2 is the third coronavirus in addition to SARS-CoV and MERS-CoV that causes severe respiratory syndrome in humans. All of them likely crossed the interspecific barrier between animals and humans and are of zoonotic origin, respectively. The origin and evolution of viruses and their phylogenetic relationships are of great importance for study of their pathogenicity and development of antiviral drugs and vaccines. The main objective of the presented study was to compare two methods for identifying relationships between coronavirus genomes: phylogenetic one based on the whole genome alignment followed by molecular phylogenetic tree inference and alignment-free clustering of triplet frequencies, respectively, using 69 coronavirus genomes selected from two public databases. Both approaches resulted in well-resolved robust classifications. In general, the clusters identified by the first approach were in good agreement with the classes identified by the second using K-means and the elastic map method, but not always, which still needs to be explained. Both approaches demonstrated also a significant divergence of genomes on a taxonomic level, but there was less correspondence between genomes regarding the types of diseases they caused, which may be due to the individual characteristics of the host. This research showed that alignment-free methods are efficient in combination with alignment-based methods. They have a significant advantage in computational complexity and provide valuable additional alternative information on the genomes relationships.


Subject(s)
Comparative Genomic Hybridization/methods , Coronavirus/genetics , Genome, Viral , Chromosome Mapping , Cluster Analysis , Coronavirus/classification , Humans , Phylogeny , SARS-CoV-2/classification , SARS-CoV-2/genetics , Sequence Alignment
14.
PLoS One ; 17(3): e0264331, 2022.
Article in English | MEDLINE | ID: covidwho-1731597

ABSTRACT

BACKGROUND: Long Covid is a public health concern that needs defining, quantifying, and describing. We aimed to explore the initial and ongoing symptoms of Long Covid following SARS-CoV-2 infection and describe its impact on daily life. METHODS: We collected self-reported data through an online survey using convenience non-probability sampling. The survey enrolled adults who reported lab-confirmed (PCR or antibody) or suspected COVID-19 who were not hospitalised in the first two weeks of illness. This analysis was restricted to those with self-reported Long Covid. Univariate comparisons between those with and without confirmed COVID-19 infection were carried out and agglomerative hierarchical clustering was used to identify specific symptom clusters, and their demographic and functional correlates. RESULTS: We analysed data from 2550 participants with a median duration of illness of 7.6 months (interquartile range (IQR) 7.1-7.9). 26.5% reported lab-confirmation of infection. The mean age was 46.5 years (standard deviation 11 years) with 82.8% females and 79.9% of participants based in the UK. 89.5% described their health as good, very good or excellent before COVID-19. The most common initial symptoms that persisted were exhaustion, chest pressure/tightness, shortness of breath and headache. Cognitive dysfunction and palpitations became more prevalent later in the illness. Most participants described fluctuating (57.7%) or relapsing symptoms (17.6%). Physical activity, stress, and sleep disturbance commonly triggered symptoms. A third (32%) reported they were unable to live alone without any assistance at six weeks from start of illness. 16.9% reported being unable to work solely due to COVID-19 illness. 37.0% reported loss of income due to illness, and 64.4% said they were unable to perform usual activities/duties. Acute systems clustered broadly into two groups: a majority cluster (n = 2235, 88%) with cardiopulmonary predominant symptoms, and a minority cluster (n = 305, 12%) with multisystem symptoms. Similarly, ongoing symptoms broadly clustered in two groups; a majority cluster (n = 2243, 88.8%) exhibiting mainly cardiopulmonary, cognitive symptoms and exhaustion, and a minority cluster (n = 283, 11.2%) exhibiting more multisystem symptoms. Belonging to the more severe multisystem cluster was associated with more severe functional impact, lower income, younger age, being female, worse baseline health, and inadequate rest in the first two weeks of the illness, with no major differences in the cluster patterns when restricting analysis to the lab-confirmed subgroup. CONCLUSION: This is an exploratory survey of Long Covid characteristics. Whilst this is a non-representative population sample, it highlights the heterogeneity of persistent symptoms, and the significant functional impact of prolonged illness following confirmed or suspected SARS-CoV-2 infection. To study prevalence, predictors and prognosis, research is needed in a representative population sample using standardised case definitions.


