Analysis of Public Transportation in Montevideo, Uruguay During the COVID-19 Pandemic

This article presents the analysis of the demand and the characterization of mobility using public transportation in Montevideo, Uruguay, during the COVID-19 pandemic. A urban data-analysis approach is applied to extract useful insights from open data from different sources, including mobility of citizens, the public transportation system, and COVID cases. The proposed approach allowed computing significant results to determine the reduction of trips caused by each wave of the pandemic, the correlation between the number of trips and COVID cases, and the recovery of the use of the public transportation system. Overall, results provide useful insights to quantify and understand the behavior of citizens in Montevideo, regarding public transportation during the COVID-19 pandemic. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Assessing the Current State of University-based Business Incubators (UBIS) in Canada and the UAE

There is a dearth of published research that explores UBIs from a comparative dimension across geographical and institutionalised contexts that assesses the current state and scope of UBI activities. This paper explores the current state of University-based Business Incubators (UBIs) both in the United Arab Emirates and Canada underpinned by a comparative case analysis approach. This study utilises both secondary and primary research data that was obtained through desk-based secondary research and qualitative methods of inquiry (semi-structured interviews) with UBI managers, academics, and support staff that were used to develop each case. This informed the development of 18 cases of UBIs in the United Arab Emirates and Canada (9 each, respectively). The data was collected through VoIP (Voice-Over-Internet-Protocol) and telephone during the COVID-19 pandemic from March 2021 to February 2022. The findings of the study illustrate that the Canadian context offers similar provisions of services for business incubators (BIs) but in comparison, the UAE-based university UBIs are much younger and are transitioning towards the development of various business and enterprise initiatives in Higher Education and are also focused on driving student recruitment using this provision. The value of this study is inherent in its comparative approach between two under-studied and represented empirical geographies (i.e., Canada and the UAE), the findings also indicate the divergence and specialisms adopted by institutions in the UAE based on the various provisions for the governmental vision 2030, and the empirical development of showcasing these initiatives to be novel for the efficacy of UBIs. © 2022, Academic Conferences and Publishing International Limited. All right reserved.

Conjoint Analysis on the Attributes Affecting the Customers' Preference of Hotel Accommodation

Hotel is an essential establishment that can offer lodging to travellers and accommodation for meetings/events. However due to the instability of the market in hotel, in addition to the COVID-19 pandemic, there is an increase of competition in the industry. In order to cope with this problem, hotels tend to increase its quality and services in order to meet the ideal preference of the customer. The study aimed to determine the combination of hotel attributes that the customer deemed preferred using a Conjoint Analysis Approach. The study specifically utilized attributes like price, accessibility to nearest landmark, inclusivity of breakfast, amenities, and dining options. The results show that the price was the most preferred attributes by the customer (40.787%), followed by the inclusivity of breakfast (32.913%), accessibility to nearest landmark (15.433%), hotel amenities (8.504%), and the least preferred is the dining options (2.362%). The outcome will be beneficial to the hotel owners and manager on the customer preference on the attributes. © 2022 ACM.

A Novel Machine Learning Classification Model to Analyze the COVID-19 Pandemic's Impact on Academics: Sentiment Analysis Approach

The COVID-19 Pandemic is considered as the worst situation for human beings;it affected people's lives worldwide. Due to this pandemic, the respective government authority announced the lockdown to break the coronavirus chain. The lockdown impacted people's mental health, leading to many psychological issues as well as hampered students' academics. In this chapter we have studied the impacts on students' academics due to lockdown effect. The data has been collected via a google form questionnaire circulated to various educational institutes. Further, we have developed a novel machine learning classifier model called Naïve Bayes-Support Vector Machine for analyzing the data, which utilizes the properties of both classifiers by using a deep learning framework. We have used natural language processing (TextBlob, Stanza and Vader) libraries to label the dataset and applied in the proposed NBSVM method and other machine learning models and classified the sentiments into two categories (Positive vs Negative). We also applied the natural language processing libraries used a topic-modelling technique called Latent Dirichlet Allocation to know the essential topics words of both classes from students' feedback data. The study revealed 83% and 86% accuracy for unigram and bigram, respectively, whereas the precision was 79% and recall 81%. According to NLP libraries' result, approximately 71% of the feedback's sentiment is negative, and only 16% of feedbacks are positive. The proposed model shown that (Naïve Bayes-Support Vector Machine) outperforms the other variants of the Naïve Bayes and support vector machine. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Intervention Analysis of COVID-19 Vaccination in Nigeria: The Naive Solution Versus Interrupted Time Series

