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1.
Molecules ; 27(12)2022 Jun 07.
Article in English | MEDLINE | ID: covidwho-1884288

ABSTRACT

In 2018, the discovery of carcinogenic nitrosamine process related impurities (PRIs) in a group of widely used drugs led to the recall and complete withdrawal of several medications that were consumed for a long time, unaware of the presence of these genotoxic PRIs. Since then, PRIs that arise during the manufacturing process of the active pharmaceutical ingredients (APIs), together with their degradation impurities, have gained the attention of analytical chemistry researchers. In 2020, favipiravir (FVR) was found to have an effective antiviral activity against the SARS-COVID-19 virus. Therefore, it was included in the COVID-19 treatment protocols and was consequently globally manufactured at large-scales during the pandemic. There is information indigence about FVR impurity profiling, and until now, no method has been reported for the simultaneous determination of FVR together with its PRIs. In this study, five advanced multi-level design models were developed and validated for the simultaneous determination of FVR and two PRIs, namely; (6-chloro-3-hydroxypyrazine-2-carboxamide) and (3,6-dichloro-pyrazine-2-carbonitrile). The five developed models were classical least square (CLS), principal component regression (PCR), partial least squares (PLS), genetic algorithm-partial least squares (GA-PLS), and artificial neural networks (ANN). Five concentration levels of each compound, chosen according to the linearity range of the target analytes, were used to construct a five-level, three-factor chemometric design, giving rise to twenty-five mixtures. The models resolved the strong spectral overlap in the UV-spectra of the FVR and its PRIs. The PCR and PLS models exhibited the best performances, while PLS proved the highest sensitivity relative to the other models.


Subject(s)
COVID-19 , Algorithms , Amides , Antiviral Agents/pharmacology , Antiviral Agents/therapeutic use , COVID-19/drug therapy , Calibration , Humans , Least-Squares Analysis , Pyrazines/therapeutic use
2.
PLoS One ; 17(6): e0269450, 2022.
Article in English | MEDLINE | ID: covidwho-1879323

ABSTRACT

This study suggested a new four-parameter Exponentiated Odd Lomax Exponential (EOLE) distribution by compounding an exponentiated odd function with Lomax distribution as a generator. The proposed model is unimodal and positively skewed whereas the hazard rate function is monotonically increasing and inverted bathtubs. Some important properties of the new distribution are derived such as quintile function and median; asymptotic properties and mode; moments; mean residual life, mean path time; mean deviation; order statistics; and Bonferroni & Lorenz curve. The value of the parameters is obtained from the maximum likelihood estimation, least-square estimation, and Cramér-Von-Mises methods. Here, a simulation study and two real data sets, "the number of deaths per day due to COVID-19 of the first wave in Nepal" and ''failure stresses (In Gpa) of single carbon fibers of lengths 50 mm", have been applied to validate the different theoretical findings. The finding of an order of COVID-19 deaths in 153 days in Nepal obey the proposed distribution, it has a significantly positive relationship between the predictive test positive rate and the predictive number of deaths per day. Therefore, the intended model is an alternative model for survival data and lifetime data analysis.


Subject(s)
COVID-19 , COVID-19/epidemiology , Humans , Least-Squares Analysis , Likelihood Functions , Nepal/epidemiology , Statistical Distributions
3.
Int J Environ Res Public Health ; 19(9)2022 04 27.
Article in English | MEDLINE | ID: covidwho-1865495

ABSTRACT

Policymakers are developing response strategies to reduce the impacts of COVID-19. However, developing response strategies without considering their relationships with the impacts of COVID-19 is ineffective. This study aims to model the causal relationships between COVID-19 impacts and response strategies in the construction industry, using Malaysia as a case study. To achieve this, a systematic literature review and semi-structured interviews with forty industry professionals were conducted, yielding 12 impacts and 22 response strategies. The impacts and strategies were inserted into a survey, and 107 valid responses were received. Exploratory factor analysis (EFA) was conducted to group the impacts and strategies. Then, partial least-squares structural equation modeling (PLS-SEM) was employed to identify the causal relationship between the impacts and strategies. The EFA results indicate that the underlying impacts are project- or material-related, and the underlying strategies are market stability and financial aid, supply chain and project support, and information and legislation. The PLS-SEM results indicate that supply chain and project support are required to address material-related impacts, and market stability and financial aid are required to address project-related impacts. This is the first paper that models the relationships between COVID-19 impacts and response strategies in the construction industry.


