scholarly journals Sociocultural behavioral traits in modelling the prediction of COVID-19 infection rates

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Charles Alba ◽  
Manasvi M. Mittal
Keyword(s):  
Global Health ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Infection Rates ◽  
Biomedical Sciences ◽  
Content Type ◽  
Behavioral Traits ◽  
Index Model ◽  
Behavioral Predictors ◽  
Cart Algorithm

PurposeOver the past decades, many health authorities and public policy experts have traditionally relied on indicators that are dependent on a nation's economy, its health-care infrastructure advancements, and superiority in biomedical sciences and technology to predict potential infection rates should a health pandemic occur. One such commonly relied-upon indicator was that of the Global Health Security (GHS) Index. However, the coronavirus disease 2019 (COVID-19) pandemic has shown how such variables prove to be inaccurate in predicting the infection rates during a global health pandemic. Hence, this paper proposes the utilization of socio-cultural behavioral traits to predict a country's COVID-19 infection rates.Design/methodology/approachThis is achieved by proposing a model involving the classification and regression tree (CART) algorithm and a Poisson regression against the six selected cultural behavioral predictors consisting of individualism, power distance, masculinity, uncertainty avoidance, long-term orientation, and indulgence.FindingsThe results show that all the selected cultural behavioral predictors are significant in impacting COVID-19 infection rates. Furthermore, the model outperforms the conventional GHS Index model based on a means squared error comparison.Research limitations/implicationsThe authors hope that this study would continue promoting the use of cultures and behaviors in modeling the spread of health diseases.Practical implicationsThe authors hope that their works could prove beneficial to public office holders, as well as health experts working in health facilities, in better predicting potential outcomes during a health pandemic, thus allowing them to plan and allocate resources efficiently.Originality/valueThe results are a testament to the fact that sociocultural behavioral traits are more reliant predictors in modeling cross-national infection rates of global health pandemics, like that of COVID-19, as compared to economic-centric indicators.

An aggregated and dynamic analysis of innovations in campus sustainability

2018 ◽  
Vol 19 (2) ◽  
pp. 353-375 ◽  
Author(s):  
Camille Washington-Ottombre ◽  
Siiri Bigalke
Keyword(s):  
Higher Education ◽  
Assessment Tool ◽  
Sustainability Assessment ◽  
Regression Tree ◽  
Education Institution ◽  
Classification And Regression Tree ◽  
Internal Factors ◽  
Content Type ◽  
Campus Sustainability

Purpose This paper aims to compose a systematic understanding of campus sustainability innovations and unpack the complex drivers behind the elaboration of specific innovations. More precisely, the authors ask two fundamental questions: What are the topics and modes of implementation of campus sustainability innovations? What are the external and internal factors that drive the development of specific innovations? Design/methodology/approach The authors code and analyze 454 innovations reported within the Sustainability Tracking Assessment and Rating System (STARS), the campus sustainability assessment tool of the Association for the Advancement of Sustainability in Higher Education. Using descriptive statistics and illustrations, the paper assesses the state of environmental innovations (EIs) within STARS. Then, to evaluate the role of internal and external drivers in shaping EIs, the authors have produced classification and regression tree models. Findings The authors’ analysis shows that external and internal factors provide incentives and a favorable context for the implementation of given EIs. External drivers such as climatic zones, local income and poverty rate drive the development of several EIs. Internal drivers beyond the role of the agent of change, often primarily emphasized by past literature, significantly impact the implementation of given EIs. The authors’ work also reveals that EIs often move beyond traditional mitigation approaches and the boundaries of campus. EIs create new dynamics of innovation that echo and reinforce the culture of a higher education institution. Originality/value This work provides the first aggregated picture of EIs in the USA and Canada. It produces a new and integrated understanding of the dynamics of campus sustainability that complexifies narratives and contextualizes the role of change agents.

Using machine learning techniques to develop prediction models for detecting unpaid credit card customers

2020 ◽  
Vol 39 (5) ◽  
pp. 6073-6087
Author(s):  
Meltem Yontar ◽  
Özge Hüsniye Namli ◽  
Seda Yanik
Keyword(s):  
Decision Tree ◽  
Credit Card ◽  
Banking Sector ◽  
Performance Metrics ◽  
Prediction Models ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Cart Algorithm

