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The aim of the research. To conduct a cluster analysis of the assessment profile of students who participated in work of medical organisations providing care to COVID-19 patients to develop recommendations for its correction. Material and methods. The study was carried out at the premises of Prof. V.F. Voino-Yasenetsky Krasnoyarsk State Medical University (KrasSMU). The study group was constituted by 66 students in 3-6 years of study of the Medical and the Paediatric faculties of the University who took part in activities of medical organisations providing healthcare to patients with COVID-19. The items were presented in the form of binary questions and ranking scales. The analysis of qualitative attributes was carried out in the form of relative values with calculation of the standard error of the proportion. For ranking and nonparametric quantitative characteristics, the mode, median, centiles (Me [P25;P75]) and other nonparametric criteria for comparative statistics and communication statistics were used. For segmentation of respondents according to some criteria, depending on the answers, the method "two-step cluster analysis" and the method of "decision tree" were used. Results. The results of the study indicate a high motivational component related to practical medical activity of medical students during the difficult epidemiological situation since 94.1% of the respondents declared the readiness to support practical healthcare. Almost half of the surveyed 47.0% of students included in cluster 2, in contrast to students of clusters 1 and 3, are characterised by a high opinion on the degree of their contribution to the struggle against the COVID-19 epidemic and a high level of knowledge and skills, rating themselves at about 9.0 points out of 10 possible. In addition, the results of the study indicate an association between the level of students' self-esteem in regard to their contribution to the fight against COVID-19 with the level of the students' self-esteem of knowledge and skills and the duration of work in a medical organisation. Conclusion. The analysis performed has made it possible to formulate guidelines for support of medical students' professional attitudes within the framework of practice-oriented education, including distance learning.Copyright © 2021, Krasnoyarsk State Medical University. All rights reserved.
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Coronavirus disease (COVID-19) is a public health problem in Thailand. Currently, there are more than 5 million infected people and the rate has been increasing at some point. It is therefore important to forecast the number of new cases over a short period of time to assist in strategic planning for the response to COVID-19. The purpose of this research paper was to compare the efficiency and prediction of the number of COVID-19 cases in Thailand using machine learning of 8 models using a regression analysis method. Using the 475-day dataset of COVID-19 cases in Thailand, the results showed that the predictive accuracy model (R2 score) from the testing dataset was the random forest (RF) model, which was 99.06%, followed by K-nearest neighbor (KNN), XGBoost. And the decision tree (DT) had the precision of 98.97, 98.67, and 98.64, respectively. And the results of the comparison of the number of infected people obtained from the prediction The models that predicted the number of real infections were the decision tree, random forest, and XGBoost, which were effective at predicting the number of infections correctly in the 2-4 day period. © 2023 Institute of Advanced Engineering and Science. All rights reserved.
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The COVID-19 condition had a substantial impact on the education sector, corporate sector and even the life of individual. With this pandemic situation e-learning/distance learning has become certain in the education sector. In spite of being beneficial to students and teachers, its efficacy in the education domain depends on several factors such as handiness of ICT devices in various socio economic groups of people and accessible internet facility. To analyze the effectiveness of this new system of e learning Sentiment Analysis plays a predominant role in identifying the user's perception. This paper focus on identifying opinions of social media users i.e. Twitter on the most prevailing issue of online learning. To analyze the subjectivity and polarity of the dynamic tweets extracted from Twitter the proposed study adopts TextBlob. As Machine Learning (ML) models and techniques manifests superior accuracy and efficacy in opinion classification, the proposed solution uses, TF-IDF (Term Frequency-Inverse Document Frequency) as feature extraction technique to build and evaluate the model. This manuscript analyses the performance of Multinomial Naive Bayes Classifier, DecisionTreeClassifier, SVC and MLP Classifier with respect to performance measure as Accuracy. © 2022 IEEE.