Subject(s)
COVID-19 , Construction Industry , COVID-19/epidemiology , Factor Analysis, Statistical , Humans , Latent Class Analysis , Least-Squares Analysis
4.
J Acquir Immune Defic Syndr ; 85(4): 475-482, 2020 12 01.
Article in English | MEDLINE | ID: covidwho-1861000

ABSTRACT

BACKGROUND: The spread of severe acute respiratory syndrome coronavirus 2, causative agent of the coronavirus disease 2019 (COVID-19), has necessitated widespread lockdown to mitigate the pandemic. This study examines the influence of resilience on the impact of COVID-related stress and enforced lockdown on mental health, drug use, and treatment adherence among people living with HIV (PLWH) in Argentina. SETTING: PLWH residing predominantly in Buenos Aires Metropolitan Area and urban regions of Argentina were identified from a private clinic electronic database. METHODS: Participants completed an anonymous online survey to evaluate the impact of COVID-19 on economic disruption, resilience, mental health outcomes (depression, anxiety, stress, and loneliness), adherence to HIV treatment, and substance use. We performed ordinary least squares and logistic regressions to test whether resilient coping buffered the impact of economic disruption on mental health and drug use during quarantine. RESULTS: A total of 1336 PLWH aged 18-82 were enrolled. The impact of economic disruption on mental health ΔF(1,1321) = 8.86, P = 0.003 and loneliness ΔF(1,1326) = 5.77, P = 0.016 was buffered by resilience. A 3-way interaction between resilient buffering, stress, and sex was significant ΔF(1,1325) = 4.76, P = 0.029. Participants reported less than excellent adherence to medication (33%), disruption to mental health services (11%), and disruption to substance abuse treatment (1.3%) during lockdown. DISCUSSION: The impact of COVID-stress and lockdown on emotional distress seemed mitigated by resilience coping strategies, and the buffering impact of resilience on perceived stress was greater among women. Results highlight PLWH's capacity to adhere to treatment in challenging circumstances and the importance of developing resilience skills for better coping with stress and adversity.


Subject(s)
Betacoronavirus , Coronavirus Infections/psychology , HIV Infections/psychology , Mental Health/trends , Pneumonia, Viral/psychology , Stress Disorders, Traumatic, Acute/psychology , Adaptation, Psychological , Adult , Aged , Aged, 80 and over , Argentina , COVID-19 , Coronavirus Infections/complications , Coronavirus Infections/economics , Coronavirus Infections/prevention & control , Female , HIV Infections/complications , Humans , Intimate Partner Violence/trends , Least-Squares Analysis , Logistic Models , Loneliness , Male , Mental Health Services/standards , Middle Aged , Pandemics/economics , Pandemics/prevention & control , Pneumonia, Viral/complications , Pneumonia, Viral/economics , Pneumonia, Viral/prevention & control , Resilience, Psychological , SARS-CoV-2 , Sex Factors , Social Isolation/psychology , Social Support , Stress Disorders, Traumatic, Acute/etiology , Substance-Related Disorders/etiology , Substance-Related Disorders/therapy , Surveys and Questionnaires , Treatment Adherence and Compliance , Young Adult
5.
Comput Math Methods Med ; 2022: 2588534, 2022.
Article in English | MEDLINE | ID: covidwho-1822102