Customer behavior prediction is gaining more importance in the banking sector like in any other sector recently. This study aims to propose a model to predict whether credit card users will pay their debts or not. Using the proposed model, potential unpaid risks can be predicted and necessary actions can be taken in time. For the prediction of customers’ payment status of next months, we use Artificial Neural Network (ANN), Support Vector Machine (SVM), Classification and Regression Tree (CART) and C4.5, which are widely used artificial intelligence and decision tree algorithms. Our dataset includes 10713 customer’s records obtained from a well-known bank in Taiwan. These records consist of customer information such as the amount of credit, gender, education level, marital status, age, past payment records, invoice amount and amount of credit card payments. We apply cross validation and hold-out methods to divide our dataset into two parts as training and test sets. Then we evaluate the algorithms with the proposed performance metrics. We also optimize the parameters of the algorithms to improve the performance of prediction. The results show that the model built with the CART algorithm, one of the decision tree algorithm, provides high accuracy (about 86%) to predict the customers’ payment status for next month. When the algorithm parameters are optimized, classification accuracy and performance are increased.

scholarly journals Spatiotemporal Analysis of Cryptosporidium Species/Genotypes and Relationships with Other Zoonotic Pathogens in Surface Water from Mixed-Use Watersheds

2012 ◽  
Vol 79 (2) ◽  
pp. 434-448 ◽  
Author(s):  
Graham Wilkes ◽  
Norma J. Ruecker ◽  
Norman F. Neumann ◽  
Victor P. J. Gannon ◽  
Cassandra Jokinen ◽  
...  
Keyword(s):  
Land Use ◽  
Surface Water ◽  
Water Samples ◽  
Regression Tree ◽  
Infection Risk ◽  
Human Infection ◽  
Classification And Regression Tree ◽  
Odds Ratios ◽  
Content Type ◽  
Zoonotic Pathogens

ABSTRACTNearly 690 raw surface water samples were collected during a 6-year period from multiple watersheds in the South Nation River basin, Ontario, Canada.Cryptosporidiumoocysts in water samples were enumerated, sequenced, and genotyped by detailed phylogenetic analysis. The resulting species and genotypes were assigned to broad, known host and human infection risk classes. Wildlife/unknown, livestock, avian, and human host classes occurred in 21, 13, 3, and <1% of sampled surface waters, respectively.Cryptosporidium andersoniwas the most commonly detected livestock species, while muskrat I and II genotypes were the most dominant wildlife genotypes. The presence ofGiardiaspp.,Salmonellaspp.,Campylobacterspp., andEscherichia coliO157:H7 was evaluated in all water samples. The greatest significant odds ratios (odds of pathogen presence when host class is present/odds of pathogen presence when host class is absent) forGiardiaspp.,Campylobacterspp., andSalmonellaspp. in water were associated, respectively, with livestock (odds ratio of 3.1), avian (4.3), and livestock (9.3) host classes. Classification and regression tree analyses (CART) were used to group generalized host and human infection risk classes on the basis of a broad range of environmental and land use variables while tracking cooccurrence of zoonotic pathogens in these groupings. The occurrence of livestock-associatedCryptosporidiumwas most strongly related to agricultural water pollution in the fall (conditions also associated with elevated odds ratios of other zoonotic pathogens occurring in water in relation to all sampling conditions), whereas wildlife/unknown sources ofCryptosporidiumwere geospatially associated with smaller watercourses where urban/rural development was relatively lower. Conditions that support wildlife may not necessarily increase overall human infection risks associated withCryptosporidiumsince mostCryptosporidiumgenotypes classed as wildlife in this study (e.g., muskrat I and II genotype) do not pose significant infection risks to humans. Consequently, from a human health perspective, land use practices in agricultural watersheds that create opportunities for wildlife to flourish should not be rejected solely on the basis of their potential to increase relative proportions of wildlife fecal contamination in surface water. The present study suggests that mitigating livestock fecal pollution in surface water in this region would likely reduce human infection risks associated withCryptosporidiumand other zoonotic pathogens.

scholarly journals Multicenter Study of the Relationship between Carbapenem MIC Values and Clinical Outcome of Patients with Acinetobacter Bacteremia

2017 ◽  
Vol 61 (9) ◽  
Author(s):  
Ya-Sung Yang ◽  
Yung-Chih Wang ◽  
Shu-Chen Kuo ◽  
Chung-Ting Chen ◽  
Chang-Pan Liu ◽  
...  
Keyword(s):  
Susceptibility Testing ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Rank Test ◽  
Bivariate Analysis ◽  
Content Type ◽  
Medical Centers ◽  
Regression Tree Analysis ◽  
Log Rank Test ◽  
The Relationship