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Objectives: The COVID19 pandemic caused over six million deaths worldwide as of 2022 and made necessary the rapid development of vaccines. The objective of this Systematic Literature Review is to summarise the main evidence from economic evaluations of vaccines against COVID19. Method(s): Searches were conducted on PubMed on July 13th 2022. The selected papers considered COVID19 vaccination scenarios without population limits. The types of study design examined were cost-benefit and cost-effectiveness analyses. Result(s): Overall, 16 articles from an initial list of 1842 were included in this review. Out of the 16 models, there were five Markov cohort models (three of them were combined with a decision tree model), four dynamic transmission models, three microsimulation models, three epidemiological models (without further information on the model structure) and one decision tree model. Model characteristics were considerably consistent between high-, middle- or low-income countries. Five studies considered both the healthcare and societal perspective, while seven studies reported only the former, and one only the latter. Two studied did not specify the study perspective. Ten of the studies did not consider any level of herd immunity, and no study considered cross-protection. Although eight studies used "naive" comparisons between vaccines, none of the studies conducted thorough indirect treatment comparison. All the models suggest that vaccines are cost-effective as they prevent death and transmission, and reduce the severity of cases. Although the sources of effectiveness estimates were always stated, the details of those studies were rarely reported. Nevertheless, the outcome measures and the key parameters used in the models were generally clearly stated and justified. Conclusion(s): This SLR highlights several challenges for conducting Health Economic evaluations of COVID19 vaccines. The quality of the models and their estimates suffered from the very fast pace of COVID19 research. Therefore, economic evidence on vaccination programs requires additional rigorous research.Copyright © 2023
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Objectives: The United Kingdom (UK) implemented an autumn 2022 booster programme that allowed those at higher risk from COVID-19, including those >= 50 years, to receive a booster to increase protection against infection and subsequent severe outcomes. As the UK transitions out of the pandemic, future booster campaigns may be required to maintain protection against such outcomes. The objective of this analysis was to estimate the value-based price (VBP) for a bivalent COVID-19 vaccine used in a future autumn 2023 campaign in the UK to protect people aged >= 50 years. Method(s): A Susceptible-Exposed-Infected-Recovered (SEIR) model was used to predict infections across a 1-year time horizon starting September 2023 with and without an autumn booster campaign. Initial effectiveness was predicted to be 89% and 97% against infection and hospitalization respectively based on BA.4/BA.5 antibody titers and correlates of protection. A monthly decline in protection of 1.4% and 4.8%, respectively, was assumed based on monovalent vaccine data. A decision tree was used to predict the quality-adjusted life-years (QALY) lost and costs associated with infections. Result(s): Considering a willingness-to-pay (WTP) threshold of 20,000/QALY, the VBP associated with an autumn 2023 booster campaign is 343/dose. Considering a WTP threshold of 30,000, the VBP increases to 476. In sensitivity analyses, excluding the post-infection costs (e.g., long COVID), reduces the VBP by 11%. Varying the hospitalization rates by +/-25% changes the VBP by +/- 6%. Varying hospitalization unit costs only impacts the VBP by 1%. Doubling the rate of waning for booster effectiveness increases the VBP by 54% because the effectiveness provided from past campaigns falls faster and an autumn 2023 booster becomes more valuable. Conclusion(s): While the trajectory of COVID-19 incidence is highly uncertain, pricing the bivalent booster lower than the VBP is expected to result in a cost-effective strategy for the UK.Copyright © 2023
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The spreading of short-term flat rentals has brought about changes in the accommodation market, often seen as a threat to traditional accommodation providers. This is particularly true in large cities which have a considerable accommodation capacity and also a large stock of flats. The aim is to indicate to what extent short-term rentals are influencing the tourist accommodation market in Warsaw. The idea behind the study is the assumption that the differences revealed between those using hotels or such flats will provide an answer to the question of the influence of the latter on Warsaw»s tourist market. Such information should be useful in the marketing activities of interested parties and in the policies of the city authorities. Analysis of the data from a survey carried out in 2021 using the CHAID decision tree indicates that the choice of accommodation type was mainly determined by situational variables. The only statistically significant demographic predictor relates to a greater interest in flats among those aged up to 34 years old. Planned expenditure per person per overnight stay proved to be a statistically significant predictor only for non-residents of Poland, with the cut-off amount set higher than the median interval for this segment. Flats were more often chosen by people travelling in a larger party or alone and those planning to stay longer than four nights, thus looking for a different offer than that of traditional city hotels. © 2023, Lodz University Press. All rights reserved.