Subject(s)
COVID-19/psychology , Cognitive Dysfunction/etiology , Dyspnea/etiology , Sleep Wake Disorders/etiology , Adolescent , Adult , COVID-19/complications , COVID-19/pathology , COVID-19/virology , Cluster Analysis , Cross-Sectional Studies , Delivery of Health Care , Female , Humans , Male , Middle Aged , SARS-CoV-2/isolation & purification , Self Report , Stress, Physiological , Surveys and Questionnaires , Young Adult
15.
Cell Host Microbe ; 27(6): 879-882.e2, 2020 06 10.
Article in English | MEDLINE | ID: covidwho-1719463

ABSTRACT

The inflammatory response to SARS-coronavirus-2 (SARS-CoV-2) infection is thought to underpin COVID-19 pathogenesis. We conducted daily transcriptomic profiling of three COVID-19 cases and found that the early immune response in COVID-19 patients is highly dynamic. Patient throat swabs were tested daily for SARS-CoV-2, with the virus persisting for 3 to 4 weeks in all three patients. Cytokine analyses of whole blood revealed increased cytokine expression in the single most severe case. However, most inflammatory gene expression peaked after respiratory function nadir, except expression in the IL1 pathway. Parallel analyses of CD4 and CD8 expression suggested that the pro-inflammatory response may be intertwined with T cell activation that could exacerbate disease or prolong the infection. Collectively, these findings hint at the possibility that IL1 and related pro-inflammatory pathways may be prognostic and serve as therapeutic targets for COVID-19. This work may also guide future studies to illuminate COVID-19 pathogenesis and develop host-directed therapies.


Subject(s)
Coronavirus Infections/genetics , Coronavirus Infections/immunology , Pneumonia, Viral/genetics , Pneumonia, Viral/immunology , Adult , Aged , Biological Variation, Individual , COVID-19 , Cluster Analysis , Coronavirus Infections/blood , Coronavirus Infections/pathology , Cytokines/blood , Gene Expression Regulation , Humans , Male , Pandemics , Pneumonia, Viral/blood , Pneumonia, Viral/pathology , Transcriptome , Up-Regulation
16.
Int J Environ Res Public Health ; 19(5)2022 Mar 01.
Article in English | MEDLINE | ID: covidwho-1715369

ABSTRACT

With the COVID-19 pandemic, the physical activity (PA) levels of university students declined as a result of confinement. The aim of the study was to analyse the segmentation of university students according to physical self-concept ratings and to determine the differences between each cluster during the pandemic. The sample consisted of 492 students aged 18-31 years, 36.8% male and 63.2% female, who were administered the PSDQ-S and IPAQ instruments. The data collected were analysed with SPSS software, from which descriptive statistics, a cluster analysis from the PSDQ-S, was obtained. The IPAQ and socio-demographic variables were used to characterise the groups. Finally, a non-hierarchical K-means analysis was performed to establish the clusters. The results reported three different group profiles of students. Significant differences were found in all self-concept variables analysed, with the exception of some items. In relation to PA level, it could be established that the Positive Physical Self-Concept group had the highest PA level and was composed of 52.1% females and 34.4% males, showing a high physical self-concept, whereas, in the Medium-Physical Self-Concept and Negative-Physical Self-Concept groups, females were predominant in number. They were also the least physically active groups and had a low physical self-concept.


Subject(s)
COVID-19 , Pandemics , Adolescent , Adult , COVID-19/epidemiology , Cluster Analysis , Exercise , Female , Humans , Male , SARS-CoV-2 , Students , Surveys and Questionnaires , Universities , Young Adult
17.
Front Public Health ; 10: 795734, 2022.
Article in English | MEDLINE | ID: covidwho-1707885

