Efficacy of the confinement policies on the COVID-19 spread dynamics in the early period of the pandemic

Spread dynamics and the confinement policies of COVID-19 exhibit different patterns for different countries. Numerous factors affect such patterns within each country. Examining these factors, and analyzing the confinement practices allow government authorities to implement effective policies in the future. In addition, they help the authorities to distribute healthcare resources optimally without overwhelming their systems. In this empirical study, we use a clustering-based approach, Hierarchical Cluster Analysis (HCA) on time-series data to capture the spread patterns at various countries. We particularly investigate the confinement policies adopted by different countries and their impact on the spread patterns of COVID-19. We limit our investigation to the early period of the pandemic, because many governments tried to respond rapidly and aggressively in the beginning. Moreover, these governments adopted diverse confinement policies based on trial-and-error in the beginning of the pandemic. We found that implementations of the same confinement policies may exhibit different results in different countries. Specifically, lockdowns become less effective in densely populated regions, because of the reluctance to comply with social distancing measures. Lack of testing, contact tracing, and social awareness in some countries forestall people from self-isolation and maintaining social distance. Large labor camps with unhealthy living conditions also aid in high community transmissions in countries depending on foreign labor. Distrust in government policies and fake news instigate the spread in both developed and under-developed countries. Large social gatherings play a vital role in causing rapid outbreaks almost everywhere. An early and rapid response at the early period of the pandemic is necessary to contain the spread, yet it is not always sufficient.

Multidimensional Machine Learning for Assessing Parameters Associated With COVID-19 in Vietnam: Validation Study.

BACKGROUND: Machine learning (ML) is a type of artificial intelligence strategy. Its algorithms are used on big data sets to see patterns, learn from their results, and perform tasks autonomously without being instructed on how to address problems. New diseases like COVID-19 provide important data for ML. Therefore, all relevant parameters should be explicitly quantified and modeled. OBJECTIVE: The purpose of this study was to determine (1) the overall preclinical characteristics, (2) the cumulative cutoff values and risk ratios (RRs), and (3) the factors associated with COVID-19 severity in unidimensional and multidimensional analyses involving 2173 SARS-CoV-2 patients. METHODS: The study population consisted of 2173 patients (1587 mild status [mild group] and asymptomatic patients, 377 moderate status patients [moderate group], and 209 severe status patients [severe group]). The status of the patients was recorded from September 2021 to March 2022. Two correlation tests, relative risk, and RR were used to eliminate unbalanced parameters and select the most remarkable parameters. The independent methods of hierarchical cluster analysis and k-means were used to classify parameters according to their r values. Finally, network analysis provided a 3-dimensional view of the results. RESULTS: COVID-19 severity was significantly correlated with age (mild-moderate group: RR 4.19, 95% CI 3.58-4.95; P<.001), scoring index of chest x-ray (mild-moderate group: RR 3.29, 95% CI 2.76-3.92; P<.001; moderate-severe group: RR 3.03, 95% CI 2.4023-3.8314; P<.001), percentage of neutrophils (mild-moderate group: RR 3.18, 95% CI 2.73-3.70; P<.001; moderate-severe group: RR 3.32, 95% CI 2.6480-4.1529; P<.001), quantity of neutrophils (moderate-severe group: RR 3.15, 95% CI 2.6153-3.8025; P<.001), albumin (moderate-severe group: RR 0.46, 95% CI 0.3650-0.5752; P<.001), C-reactive protein (mild-moderate group: RR 3.4, 95% CI 2.91-3.97; P<.001), and ratio of lymphocytes (moderate-severe group: RR 0.34, 95% CI 0.2743-0.4210; P<.001). Significant inversion of correlations among the severity groups is important. Alanine transaminase and leucocytes showed a significant negative correlation (r=-1; P<.001) in the mild group and a significant positive correlation in the moderate group (r=1; P<.001). Transferrin and anion Cl showed a significant positive correlation (r=1; P<.001) in the mild group and a significant negative correlation in the moderate group (r=-0.59; P<.001). The clustering and network analysis showed that in the mild-moderate group, the closest neighbors of COVID-19 severity were ferritin and age. C-reactive protein, scoring index of chest x-ray, albumin, and lactate dehydrogenase were the next closest neighbors of these 3 factors. In the moderate-severe group, the closest neighbors of COVID-19 severity were ferritin, fibrinogen, albumin, quantity of lymphocytes, scoring index of chest x-ray, white blood cell count, lactate dehydrogenase, and quantity of neutrophils. CONCLUSIONS: This multidimensional study in Vietnam showed possible correlations between several elements and COVID-19 severity to provide clinical reference markers for surveillance and diagnostic management.

Cluster analysis of bovine respiratory disease (BRD)-associated pathogens shows the existence of two epidemiological patterns in BRD outbreaks.