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Air pollution is one of the most significant concerns of the present era, which has severe and alarming effects on human health and the environment, thereby escalating the climate change issue. Hence, in-depth analysis of air pollution data and accurate air quality forecasting is crucial in controlling the growing pollution levels. It also aids in designing appropriate policies to prevent exposure to toxic pollutants and taking necessary precautionary measures. Air quality in Delhi, the capital of India, is inferior compared to other major cities in the world. In this study, daily and hourly concentrations of air pollutants in the Delhi region were collected and analyzed using various methods. A comparative analysis is performed based on months, seasons, and the topography of different stations. The effect of the Covid-19 lockdown on the reduction of pollutant levels is also studied. A correlation analysis is performed on the available data to show the relationships and dependencies among different pollutants, their relationship with weather parameters, and the correlations between the stations. Various machine learning models were used for air quality forecasting, like Linear Regression, Vector Auto Regression, Gradient Boosting Machine, Random Forest, and Decision Tree Regression. The performance of these models was compared using RMSE, MAE, and MAPE metrics. This study is focused on the dire state of air pollution in Delhi, the primary reasons behind it, and the efficacy of calculated lockdowns in bringing down pollution levels. It also highlights the potential of Linear Regression and Decision Tree Regression models in predicting the air quality for different time intervals. © 2022 IEEE.
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Objectives: Mucormycosis is a rare invasive fungal infection with high lethality, affecting mainly patients with hematological neoplasia, decompensated diabetes, and covid-19 infection. The aim was to perform a cost-effectiveness analysis of liposomal Amphotericin B (standard treatment) versus isavuconazole for treating mucormycosis in the consolidation phase from the perspective of the Brazilian Unified Health System. Method(s): A decision tree model was built. The analysis considered the costs of the treatment over a six-month time horizon. This included hospitalization during the entire course of treatment and the expenditures related to dialysis, complication occurring in 5% (3%-6%) of cases treated with the Amphotericin B. Appointments with specialists were included in the isavuconazole arm, and amphotericin B was used if the patient failed to respond to isavuconazole. The utility of the patient with mucormycosis, cured and with renal failure was estimated. Uncertainties were assessed through probabilistic and deterministic sensitivity analyses. Result(s): The average cost of amphotericin B and isavuconazole arm was R$1.054.874,39 and R$522.344,05, respectively. The utility was 0.479 with amphotericin B and 0.480 with isavuconazole. The ICER was R$ -684,494,237 (dominant). In deterministic sensitivity analysis, the probability of dialysis was the variable with the greatest impact. In probabilistic analysis, the ICER is distributed in the right and left lower quadrant, the acceptability curve for all the scenarios analyzed is favorable for isavuconazole. The budget impact suggests a potential savings of between R$ 350 million and R$ 415 million over five years. Conclusion(s): The treatment of mucormycosis during the consolidation phase with isavuconazole represents a lower cost, besides the convenience of oral treatment and reduced incidence of severe adverse events, with mortality similar to the Amphotericin B arm. In Brazil, the formulation of posaconazole approved is inadequate for treating mucormycosis during the consolidation phase, therefore isavuconazole is the single oral drug available.Copyright © 2023
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BACKGROUND AND OBJECTIVE: Corona virus causes respiratory tract infections in mammals. The latest type of Severe Acute Respiratory Syndrome Corona-viruses 2 (SARS-CoV-2), Corona virus spread in humans in December 2019 in Wuhan, China. The purpose of this study was to investigate the relationship between type 2 diabetes mellitus (T2DM), and their biochemical and hematological factors with the level of infection with COVID-19 to improve the treatment and management of the disease. MATERIAL AND METHOD: This study was conducted on a population of 13,170 including 5780 subjects with SARS-COV-2 and 7390 subjects without SARS-COV-2, in the age range of 35-65 years. Also, the associations between biochemical factors, hematological factors, physical activity level (PAL), age, sex, and smoking status were investigated with the COVID-19 infection. RESULT: Data mining techniques such as logistic regression (LR) and decision tree (DT) algorithms were used to analyze the data. The results using the LR model showed that in biochemical factors (Model I) creatine phosphokinase (CPK) (OR: 1.006 CI 95% (1.006,1.007)), blood urea nitrogen (BUN) (OR: 1.039 CI 95% (1.033, 1.047)) and in hematological factors (Model II) mean platelet volume (MVP) (OR: 1.546 CI 95% (1.470, 1.628)) were significant factors associated with COVID-19 infection. Using the DT model, CPK, BUN, and MPV were the most important variables. Also, after adjustment for confounding factors, subjects with T2DM had higher risk for COVID-19 infection. CONCLUSION: There was a significant association between CPK, BUN, MPV and T2DM with COVID-19 infection and T2DM appears to be important in the development of COVID-19 infection.