ABSTRACT

Impulse indicator saturation is a popular method for outlier detection in time series modeling, which outperforms the least trimmed squares (LTS), M-estimator, and MM-estimator. However, using the IIS method for outlier detection in cross-sectional analysis has remained unexplored. In this paper, we probe the feasibility of the IIS method for cross-sectional data. Meanwhile, we are interested in forecasting performance and covariate selection in the presence of outliers. IIS method uses Autometrics techniques to estimate the covariates and outlier as the number of covariates P > n observations. Besides Autometrics, regularization techniques are a well-known method for covariate selection and forecasting in high-dimensional analysis. However, the efficiency of regularization techniques for the IIS method has remained unexplored. For this purpose, we explore the efficiency of regularization techniques for out-of-sample forecast in the presence of outliers with 6 and 4 standard deviations (SD) and orthogonal covariates. The simulation results indicate that SCAD and MCP outperform in forecasting and covariate selection with 4 SD (20% and 5% outliers) compared to Autometrics. However, LASSO and AdaLASSO select more covariates than SCAD and MCP and possess higher RMSE. Overall, regularization techniques possess the least RMSE than Autometrics, as Autometrics possesses the least average gauge at the cost of the least average potency. We use COVID-19 cross-sectional data collected from 1 July 2021 to 30 September 2021 for real data analysis. The SCAD and MCP select CRP level, gender, and other comorbidities as an important predictor of hospital stay with the least out-of-sample RMSE of 7.45 and 7.50, respectively.


Subject(s)
COVID-19 , COVID-19/epidemiology , Computer Simulation , Cross-Sectional Studies , Humans , Least-Squares Analysis , Research Design
6.
PLoS One ; 17(2): e0263888, 2022.
Article in English | MEDLINE | ID: covidwho-1690705

ABSTRACT

BACKGROUND: The COVID Stress Scales (CSS) assess health- and contamination-related distress in the face of a medical outbreak like the ongoing COVID-19 pandemic. Though the CSS is translated into 21 languages, it has not been validated in a Swedish national sample. AIM: Our general objective is to provide a translation, replication, and validation of the CSS and test its convergent- and discriminant validity in relation to anxiety, health anxiety, depression, and stress in the general Swedish population. We also present latent psychometric properties by modelling based on item response theory. METHODS: Participants consisted of 3044 Swedish adults (> 18 years) from a pre-stratified (gender, age, and education) sample from The Swedish Citizen Panel. Mental health status was assessed by validated instruments, including the CSS, PHQ-4, SHAI-14, and PSS-10. RESULTS: Results indicate that our Swedish translation of CSS has good psychometric properties and consists of 5 correlated factors. DISCUSSION: The CSS is useful either as a unidimensional or multidimensional construct using the CSS scales to measure key facets of pandemic-related stress. CONCLUSIONS: The findings support the cross-cultural validity of the CSS and its potential utility in understanding many of the emotional challenges posed by the current and future pandemics.


Subject(s)
COVID-19/psychology , Psychiatric Status Rating Scales , Stress, Psychological/psychology , Adolescent , Adult , Aged , Aged, 80 and over , Discriminant Analysis , Factor Analysis, Statistical , Female , Humans , Least-Squares Analysis , Male , Middle Aged , Regression Analysis , Socioeconomic Factors , Sweden , Young Adult
7.
Anal Chem ; 94(5): 2425-2433, 2022 02 08.
Article in English | MEDLINE | ID: covidwho-1650031

ABSTRACT

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused the worst global health crisis in living memory. The reverse transcription polymerase chain reaction (RT-qPCR) is considered the gold standard diagnostic method, but it exhibits limitations in the face of enormous demands. We evaluated a mid-infrared (MIR) data set of 237 saliva samples obtained from symptomatic patients (138 COVID-19 infections diagnosed via RT-qPCR). MIR spectra were evaluated via unsupervised random forest (URF) and classification models. Linear discriminant analysis (LDA) was applied following the genetic algorithm (GA-LDA), successive projection algorithm (SPA-LDA), partial least squares (PLS-DA), and a combination of dimension reduction and variable selection methods by particle swarm optimization (PSO-PLS-DA). Additionally, a consensus class was used. URF models can identify structures even in highly complex data. Individual models performed well, but the consensus class improved the validation performance to 85% accuracy, 93% sensitivity, 83% specificity, and a Matthew's correlation coefficient value of 0.69, with information at different spectral regions. Therefore, through this unsupervised and supervised framework methodology, it is possible to better highlight the spectral regions associated with positive samples, including lipid (∼1700 cm-1), protein (∼1400 cm-1), and nucleic acid (∼1200-950 cm-1) regions. This methodology presents an important tool for a fast, noninvasive diagnostic technique, reducing costs and allowing for risk reduction strategies.