ABSTRACT The Clinical and Laboratory Standards Institute (CLSI) and the European Committee on Antimicrobial Susceptibility Testing (EUCAST) offer different recommendations for carbapenem MIC susceptibility breakpoints for Acinetobacter species. In addition, the clinical efficacy of the intermediate category remains uncertain. This study was designed to determine the optimal predictive breakpoints based on the survival of patients with Acinetobacter bacteremia treated with a carbapenem. We analyzed the 30-day mortality rates of 224 adults who received initial carbapenem monotherapy for the treatment of Acinetobacter bacteremia at 4 medical centers over a 5-year period, according to the carbapenem MICs of the initial isolates. The 30-day mortality was about 2-fold greater in patients whose isolates had carbapenem MICs of ≥8 mg/liter than in those with isolates with MICs of ≤4 mg/liter. The differences were significant by bivariate analysis (53.1% [60/113] versus 25.2% [28/111], respectively; P < 0.001) and on survival analysis by the log rank test (P < 0.001). Classification and regression tree analysis revealed a split between MICs of 4 and 8 mg/liter and predicted the same difference in mortality, with a P value of <0.001. Carbapenem treatment for Acinetobacter bacteremia caused by isolates with carbapenem MICs of ≥8 mg/liter was an independent predictor of 30-day mortality (odds ratio, 4.218; 95% confidence interval, 2.213 to 8.039; P < 0.001). This study revealed that patients with Acinetobacter bacteremia treated with a carbapenem had a more favorable outcome when the carbapenem MICs of their isolates were ≤4 mg/liter than those with MICs of ≥8 mg/liter.

scholarly journals Statistical Analysis of Treatment Planning Parameters for Prediction of Delivery Quality Assurance Failure for Helical Tomotherapy

2020 ◽  
Vol 19 ◽  
pp. 153303382097969
Author(s):  
Kyung Hwan Chang ◽  
Young Hyun Lee ◽  
Byung Hun Park ◽  
Min Cheol Han ◽  
Jihun Kim ◽  
...  
Keyword(s):  
Quality Assurance ◽  
Treatment Planning ◽  
Planning Process ◽  
Helical Tomotherapy ◽  
Regression Tree ◽  
Roc Curves ◽  
Classification And Regression Tree ◽  
Variable Model ◽  
Predicted Probability ◽  
Cart Algorithm

Purpose: This study aimed to investigate the parameters with a significant impact on delivery quality assurance (DQA) failure and analyze the planning parameters as possible predictors of DQA failure for helical tomotherapy. Methods: In total, 212 patients who passed or failed DQA measurements were retrospectively included in this study. Brain (n = 43), head and neck (n = 37), spinal (n = 12), prostate (n = 36), rectal (n = 36), pelvis (n = 13), cranial spinal irradiation and a treatment field including lymph nodes (n = 24), and other types of cancer (n = 11) were selected. The correlation between DQA results and treatment planning parameters were analyzed using logistic regression analysis. Receiver operating characteristic (ROC) curves, areas under the curves (AUCs), and the Classification and Regression Tree (CART) algorithm were used to analyze treatment planning parameters as possible predictors for DQA failure. Results: The AUC for leaf open time (LOT) was 0.70, and its cut-off point was approximately 30%. The ROC curve for the predicted probability calculated when the multivariate variable model was applied showed an AUC of 0.815. We confirmed that total monitor units, total dose, and LOT were significant predictors for DQA failure using the CART. Conclusions: The probability of DQA failure was higher when the percentage of LOT below 100 ms was higher than 30%. The percentage of LOT below 100 ms should be considered in the treatment planning process. The findings from this study may assist in the prediction of DQA failure in the future.

Remote Sense Image Classification Based on CART Algorithm

2013 ◽  
Vol 864-867 ◽  
pp. 2782-2786
Author(s):  
Bao Hua Yang ◽  
Shuang Li
Keyword(s):  
Remote Sensing ◽  
Decision Tree ◽  
Regression Tree ◽  
Remote Sensing Image ◽  
Classification And Regression Tree ◽  
Classification Method ◽  
Remote Sense ◽  
Data Source ◽  
Cart Algorithm ◽  
Tree Method

This papers deals with the study of the algorithm of classification method based on decision tree for remote sensing image. The experimental area is located in the Xiangyang district, the data source for the 2010 satellite images of SPOT and TM fusion. Moreover, classification method based on decision tree is optimized with the help of the module of RuleGen and applied in regional remote sensing image of interest. The precision of Maximum likelihood ratio is 95.15 percent, and 94.82 percent for CRAT. Experimental results show that the classification method based on classification and regression tree method is as well as the traditional one.

scholarly journals Pharmacokinetic/Pharmacodynamic Determinants of Vancomycin Efficacy in Enterococcal Bacteremia