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COVID-19 , Diabetes Mellitus, Type 2 , Animals , Humans , Adult , Middle Aged , Aged , SARS-CoV-2 , Algorithms , Creatine Kinase , Data Mining , MammalsABSTRACT
The Indonesian government provided various social assistance programs to local governments during Covid-19. One of the difficulties for the local governments in determining candidates for social aid is ensuring that the number of candidates is in balance with the available quota. Therefore, the local governments must select the most eligible candidates. This study proposes a priority model that can provide recommendations for candidates who meet the criteria for social assistance. The six parameters used in this study were: number of dependents, occupation, income, age, Covid status, and citizen status. The model operates in two stages, namely classification followed by ranking. The classification stage is conducted using a decision tree, while the ranking stage is performed conducted using the Analytical Hierarchy Process (AHP) algorithm. The decision tree separates two classes, namely, eligible and non-eligible. In addition, the classification process is also used to determine the dominant attributes and played a role in the modeling. The proposed model generates a list of the most eligible candidates based on our research. These are sorted by weight from greatest to most eligible using five dominant parameters: number of dependents, income, age, Covid status, and citizen status.
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In recent years, the rapid development of artificial intelligence has enabled the launch of many new screening tools. This review aims to facilitate screening tool selection through a systematic narrative review and feature analysis. The current adoption rate of transparent tool reporting is low: by screening 191 studies published in the Review of Educational Research since 2015, we found that only eight studies reported screening tools. More research is needed to understand the reasons behind this phenomenon. After consulting various sources, 26 available screening tools in the market were found. Among them, we identified and evaluated 12 screening tools for educational reviewers and ranked them in descending order of feature score: Covidence (1), DistillerSR (2, tied), EPPI-Reviewer (2, tied), CADIMA (4), Swift-Active (5), Rayyan (6, tied), SysRev (6, tied), Abstrackr (8, tied), ReLiS (8, tied), RevMan (8, tied), ASReview (11), and Excel (12). In the discussion, we provide insights into the promise and bias in tools' machine learning algorithms. Our results encourage researchers to report their tool usage in publications and select tools based on suitability instead of convenience. © 2023 Taylor & Francis Group, LLC.
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Background: Since its emergence in December 2019, until June 2022, coronavirus 2019 (COVID-19) has impacted populations all around the globe with it having been contracted by ~ 535 M people and leaving ~ 6.31 M dead. This makes identifying and predicating COVID-19 an important healthcare priority. Method and Material: The dataset used in this study was obtained from Shahid Beheshti University of Medical Sciences in Tehran, and includes the information of 29,817 COVID-19 patients who were hospitalized between October 8, 2019 and March 8, 2021. As diabetes has been shown to be a significant factor for poor outcome, we have focused on COVID-19 patients with diabetes, leaving us with 2824 records. Results: The data has been analyzed using a decision tree algorithm and several association rules were mined. Said decision tree was also used in order to predict the release status of patients. We have used accuracy (87.07%), sensitivity (88%), and specificity (80%) as assessment metrics for our model. Conclusion: Initially, this study provided information about the percentages of admitted Covid-19 patients with various underlying disease. It was observed that diabetic patients were the largest population at risk. As such, based on the rules derived from our dataset, we found that age category (51-80), CPR and ICU residency play a pivotal role in the discharge status of diabetic inpatients.