Subject(s)
COVID-19 , Saliva , Discriminant Analysis , Humans , Least-Squares Analysis , Multivariate Analysis , SARS-CoV-2 , Spectroscopy, Fourier Transform Infrared
8.
PLoS One ; 17(1): e0262774, 2022.
Article in English | MEDLINE | ID: covidwho-1643281

ABSTRACT

Recent studies on burnout (BO) have included both individual and situational factors, referred to as job-person fit (JPF). The present study aimed to evaluate the prevalence rate of BO in the hospital staff working at a tertiary referral hospital in southwest Iran and then to highlight the importance of the person in the context of his/her work life. This cross-sectional study was conducted in 2020 on all hospital staff using a three-part questionnaire comprised of personal and work-situational factors, the Perceived Stress Scale (PSS), and the Psychological Empowerment Scale (PES). The partial least squares (PLS) path modelling and the neural network (NN) model were used to identify the significant variables within the BO dimensions. A total of 358 staff completed the questionnaire and were recruited for the study. Emotional exhaustion (EE) was seen in 137 medical staff (38.3%) and depersonalization (DP) was observed in 75 individuals (20.1%). Thinking about job change was the most important factor positively correlated with EE. Positive stress and work experience were among the most significant factors negatively associated with PA and DP, respectively. The hospital staff experienced BO in a way comparable to the national results. Work-situational and personal variables interacted with the three dimensions of BO in the hospital staff. More experienced staff also felt more accomplished and successful, resulting in the identification of a decreased level of DP and elevated PA.


Subject(s)
Burnout, Professional/epidemiology , Job Satisfaction , Personnel, Hospital/statistics & numerical data , Tertiary Care Centers/statistics & numerical data , Adult , Burnout, Professional/etiology , Cross-Sectional Studies , Educational Status , Female , Humans , Iran/epidemiology , Least-Squares Analysis , Male , Marital Status , Models, Statistical , Occupational Stress/epidemiology , Occupational Stress/etiology , Personnel Turnover/statistics & numerical data , Personnel, Hospital/psychology , Surveys and Questionnaires
9.
PLoS One ; 17(1): e0261869, 2022.
Article in English | MEDLINE | ID: covidwho-1629533

ABSTRACT

The aim of this study is to investigate the key factors influencing the acceptance of COVID-19 vaccines and develop a model based on the theory of reasoned action, belief in conspiracy theory, awareness, perceived usefulness, and perceived ease of use. The authors created and distributed a self-administered online questionnaire using Google Forms. Data were collected from 351 respondents ranging in age from 19 to 30 years, studying at the graduate and postgraduate levels at various public universities in Bangladesh. The Partial Least Squares Structural Equation Modeling (PLS-SEM) method was used to analyze the data. The results indicate that belief in conspiracy theory undermines COVID-19 vaccine acceptance, thereby negatively impacting the individual attitudes, subjective norms, and acceptance. Individual awareness, on the other hand, has a strong positive influence on the COVID-19 vaccine acceptance. Furthermore, the perceived usefulness of vaccination and the perceived ease of obtaining the vaccine positively impact attitude and the acceptance of immunization. Individuals' positive attitudes toward immunization and constructive subjective norms have a positive impact on vaccine acceptance. This study contributes to the literature by combining the theory of reasoned action with conspiracy theory, awareness, perceived usefulness, and perceived ease of use to understand vaccine acceptance behavior. Authorities should focus on campaigns that could reduce misinformation and conspiracy surrounding COVID-19 vaccination. The perceived usefulness of vaccination to prevent pandemics and continue normal education will lead to vaccination success. Furthermore, the ease with which people can obtain the vaccine and that it is free of cost will encourage students to get vaccinated to protect themselves, their families, and society.