2017 ◽  
Vol 62 (3) ◽  
Author(s):  
Muhammed Taufiq Bin Jumah ◽  
Shawn Vasoo ◽  
Sanjay R. Menon ◽  
Partha Pratim De ◽  
Michael Neely ◽  
...  
Keyword(s):  
Primary Outcome ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Vancomycin Therapy ◽  
Content Type ◽  
Regression Tree Analysis ◽  
Gradient Diffusion ◽  
All Cause Mortality ◽  
Mic Value ◽  
Enterococcal Bacteremia

ABSTRACTWhile pharmacokinetic-pharmacodynamic targets for vancomycin therapy are recognized for invasive methicillin-resistantStaphylococcus aureusinfections, scant data are available to guide therapy for other Gram-positive infections. A retrospective single-center cohort of patients withEnterococcusbacteremia hospitalized between 1 January 2009 and 31 May 2015 were studied. The average vancomycin AUC0–24was computed using a Bayesian approach. The MIC was determined by gradient diffusion (Etest; bioMérieux), and the average AUC0–24/MIC value over the initial 72 h of therapy was calculated. We assessed 30-day all-cause mortality as the primary outcome. Classification and regression tree analysis (CART) was used to identify the vancomycin AUC0–24/MIC value associated with 30-day mortality. Fifty-seven patients with enterococcal bacteremia (32E. faecium, 21E. faecalis, and 4 otherEnterococcusspp.) were studied. The median vancomycin MIC was 0.75 mg/liter (range, 0.38 to 3 mg/liter). All-cause 30-day mortality occurred in 10 of 57 patients (17.5%). A CART-derived vancomycin AUC/MICEtestvalue of ≥389 was associated with reduced mortality (P= 0.017); failure to achieve this independently predicted 30-day mortality (odds ratio, 6.83 [95% confidence interval = 1.51 to 30.84];P= 0.01). We found that a vancomycin AUC/MICEtestvalue of ≥389 achieved within 72 h was associated with reduced mortality. Larger, prospective studies are warranted to verify the vancomycin pharmacodynamic targets associated with maximal clinical outcomes and acceptable safety.

scholarly journals Is age an additional factor in the treatment of elderly patients with glioblastoma? A new stratification model: an Italian Multicenter Study

2020 ◽  
Vol 49 (4) ◽  
pp. E13 ◽  
Author(s):  
Tamara Ius ◽  
Teresa Somma ◽  
Roberto Altieri ◽  
Filippo Flavio Angileri ◽  
Giuseppe Maria Barbagallo ◽  
...  
Keyword(s):  
Elderly Patients ◽  
Therapeutic Option ◽  
Regression Tree ◽  
Continuous Variable ◽  
Classification And Regression Tree ◽  
Imaging Data ◽  
Cart Analysis ◽  
Survival Score ◽  
Molecular Features ◽  
Cart Algorithm

OBJECTIVEApproximately half of glioblastoma (GBM) cases develop in geriatric patients, and this trend is destined to increase with the aging of the population. The optimal strategy for management of GBM in elderly patients remains controversial. The aim of this study was to assess the role of surgery in the elderly (≥ 65 years old) based on clinical, molecular, and imaging data routinely available in neurosurgical departments and to assess a prognostic survival score that could be helpful in stratifying the prognosis for elderly GBM patients.METHODSClinical, radiological, surgical, and molecular data were retrospectively analyzed in 322 patients with GBM from 9 neurosurgical centers. Univariate and multivariate analyses were performed to identify predictors of survival. A random forest approach (classification and regression tree [CART] analysis) was utilized to create the prognostic survival score.RESULTSSurvival analysis showed that overall survival (OS) was influenced by age as a continuous variable (p = 0.018), MGMT (p = 0.012), extent of resection (EOR; p = 0.002), and preoperative tumor growth pattern (evaluated with the preoperative T1/T2 MRI index; p = 0.002). CART analysis was used to create the prognostic survival score, forming six different survival groups on the basis of tumor volumetric, surgical, and molecular features. Terminal nodes with similar hazard ratios were grouped together to form a final diagram composed of five classes with different OSs (p < 0.0001). EOR was the most robust influencing factor in the algorithm hierarchy, while age appeared at the third node of the CART algorithm. The ability of the prognostic survival score to predict death was determined by a Harrell’s c-index of 0.75 (95% CI 0.76–0.81).CONCLUSIONSThe CART algorithm provided a promising, thorough, and new clinical prognostic survival score for elderly surgical patients with GBM. The prognostic survival score can be useful to stratify survival risk in elderly GBM patients with different surgical, radiological, and molecular profiles, thus assisting physicians in daily clinical management. The preliminary model, however, requires validation with future prospective investigations. Practical recommendations for clinicians/surgeons would strengthen the quality of the study; e.g., surgery can be considered as a first therapeutic option in the workflow of elderly patients with GBM, especially when the preoperative estimated EOR is greater than 80%.

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