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Covid-19 has had a destructive influence on global economics, social life, education, and technologies. The rise of the Covid-19 pandemic has increased the use of digital tools and technologies for epidemic control. This research uses machine learning (ML) models to identify populated areas and predict the disease's risk and impact. The proposed system requires only details about mask utilization, temperature, and distance between individuals, which helps protect the individual's privacy. The gathered data is transferred to an ML engine in the cloud to determine the risk probability of public areas concerning Covid-19. Extracted data are input for multiple ML techniques such as Random Forest (RF), Decision tree (DT), Naive Bayes classifier(NBC), Neural network(NN), and Support vector machine (SVM). Expectation maximization (EM), K-means, Density, Filtered, and Farthest first (FF) clustering algorithms are applied for clustering. Compared to other algorithms, the K-means produces better superior accuracy. The regression technique is utilized for prediction. The outcomes of several methods are compared, and the most suitable ML algorithms utilized in this study are used to identify high-risk locations. In comparison to other identical architectures, the suggested architecture retains excellent accuracies. It is observed that the time taken to build the model using locally weighted learning(LWL) was 0.02 seconds, and the NN took more time to build, which is 0.90 seconds. To test the model, an LWL algorithm took more time which is 1.73 seconds, and the NN took less time to test, which is 0.02 seconds. The NBC has a 99.38 percent accuracy, the RF classifier has a 97.33 percent accuracy, and the DT has a 94.51 percent accuracy for the same data set. These algorithms have significant possibilities for predicting the likelihood of crowd risks of Covid-19 in a public space. This approach generates automatic notifications to concerned government authorities in any aberrant detection. This study is likely to aid researchers in modeling healthcare systems and spur additional research into innovative technology. © 2023 IEEE.
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Physical activity and exercise participation among older adults have decreased dramatically because of the physical distancing measures implemented to prevent the spread of COVID-19. However, even in the face of unforeseen environmental changes, physical activity and exercise for older adults must be sustainable. This study aimed to identify the influencing physical activity and exercise participation among older adults in 2020 when varying levels of quarantine were in place as a protective measure against the COVID-19 pandemic to build a foundation for sustainable older adult health strategies. We utilized a large-scale dataset from the 2020 National Survey of Older Koreans conducted in 2020. Twenty survey questions were used as predictors, and logistic regression and decision tree analyses were utilized to identify influencing factors. Through a logistic regression analysis, 16 factors influencing exercise participation were identified. Additionally, through a decision tree analysis, 7 factors that influence exercise participation and 8 rules were derived through a combination of these factors. According to the results of this study, the use of ICT technologies, such as ‘smartphone or tablet PC', can be a useful tool to maintain or promote physical activity and exercise by older adults in a situation like the COVID-19 pandemic. In conclusion, physical activity and exercise intervention strategies should be developed with comprehensive consideration of the influencing factors to ensure that physical activity and exercise among older adults can be sustained uninterrupted in the face of unforeseen circumstances, such as the COVID-19 pandemic.
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Featured ApplicationThe study could be used for sitting posture monitoring in a work-from-home setup. This could also be used for rehabilitation purposes of patients who has posture-related problems.Human posture recognition is one of the most challenging tasks due to the variation in human appearance, changes in the background and illumination, additional noise in the frame, and diverse characteristics and amount of data generated. Aside from these, generating a high configuration for recognition of human body parts, occlusion, nearly identical parts of the body, variations of colors due to clothing, and other various factors make this task one of the hardest in computer vision. Therefore, these studies require high-computing devices and machines that could handle the computational load of this task. This study used a small-scale convolutional neural network and a smartphone built-in camera to recognize proper and improper sitting posture in a work-from-home setup. Aside from the recognition of body points, this study also utilized points' distances and angles to help in recognition. Overall, the study was able to develop two objective datasets capturing the left and right side of the participants with the supervision and guidance of licensed physical therapists. The study shows accuracies of 85.18% and 92.07%, and kappas of 0.691 and 0.838, respectively. The system was developed, implemented, and tested in a work-from-home environment.
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Objectives: In support of schools restarting during the COVID-19 pandemic, some schools partnered with local experts in academia, education, community, and public health to provide decision-support tools for determining what actions to take when presented with students at risk for spreading infection at school. Methods: The Student Symptom Decision Tree, developed in Orange County, California, is a flow chart consisting of branching logic and definitions to assist school personnel in making decisions regarding possible COVID-19 cases in schools which was repeatedly updated to reflect evolving evidence-based guidelines. A survey of 56 school personnel evaluated the frequency of use, acceptability, feasibility, appropriateness, usability, and helpfulness of the Decision Tree. Results: The tool was used at least 6 times a week by 66% of respondents. The Decision Tree was generally perceived as acceptable (91%), feasible (70%), appropriate (89%), usable (71%) and helpful (95%). Suggestions for improvement included reducing the complexity in content and formatting of the tool. Conclusions: The data suggest that school personnel found value in the Decision Tree, which was intended to assist them with making decisions in a challenging and rapidly evolving pandemic.