Subject(s)
Awareness , COVID-19/prevention & control , Vaccination/psychology , Adult , Attitude , Bangladesh , COVID-19/virology , Female , Humans , Least-Squares Analysis , Male , Perception , SARS-CoV-2/isolation & purification , Students/psychology , Surveys and Questionnaires , Universities , Vaccination/statistics & numerical data , Young Adult
10.
Comput Math Methods Med ; 2021: 2689000, 2021.
Article in English | MEDLINE | ID: covidwho-1566408

ABSTRACT

We have studied one of the most common distributions, namely, Lindley distribution, which is an important continuous mixed distribution with great ability to represent different systems. We studied this distribution with three parameters because of its high flexibility in modelling life data. The parameters were estimated by five different methods, namely, maximum likelihood estimation, ordinary least squares, weighted least squares, maximum product of spacing, and Cramér-von Mises. Simulation experiments were performed with different sample sizes and different parameter values. The different methods were compared on the generated data by mean square error and mean absolute error. In addition, we compared the methods for real data, which represent COVID-19 data in Iraq/Anbar Province.


Subject(s)
COVID-19/epidemiology , Public Health Informatics/methods , Algorithms , Computer Simulation , Humans , Iraq , Least-Squares Analysis , Likelihood Functions , Models, Statistical , Public Health Informatics/standards , SARS-CoV-2 , Statistics as Topic
11.
PLoS One ; 16(11): e0260380, 2021.
Article in English | MEDLINE | ID: covidwho-1542191

ABSTRACT

OBJECTIVE: Availability of safe and effective vaccines against COVID-19 is critical for controlling the pandemic, but herd immunity can only be achieved with high vaccination coverage. The present research examined psychological factors associated with intentions to receive COVID-19 vaccination and whether reluctance towards novel pandemic vaccines are similar to vaccine hesitancy captured by a hypothetical measure used in previous research. METHOD: Study 1 was administered to undergraduate students when COVID-19 was spreading exponentially (February-April 2020). Study 2 was conducted with online panel workers toward the end of the first U.S. wave (July 2020) as a pre-registered replication and extension of Study 1. In both studies, participants (total N = 1,022) rated their willingness to receive the COVID-19 vaccination and to vaccinate a hypothetical child for a fictitious disease, and then responded to various psychological measures. RESULTS: In both studies, vaccination intentions were positively associated with past flu vaccine uptake, self-reported vaccine knowledge, vaccine confidence, and sense of collective responsibility. Complacency (not perceiving disease as high-risk), anti-vaccine conspiracy beliefs, perceived vaccine danger, and mistrust in science/scientists were negative correlates of vaccination intentions. Constraints (psychological barriers), calculation (extensive information-searching), analytical thinking, perceived disease vulnerability, self-other overlap, and conservatism were weakly associated with vaccination intentions but not consistently across both studies or vaccine types. Additionally, similar factors were associated with both real and hypothetical vaccination intentions, suggesting that conclusions from pre-COVID vaccine hesitancy research mostly generalize to the current pandemic situation. CONCLUSION: Encouraging flu vaccine uptake, enhancing confidence in a novel vaccine, and fostering a sense of collective responsibility are particularly important as they uniquely predict COVID-19 vaccination intentions. By including both actual pandemic-related hesitancy measures and hypothetical hesitancy measures from past research in the same study, this work provides key context for the generalizability of earlier non-pandemic research.


Subject(s)
COVID-19 Vaccines/immunology , COVID-19/immunology , COVID-19/psychology , Intention , Internet , Pandemics , Students/psychology , Vaccination/psychology , COVID-19/epidemiology , Female , Humans , Least-Squares Analysis , Male , Regression Analysis , Surveys and Questionnaires , United States/epidemiology , Young Adult
12.
Sensors (Basel) ; 21(22)2021 Nov 17.
Article in English | MEDLINE | ID: covidwho-1538465