In this paper, an intervention analysis approach was applied to daily cases of COVID-19 in Nigeria in order to evaluate the utilization and effect of the COVID-19 vaccine administered in the country. Data on the daily report of COVID-19 cases in Nigeria were collected and subjected to two models: the naïve solution and the interrupted time series (the intervention model). Based on the Alkaike Information Criterion (AIC), sigma2, and log likelihood values, the interrupted time series model outperformed the Naïve solution model. ARIMA (4, 1, 4) with exogenous variables was identified as the best model. It was observed that the intervention (vaccination) was not significant at the 5% level of significance in reducing the number of daily COVID-19 cases in Nigeria since the start of the vaccination on March 5, 2021, until March 28, 2022. Also, the ARIMA (4, 1, 4) forecasts indicated that there will be surge in the number of daily COVID-19 cases in Nigeria between January and April 2023. As a result, we recommend strict adherence to COVID-19 protocols as well as further vaccination and sensitization programs to educate people on the importance of vaccine uptake and avoid Corona virus spread in the year 2023 and beyond. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

The Sustainability of the Supply Chain of the City of Meknes in Fruits and Vegetables Tested by the Covid-19

This article examines the sustainability of the supply chain of fruits and vegetables (SCF V), in Meknes, Morocco, in relation with Covid-19 pandemic. Our methodology is based on a theoretical framework associated with the dimensions of sustainability (economic, social, environmental, territorial and health). We mobilize a database resulting from 121 surveys conducted among producers, distributors and consumers. To process the collected data, we have chosen the method of weighting the sub-indicators by maximizing scores from the Data Envelopment Analysis (DEA) approach and the method of weighting by arithmetic means. The results show that the overall durability of SCFV/Meknes has improved considerably, especially during the strict containment related to COVID-19. © 2022 IEEE.

A Topological Data Analysis Approach to the COVID-19

Topological data analysis (TDA) has emerged as a method for understanding data clouds by extracting and comparing the structure of datasets. This paper applies one of the TDA instruments available which is called the Mapper algorithm to analyze the COVID-19 data in China. The Mapper graphs generated by the algorithm successfully reflect the development of COVID-19 across China and provide a relatively complete visualization of the pandemic. Experimental results indicate that the proposed method may have the potential to become a robust predictive tool for the spread of the coronavirus. © 2022 IEEE.

Using machine learning techniques to study economic trends in various U.S. industries in the post-epidemic era

The aim of the project is to predict and analyse broad trends across the US economy using stock data from mainstream companies in six industries on Forbes 2000 and data from COVID-19. A time series analysis approach was used to predict the daily increases in each company's share price. The following five supervised learning techniques (logistic regression, random forest, decision tree, neural network and XGBoost) were used. As the accuracy of the results predicted by the different models for each company varies considerably, only the results predicted by the most accurate model for each company have been selected for analysed. The results show that the Electronic Pleased Technology Industry and the Social Entertainment Internet Industry remain break-even for COVID-19;the E-Commerce Industry shows a significant increase;The Financial Services Industry shows a significant drop in share price, while the Insurance Industry and Pharmaceutical Industry show a small drop in share price. © COPYRIGHT SPIE. Downloading of the is permitted for personal use only.

Redefining knowledge management in the era of CoviD-19: A concept analysis approach in the context of higher education

Higher education institutions are frequently using knowledge management to achieve their mission. Due to COVID-19, education has moved to online, this has led to an increasing need for knowledge management and thus put under scrutiny to obtain the best results. However, successful knowledge management requires definition as the first step. Knowledge Management is a complex and multi-dimensional concept making its definition hard to craft. The lack of well-defined universally accepted definition is a problem associated with KM is due to the diversity of areas it exists in, lack of consensus and others. In this paper, an objective concept analysis was undertaken to examine the attributes, characteristics and uses of KM for the purpose of defining KM in the higher educational institution. Therefore, this paper aims at deriving a definition of the term knowledge management with the help of concept analysis which is a recognized method for the terminological studies. The study concluded with a definition based on the results of the analysis. © 2021 IEEE.

A functional data analysis approach to assess the prognostic value of SARS-CoV-2 infections surrogate data

COVID-19 is characterised by quite diverse prognosis. While the majority of infected individuals present no or very mild symptoms, some individuals develop severe disease requiring intensive care. This work leverages the parameters of a virtual cohort of infected individuals generated by a computational immunology model. In so doing we identify the most relevant immunological parameters for the classification of severe COVID-19 cases. The functional data analysis approach used turns out to be appropriate to analyse the output of the computational model. In this work, we classify the disease prognosis using both statistical models and machine learning algorithms adapted from functional data analysis and we compare their performances. © 2021 IEEE.