A hierarchical cluster analysis was used to classify outbreaks of bovine respiratory disease (BRD; n = 156) in natural groups according to the detection of nine pathogens (parainfluenza 3 virus (PI-3), bovine respiratory syncytial virus (BRSV), bovine coronavirus (BCV), bovine viral diarrhea virus (BVDV), and bovine herpesvirus 1 (BHV-1), Mannheimia haemolytica, Pasteurella multocida, Histophilus somni, and Mycoplasma bovis. Pathogens were detected by individual q-PCRs. Two clusters were identified. Cluster 1 was characterized by a relatively high frequency (40-72%) of four BRD-associated viruses, supporting their primary involvement in BRD. Cluster 2 was characterized by frequencies of PI-3, BRSV, or BVDV below 10% each. P. multocida and M. haemolytica were detected with high frequencies in both clusters (P > 0.05), while M. bovis and H. somni showed a significantly higher frequency in cluster 1and 2, respectively. Outbreaks in cluster 1 were associated with preweaning calves younger than 5 months (OR 2.2; 95% CI 1.1-4.5) and with cold months, whereas cluster 2 was associated with fattening calves older than 5 months after arrival to feedlots and without any seasonality. Thus, in addition to the classic epidemiological BRD pattern characterized by the primary involvement of viruses occurring preferably during winter and affecting young calves, there is a second pattern in which viruses would be less relevant, affecting mainly calves older than 5 months at any time of the year. This study allows a better understanding of the BRD epidemiology, which can be useful when implementing management and prophylaxis measures for a better control of this disease.

Attitudes wards Online Learning ring COVID-19: A Cluster and Sentiment Analyses

This paper investigated the attitudes of 702 college students toward the implementation of fully online learning during the COVID-19 pandemic. Toward this goal, responses of the students were collected and analyzed through hierarchical cluster and sentiment analyses using the R software. Hierarchical cluster analysis revealed hopeful and apprehensive attitudes toward online learning. Advantages of online learning emerged as positive sentiments while challenges and their impact on mental health emerged as negative sentiments. It is concluded that online learning is a promising platform of learning provided that its shortcomings are addressed. Implications to teaching are offered. © 2022 The authors and IOS Press.

MOTIVATIONAL DIFFERENCES AMONG SOFTWARE PROFESSIONALS

[Context] The motivation and well-being of software professionals are challenged. COVID-19 pandemic shifted the work landscape, making hybrid and remote workplace settings the standard and putting previously established motivation management tools at risk. Increasing autonomous motivation of software professionals and optimizing multitasking to remain within preferred IT roles might be one approach to overcoming the new obstacles. [Method] Using a quantitative approach, the present study examined the proposed nomological network of software engineering roles, motivation, and personality traits. A conveniently sampled quantitative survey was employed in eight IT companies and two professional IT forums. It produced a considerable (N = 243) data corpus. Based on the state-of-the-art research, hypotheses were formulated, and their statistical counterparts tested by suitable statistical methods, such as the Kruskal-Wallis test. In addition, hierarchical cluster analysis was employed to meaningfully characterize personal differences among software professionals. Finally, correlation analysis was used to derive the strengths of the causal relationships. [Result] Software professionals in this study were of four distinct personality types with varying motivational levels. The openness/intellect dimension was found to significantly nurture motivation in project manager, developer, and analytical roles. In contrast, neuroticism was detrimental to motivation in all roles. The results and future study recommendations were discussed. © 2022 IDIMT. All rights reserved.

Efforts in adopting the ultra-processed food and soft drinks labeling legislation in a COVID-19 environment: The cases of Colombia and Mexico

Diabetes contributes to COVID-19 deaths in Colombia and Mexico, where the latter having the highest prevalence of diabetes among OECD countries. Some reports consider that advertising influences diabetes by confusing labels on ultra-processed foods and soft drinks that lead to unhealthy food choices. Both countries are in the process of modifying their labeling legislation;however, governments and food industries have pushed to delay its implementation. Using a mixed research design, we interviewed 550 consumers in both countries during June?July 2020;a high number of respondents misunderstand today's food labeling and are unaware of the new labeling legislation. Respondents strongly agree that the food industry should be in charge of changing the labels;otherwise, they would consider not buying their products. Using cluster analysis, we identified three groups that would help design public policies, nutritional and educational campaigns. Although changes in food labeling alone are not enough to reduce obesity and diabetes rates, food labels constitute public health tools due they assist consumers to make food and nutritional choices (considering that nutrition can help prevent and overcome COVID-19). The costs of maintaining current labels could increase Colombians and Mexicans illnesss and poverty. These deceptive practices of the food industry would harm their brands.