ABSTRACT

The problems that the key biomass variables in Pichia pastoris fermentation process are difficult measure in real time; this paper mainly proposes a multi-model soft sensor modeling method based on the piecewise affine (PWA) modeling method, which is optimized by particle swarm optimization (PSO) with an improved compression factor (ICF). Firstly, the false nearest neighbor method was used to determine the order of the PWA model. Secondly, the ICF-PSO algorithm was proposed to cooperatively optimize the number of PWA models and the parameters of each local model. Finally, a least squares support vector machine was adopted to determine the scope of action of each local model. Simulation results show that the proposed ICF-PSO-PWA multi-model soft sensor modeling method accurately approximated the nonlinear features of Pichia pastoris fermentation, and the model prediction accuracy is improved by 4.4884% compared with the weighted least squares vector regression model optimized by PSO.


Subject(s)
Algorithms , Support Vector Machine , Fermentation , Least-Squares Analysis , Saccharomycetales
13.
BMC Public Health ; 21(1): 2032, 2021 11 06.
Article in English | MEDLINE | ID: covidwho-1506366

ABSTRACT

BACKGROUND: The research aimed to formulate and test a model concerning COVID-19 perceptions effects on job insecurity and a set of psychosocial factors comprising anxiety, depression, job burnout and job alienation in the Middle East and North African (hereafter, MENA) regional context. Also, the study attempted to examine whether locus of control can moderate these hypothesised linkages amongst customer service employees working in MENA hospitality organisations. METHODS: The study is based on a sample of 885 responses to an online survey and Partial Least Square Structural Equation Modelling (PLS-SEM). RESULTS: The main findings show the existence of a significant correlation between COVID perceptions and job insecurity and all psychosocial factors, i.e., more intense COVID-19 perceptions accompany higher levels of job insecurity, anxiety, depression, job burnout and job alienation. Furthermore, our results revealed that, in pandemic time, hospitality customer service employees with external locus of control are more likely to suffer higher alienation, anxiety and depression than those with internal locus of control. CONCLUSIONS: The research originality centres on the establishment that COVID-19 has a severe negative impact within the hospitality customer service labour force (in the MENA region). These effects were more profound for participants who claimed external locus of control than those with internal locus of control.


Subject(s)
COVID-19 , Pandemics , Employment , Humans , Internal-External Control , Job Satisfaction , Latent Class Analysis , Least-Squares Analysis , Perception , SARS-CoV-2
14.
PLoS One ; 16(11): e0259226, 2021.
Article in English | MEDLINE | ID: covidwho-1502072

ABSTRACT

When emerging technologies transform an organization's way of working, explorative business process management (BPM) becomes a new challenge. Although digital innovations can boost process efficacy and business productivity, employees do not necessarily accept the implied work changes. We therefore looked at the increased digitalization efforts during the COVID-19 lockdowns, during which employees were forced to drastically rethink work by heavily depending on technology for communication and almost all business tasks. This global setting allowed us to scrutinize disruptive work changes and how employees can cope with disruptive work adaptations. We also looked into the explorative skillset needed to adapt to these changes. To theorize about an explorative BPM acceptance model, eleven hypotheses were supported based on a solid theoretical foundation. We followed a quantitative research design using partial least squares for structural equation modeling (PLS-SEM) at the university administration settings in two regions, including purposive sampling. Data analysis covered both a measurement model assessment and structural model assessment. Our findings reveal that employees' perceived work modalities, feeling creative and feeling flexible are more promising features than perceived influence and attitude related to explorative work and skill development. We also offer novel insights into explorative business process management (BPM) skills, and which skills are more productive in uncertain or dynamic working conditions. This research is a learning path for managers struggling with flexible or competitive business environments, and more specifically to facilitate employee willingness.