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Coronavirus has greatly impacted various aspects of human life, including human psychology and human disposition. In this paper, the authors analyzed the impact of the COVID-19 pandemic on human health. In the proposed work, human disposition analysis during COVID-19 using machine learning (HuDA_COVID), where factors such as age, employment, addiction, stress level are studied. A mass survey is conducted on individuals of various age groups, regions, and professions, and the methodology achieved varied accuracy ranges from 87.5% to 98%. The study shows people are worried about lockdown, work, and relationships. Furthermore, 23% of the respondents have not had any effect. Forty-five percent and 32% have had positive and negative effects, respectively. HuDA_COVID is a novel study in human disposition analysis in COVID-19 where a weighted assignment indicating the health status is also proposed. HuDA_COVID clearly indicates a need for a methodical approach towards the human psychological needs to help the social organizations formulating holistic interventions for affected individuals.
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Present study focuses on the attitudes/perceptions regarding negative attitudes, hesitancy (uncertainty, unwillingness) and anxiety towards COVID-19 within the Saudian context. A cross-sectional web-based study uses convenience sampling technique for data collection through self-administrated validated questionnaire translated in Arabic language. Outcomes of the study revealed that more than 3/4th (80%) of respondents expressed intermediate to high levels of negative attitude towards vaccines, in general. The most common reasons for vaccine hesitancy were the concerns about the vaccine's possible side effects, not taking it as a serious infection, and its efficacy in preventing the infection. Regarding anxiety towards coronavirus, it was found to be quite low. Decision tree analysis was used to assess the relationship between hesitancy and demographic characteristics of the respondents. Findings of the study pinpoint specific areas, on which to focus on, for the health care administrators in case of resurgence of the pandemic. The health administrators may incorporate the suggestions of the present study when framing their future policies for enhancing confidence and alleviating fears of the populace at large to receive COVID-19 vaccination.
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This paper questions the evaluation of innovation systems and innovation measurements and the effectiveness of innovation policies applied at the territorial level by assessing whether the existing European regional scoreboard is effective in providing accurate inputs for decision-makers in mountainous regions. The aim of the research is to provide, through comparative analysis by using statistical multi-methods of two mountainous macro-regions (the Alps and the Carpathians), a possible and available path to develop novel perspectives and alternative views on innovation systems' performance for informed and territorial-based policy making by using the indicators of the Regional Innovation Scoreboard. The methodology used includes descriptive statistics, chi-square bivariate test, Student's t test, one-way ANOVA with Bonferroni post hoc multiple comparisons, multilinear regression analysis, and decision tree with CRT (classification and regression trees) algorithm. Our results emphasize the similarities and differences between the Alpine and Carpathian mountain regions, find the best predictors for each mountain region, and provide a scientific basis for the development of a holistic approach linking measurement theory, innovation systems, innovation policies, and their territorial approach toward sustainable development of mountain areas. The paper's contribution is relevant in the context of remote, rural, and mountain areas, which are usually left behind in terms of innovation chances and in the context of the COVID-19 aftermath with budget constraints. The present results are pertinent for designing effective smart specialization strategies in these regions due to the difficulties that most remote areas and less developed regions are facing in developing innovation policies. © 2023 by the authors.
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In recent years, the number of online courses in India has skyrocketed especially due to the Covid pandemic. The most significant increments have happened in degree colleges, where 85% concur that internet based courses are important for their drawn-out procedure when contrasted with 60% in 2015. The distribution of online courses has evolved dramatically as technology has advanced. Web-based platform provides new challenges for both teachers and students. Teachers should be clear about the effectiveness of online learning in teaching students. For that, the possibilities of online learning should be compared with traditional learning. Students are evaluated based on their focus on online learning. This study aims to determine the efficacy of online courses by predicting student performance in an e-learning system. These research findings evaluate modern learning methods, highlight students' potential and help teachers understand how to assess and lead students on online platforms. © 2023 IEEE.