ABSTRACT

Background: Descriptions of single clinical symptoms of coronavirus disease 2019 (COVID-19) have been widely reported. However, evidence of symptoms associations was still limited. We sought to explore the potential symptom clustering patterns and high-frequency symptom combinations of COVID-19 to enhance the understanding of people of this disease. Methods: In this retrospective cohort study, a total of 1,067 COVID-19 cases were enrolled. Symptom clustering patterns were first explored by a text clustering method. Then, a multinomial logistic regression was applied to reveal the population characteristics of different symptom groups. In addition, time intervals between symptoms onset and the first visit were analyzed to consider the effect of time interval extension on the progression of symptoms. Results: Based on text clustering, the symptoms were summarized into four groups. Group 1: no-obvious symptoms; Group 2: mainly fever and/or dry cough; Group 3: mainly upper respiratory tract infection symptoms; Group 4: mainly cardiopulmonary, systemic, and/or gastrointestinal symptoms. Apart from Group 1 with no obvious symptoms, the most frequent symptom combinations were fever only (64 cases, 47.8%), followed by dry cough only (42 cases, 31.3%) in Group 2; expectoration only (21 cases, 19.8%), followed by expectoration complicated with fever (10 cases, 9.4%) in Group 3; fatigue complicated with fever (12 cases, 4.2%), followed by headache complicated with fever was also high (11 cases, 3.8%) in Group 4. People aged 45-64 years were more likely to have symptoms of Group 4 than those aged 65 years or older (odds ratio [OR] = 2.66, 95% CI: 1.21-5.85) and at the same time had longer time intervals. Conclusions: Symptoms of COVID-19 could be divided into four clustering groups with different symptom combinations. The Group 4 symptoms (i.e., mainly cardiopulmonary, systemic, and/or gastrointestinal symptoms) happened more frequently in COVID-19 than in influenza. This distinction could help deepen the understanding of this disease. The middle-aged people have a longer time interval for medical visit and was a group that deserve more attention, from the perspective of medical delays.


Subject(s)
COVID-19 , Aged , Ambulatory Care , Cluster Analysis , Humans , Middle Aged , Retrospective Studies , SARS-CoV-2
18.
PLoS One ; 17(2): e0263367, 2022.
Article in English | MEDLINE | ID: covidwho-1686100

ABSTRACT

This work presents a tool for forecasting the spread of the new coronavirus in Mexico City, which is based on a mathematical model with a metapopulation structure that uses Bayesian statistics and is inspired by a data-driven approach. The daily mobility of people in Mexico City is mathematically represented by an origin-destination matrix using the open mobility data from Google and the Transportation Mexican Survey. This matrix is incorporated in a compartmental model. We calibrate the model against borough-level incidence data collected between 27 February 2020 and 27 October 2020, while using Bayesian inference to estimate critical epidemiological characteristics associated with the coronavirus spread. Given that working with metapopulation models leads to rather high computational time consumption, and parameter estimation of these models may lead to high memory RAM consumption, we do a clustering analysis that is based on mobility trends to work on these clusters of borough separately instead of taken all of the boroughs together at once. This clustering analysis can be implemented in smaller or larger scales in different parts of the world. In addition, this clustering analysis is divided into the phases that the government of Mexico City has set up to restrict individual movement in the city. We also calculate the reproductive number in Mexico City using the next generation operator method and the inferred model parameters obtaining that this threshold is in the interval (1.2713, 1.3054). Our analysis of mobility trends can be helpful when making public health decisions.


Subject(s)
COVID-19/epidemiology , Transportation , Basic Reproduction Number , Cluster Analysis , Geography , Humans , Mexico/epidemiology , Models, Biological , Probability , Reproducibility of Results
19.
Sci Rep ; 12(1): 1932, 2022 02 04.
Article in English | MEDLINE | ID: covidwho-1671619

ABSTRACT

College students commonly experience psychological distress when faced with intensified academic demands and changes in the social environment. Examining the nature and dynamics of students' affective and behavioral experiences can help us better characterize the correlates of psychological distress. Here, we leveraged wearables and smartphones to study 49 first-year college students continuously throughout the academic year. Affect and sleep, academic, and social behavior showed substantial changes from school semesters to school breaks and from weekdays to weekends. Three student clusters were identified with behavioral and affective dissociations and varying levels of distress throughout the year. While academics were a common stressor for all, the cluster with highest distress stood out by frequent report of social stress. Moreover, the frequency of reporting social, but not academic, stress predicted subsequent clinical symptoms. Two years later, during the COVID-19 pandemic, the first-year cluster with highest distress again stood out by frequent social stress and elevated clinical symptoms. Focus on sustained interpersonal stress, relative to academic stress, might be especially helpful to identify students at heightened risk for psychopathology.


Subject(s)
COVID-19/psychology , Sleep , Social Behavior , Stress, Psychological , Students/psychology , Academic Performance , Actigraphy , Adolescent , Affect , Cluster Analysis , Female , Humans , Male , Young Adult
20.
Clin Infect Dis ; 74(1): 169-170, 2022 01 07.
Article in English | MEDLINE | ID: covidwho-1650558
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