Subject(s)
COVID-19/epidemiology , Commerce , Communicable Disease Control/methods , Employment , Outcome and Process Assessment, Health Care , Pandemics , Adult , Aged , Algorithms , Creativity , Female , Humans , Learning , Least-Squares Analysis , Male , Middle Aged , Models, Organizational , SARS-CoV-2 , Technology , Young Adult
15.
Comput Math Methods Med ; 2021: 7196492, 2021.
Article in English | MEDLINE | ID: covidwho-1476882

ABSTRACT

COVID-19 has swept through the world since December 2019 and caused a large number of patients and deaths. Spatial prediction on the spread of the epidemic is greatly important for disease control and management. In this study, we predicted the cumulative confirmed cases (CCCs) from Jan 17 to Mar 1, 2020, in mainland China at the city level, using machine learning algorithms, geographically weighted regression (GWR), and partial least squares regression (PLSR) based on population flow, geolocation, meteorological, and socioeconomic variables. The validation results showed that machine learning algorithms and GWR achieved good performances. These models could not effectively predict CCCs in Wuhan, the first city that reported COVID-19 cases in China, but performed well in other cities. Random Forest (RF) outperformed other methods with a CV-R 2 of 0.84. In this model, the population flow from Wuhan to other cities (WP) was the most important feature and the other features also made considerable contributions to the prediction accuracy. Compared with RF, GWR showed a slightly worse performance (CV-R 2 = 0.81) but required fewer spatial independent variables. This study explored the spatial prediction of the epidemic based on multisource spatial independent variables, providing references for the estimation of CCCs in the regions lacking accurate and timely.


Subject(s)
COVID-19/diagnosis , COVID-19/epidemiology , Computational Biology/methods , Geography , Machine Learning , Algorithms , China/epidemiology , Cities , Climate , Communicable Diseases , Environmental Monitoring , Epidemics , Humans , Least-Squares Analysis , Models, Statistical , Reproducibility of Results , SARS-CoV-2 , Social Class , Spatial Regression
16.
Sci Rep ; 11(1): 15409, 2021 10 11.
Article in English | MEDLINE | ID: covidwho-1462018

ABSTRACT

Early diagnosis of COVID-19 in suspected patients is essential for contagion control and damage reduction strategies. We investigated the applicability of attenuated total reflection (ATR) Fourier transform infrared (FTIR) spectroscopy associated with machine learning in oropharyngeal swab suspension fluid to predict COVID-19 positive samples. The study included samples of 243 patients from two Brazilian States. Samples were transported by using different viral transport mediums (liquid 1 or 2). Clinical COVID-19 diagnosis was performed by the RT-PCR. We built a classification model based on partial least squares (PLS) associated with cosine k-nearest neighbours (KNN). Our analysis led to 84% and 87% sensitivity, 66% and 64% specificity, and 76.9% and 78.4% accuracy for samples of liquids 1 and 2, respectively. Based on this proof-of-concept study, we believe this method could offer a simple, label-free, cost-effective solution for high-throughput screening of suspect patients for COVID-19 in health care centres and emergency departments.


Subject(s)
COVID-19 Testing/methods , COVID-19/diagnosis , SARS-CoV-2/isolation & purification , Spectroscopy, Fourier Transform Infrared/methods , Adult , Aged , Brazil/epidemiology , COVID-19/epidemiology , Early Diagnosis , Female , Humans , Least-Squares Analysis , Machine Learning , Male , Middle Aged , Time Factors
17.
J Community Psychol ; 50(3): 1282-1314, 2022 04.
Article in English | MEDLINE | ID: covidwho-1442002

ABSTRACT

A critical part of the national economy is small and medium enterprises (SMEs). SME owners are vital contributors to the overall economy. Due to their limited capital and assets, they are more vulnerable. In comparison to their contribution, the value of assessing SME owner's depression, anxiety, and mental stress has been very minimal during the COVID-19 outbreak. Firms were forced to close due to lockdown, and they faced substantial business losses. Thus, this study aims to investigate SME owners' psychological distress due to business losses during this pandemic. The study used psychological parameters: Depression, Anxiety, Stress Scale-21 (DASS-21) to examine SME owners' psychological distress. A total number of 217 owners were surveyed through a judgmental sampling technique using a structured questionnaire. Data were analyzed employing partial least square-based structural equation modeling (PLS-SEM). The findings showed that DASS-21 parameters and fear of business loss affected psychological distress. Besides, fear of business loss increases psychological distress, whereas government support lessens the distress. Theoretically, this study extended the scope of DASS-21 scale and contributed to the literature of psychology. In terms of policy implications, this study provides useful information for government, policymakers, and SME owners about the effects mentioned above.