Analysis of the Hungarian investment funds along economic cycles

As a result of the 2008 financial crisis, the international financial system underwent a fundamental change. The crisis has highlighted various weaknesses in the economic system. One of these weaknesses was the unregulated nature of investment markets and their inefficient structural structure. Funds managed by investment fund managers have also been hit hard by the crisis. In the post-crisis period of 2008, there was a dynamic economic boom, which also affected the types of investment funds and their changes. However, the economic crisis caused by the COVID-19 pandemic from 2020 onwards is a special crisis. Its unique nature is reflected in the fact that financial markets have remained stable under the influence of central banks. This, in turn, did not necessarily affect the investment market, and in particular investment funds, as expected in the event of a downturn. In our research, we illustrate the change of investment funds along economic cycles through the quantitative changes of Hungarian investment funds. In our analysis, we illustrate the evolution of fund changes through hierarchical cluster analyzes. In the course of our research, we found that Hungarian investment funds move in line with market and retail investment trends, and the structure of investment funds does not show a significant change during the sixteen years examined, regardless of changes in economic cycles. © Institute of Society Transformation, 2021

Effects of COVID-19 lockdown and unlock on the health of tropical large river with associated human health risk.

River Damodar (India) is one of the most significant tropical large rivers and this river is carrying predominantly industrial effluents, urban sewage, and non-degradable chemical agricultural fertilizers. Several industries, cities, and townships directly depend on this important river throughout the year. It is highly essential to evaluate its surface water quality, characteristics, and improvement status during the COVID-19 lockdown and unlock phases. The major objectives of the present study are to analyse changing nature of heavy metals (Zn, Cd, Pb, Ni, Cr, and Fe) and microbial load (TVC, TC, and FC) of river water and to identify heavy metals impact on water quality and human health in pre, during, and after unlocking of COVID-19 lockdown. Here, a total of 33 water samples have been collected in the pre-lockdown, lockdown, and unlock phases. The results showed that decreasing trend of the microbial load was found in the lockdown phase. Heavy metal pollution index (HPI) illustrated that all samples are highly polluted (HPI > 150) during the pre-lockdown phase, while during the lockdown phase; HPI showed that around 54.54% of samples have been positively changed (low pollution level). During the unlock phase, 45.45% of samples were again amplified to the high pollution level. Pearson's correlation coefficient and hierarchical cluster analysis indicated strong relation among heavy metals with faecal coliform at a 0.05% level of significance. Noncarcinogenic hazard index (HI) shows the higher possibility of health risk (HI > 1) particularly for children in all the phases and during the lockdown phase, 36.36% of samples showed no possible health risk for adults (HI < 1). However, HI of dermal contact showed no possible health risk for children and adults in the assessment periods. This applied research can definitely assist planners and administrators in making effective solutions regarding public health.

Exploring the perceptions of Malaysian Gen Z towards the impact of COVID-19 on sustainable development.

COVID-19 has affected the pursuit of sustainable development in multifaceted ways; this study investigates Malaysian Gen Z perceptions of the COVID-19 pandemics' disruptions to sustainable development. The exploratory research began with brainstorming from Malaysian Gen Z individuals with the following excerpt, 'The COVID-19 pandemic has disrupted the world and led to unprecedented change. How do you believe this has impacted either positively or negatively, the global pursuit of sustainable development?' Ninety-eight unique statements were generated with subsequent participants sorting these statements into thematic groups before rating each of them on impact and duration. Subsequently, multi-dimensional scaling and cluster analysis was performed with eight-cluster solution being proposed. This study suggests that the pandemic has contributed both positively and negatively to sustainable development, while also highlighting the duration of these impacts. This community-based participatory research provides a guide for policy to mitigate negative impacts whilst also attempting to fully realise the positive impacts in response to managing the unprecedented effects of the pandemic.

An Evaluation of the Potential of Essential Oils against SARS-CoV-2 from In Silico Studies through the Systematic Review Using a Chemometric Approach.

Essential oils (EOs) and their compounds have attracted particular attention for their reported beneficial properties, especially their antiviral potential. However, data regarding their anti-SARS-CoV-2 potential are scarce in the literature. Thus, this study aimed to identify the most promising EO compounds against SARS-CoV-2 based on their physicochemical, pharmacokinetic, and toxicity properties. A systematic literature search retrieved 1669 articles; 40 met the eligibility criteria, and 35 were eligible for analysis. These studies resulted in 465 EO compounds evaluated against 11 human and/or SARS-CoV-2 target proteins. Ninety-four EO compounds and seven reference drugs were clustered by the highest predicted binding affinity. Furthermore, 41 EO compounds showed suitable drug-likeness and bioactivity score indices (≥0.67). Among these EO compounds, 15 were considered the most promising against SARS-CoV-2 with the ADME/T index ranging from 0.86 to 0.81. Some plant species were identified as EO potential sources with anti-SARS-CoV-2 activity, such as Melissa officinalis Arcang, Zataria multiflora Boiss, Eugenia brasiliensis Cambess, Zingiber zerumbet Triboun & K.Larsen, Cedrus libani A.Rich, and Vetiveria zizanoides Nash. Our work can help fill the gap in the literature and guide further in vitro and in vivo studies, intending to optimize the finding of effective EOs against COVID-19.