Coronavirus disease-2019 risk management using strengths, weaknesses, opportunities, threats analysis approach in the health system: A qualitative multimethod study.

BACKGROUND: Utilizing the successful experiences of countries and local regions can be useful in the management and control of coronavirus disease-2019 (COVID-19), so the research team aims to determine and extract the strengths, weaknesses, opportunities, and threats of the health system in the risk management of COVID-19 using strengths, weaknesses, opportunities, threats (SWOT) analytical approach. MATERIALS AND METHODS: This study was performed by a qualitative multimethod approach. In addition to reviewing the minutes of meetings and approvals of the Provincial Anti-Corona Headquarters, focused group meetings and in-depth semi-structured individual interviews were conducted. The results were extracted based on the SWOT analytical approach in the form of strengths, weaknesses, opportunities, and threats of the health system and then based on the SWOT matrix, the necessary strategies were identified. RESULTS: In the necessary strategies, based on SWOT matrix in SO strategies: SO1, formation of regional health assessment teams; SO2, promotion of preparedness, resilience, and effective response; SO3, activation of research and training centers; SO4, integrated management, supervision, and coordination; in WO strategies: WO1, analysis and COVID-19 risk monitoring; WO2, communication and risk information management; WO3, people-based management; and WO4, activation of local economic institutions and manufacturing centers; in ST: ST1, comprehensive care system strategies; and ST2, enhancing social trust with a transparency approach; and finally in WT strategies; WT1, stress management; and WT2, specific financial system design for disaster management were identified. CONCLUSION: Now, for the prevention and control of this disease, the need of empathy and participation of all human societies is felt more than anything else. These experience and analysis are based on the SWOT approach for the health system to be able to provide solutions and practical points that can be used by stakeholders.

Machine learning models for COVID-19 confirmed cases prediction: A meta-analysis approach

COVID-19, CoronaVirus Disease - 2019, belongs to the genus of Coronaviridae. COVID-19 is no longer pandemic but rather endemic with the number of deaths around the world of more than 3,166,516 cases. This reality has placed a massive burden on limited healthcare systems. Thus, many researchers try to develop a prediction model to further understand this phenomenon. One of the recent methods used is machine learning models that learn from the historical data and make predictions about the events. These data mining techniques have been used to predict the number of confirmed cases of COVID-19. This paper investigated the variability of the effect size on the correlation performance of machine learning models in predicting confirmed cases of COVID-19 using meta-analysis. It explored the correlation between actual and predicted COVID-19 cases from different Neural Network machine learning models by means of estimated variance, chi-square heterogeneity (Q), heterogeneity index (I2) and random effect model. The results gave a good summary effect of 95% confidence interval. Based on chi-square heterogeneity (Q) and heterogeneity index (I2), it was found that the correlations were heterogeneous among the studies. The 95% confidence interval of effect summary also supported the difference in correlation between actual and predicted number of confirmed COVID-19 cases among the studies. There was no evidence of publication bias based on funnel plot and Egger and Begg's test. Hence, findings from this study provide evidence of good prediction performance from the Neural Network model based on a combination of studies that can later serve in the prediction of COVID-19 confirmed cases. © Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence.

A Persons-Centered Approach for Prevention of COVID-19 Disease and Its Impacts in Persons With Disabilities.

The World Health Organization (WHO) considers COVID-19 a great threat to humanity and, thus, declared the COVID-19 outbreak a pandemic on March 11, 2020. To limit its transmission, governments announced lockdowns in their respective nations, and recommended control measures, including behavior change. Persons with disabilities (PwDs) are among the population that may be at a higher risk of becoming infected and may suffer serious illness due to COVID-19. Additionally, lockdowns pose immense challenges and have tremendous impacts on PwDs in terms of receiving their daily support. To mitigate these challenges, their impact, and to reduce the risk of infection, it is important to design strategies that can improve the overall outcome for PwDs. This study therefore intends to provide a uniform strategy or guideline using the person-centered approach principles which is perhaps the most feasible and implementable approach to circumvent the challenges faced by PwDs during emergency lockdowns. Two case studies are used as examples. This pandemic also provides an opportunity for health care planners and policymakers in the health sector to implement reforms to ensure disability inclusiveness in potential future emergency lockdowns.