Subject(s)
COVID-19 , Psychological Distress , Communicable Disease Control , Ghana , Humans , Latent Class Analysis , Least-Squares Analysis , SARS-CoV-2
19.
Int J Mol Sci ; 22(17)2021 Sep 02.
Article in English | MEDLINE | ID: covidwho-1390657

ABSTRACT

COVID-19 is a global threat that has spread since the end of 2019, causing severe clinical sequelae and deaths, in the context of a world pandemic. The infection of the highly pathogenetic and infectious SARS-CoV-2 coronavirus has been proven to exert systemic effects impacting the metabolism. Yet, the metabolic pathways involved in the pathophysiology and progression of COVID-19 are still unclear. Here, we present the results of a mass spectrometry-based targeted metabolomic analysis on a cohort of 52 hospitalized COVID-19 patients, classified according to disease severity as mild, moderate, and severe. Our analysis defines a clear signature of COVID-19 that includes increased serum levels of lactic acid in all the forms of the disease. Pathway analysis revealed dysregulation of energy production and amino acid metabolism. Globally, the variations found in the serum metabolome of COVID-19 patients may reflect a more complex systemic perturbation induced by SARS-CoV-2, possibly affecting carbon and nitrogen liver metabolism.


Subject(s)
Biomarkers/blood , Carbon/metabolism , Liver/metabolism , Metabolome , Nitrogen/metabolism , Amino Acids/metabolism , COVID-19/blood , COVID-19/pathology , COVID-19/virology , Cytokines/blood , Discriminant Analysis , Humans , Least-Squares Analysis , Metabolic Networks and Pathways/genetics , Metabolomics/methods , SARS-CoV-2/isolation & purification , Severity of Illness Index
20.
Comput Math Methods Med ; 2021: 8873059, 2021.
Article in English | MEDLINE | ID: covidwho-1362017

ABSTRACT

When encountering the outbreak and early spreading of COVID-19, the Government of Japan imposed gradually upgraded restriction policies and declared the state of emergency in April 2020 for the first time. To evaluate the efficacy of the countering strategies in different periods, we constructed a SEIADR (susceptible-exposed-infected-asymptomatic-documented-recovered) model to simulate the cases and determined corresponding spreading coefficients. The effective reproduction number R t was obtained to evaluate the measures controlling the COVID-19 conducted by the Government of Japan during different stages. It was found that the strict containing strategies during the state of emergency period drastically inhibit the COVID-19 trend. R t was decreased to 1.1123 and 0.8911 in stages 4 and 5 (a state of emergency in April and May 2020) from 3.5736, 2.0126, 3.0672 in the previous three stages when the containing strategies were weak. The state of emergency was declared again in view of the second wave of massive infections in January 2021. We estimated the cumulative infected cases and additional days to contain the COVID-19 transmission for the second state of emergency using this model. R t was 1.028 which illustrated that the strategies were less effective than the previous state of emergency. Finally, the overall infected population was predicted using combined isolation and testing intensity; the effectiveness and the expected peak time were evaluated. If using the optimized control strategies in the current stage, the spread of COVID-19 in Japan could be controlled within 30 days. The total confirmed cases should reduce to less than 4.2 × 105 by April 2021. This model study suggested stricter isolating measures may be required to shorten the period of the state of emergency.


Subject(s)
COVID-19/epidemiology , COVID-19/transmission , Emergencies , Models, Biological , Pandemics , SARS-CoV-2 , Algorithms , COVID-19/prevention & control , COVID-19 Testing/methods , COVID-19 Testing/statistics & numerical data , Communicable Disease Control/legislation & jurisprudence , Communicable Disease Control/methods , Communicable Disease Control/statistics & numerical data , Computational Biology , Computer Simulation , Humans , Japan/epidemiology , Least-Squares Analysis , Mathematical Concepts , Models, Statistical , National Health Programs/legislation & jurisprudence , Nonlinear Dynamics , Pandemics/prevention & control , Pandemics/statistics & numerical data
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