Can Socioeconomic, Health, and Safety Data Explain the Spread of COVID-19 Outbreak on Brazilian Federative Units?

The effect of obesity on the mortality of hospitalized adults with COVID-19 considering the human development index: A systematic review and meta-analysis

The objective of this study is to verify the association between obesity and mortality in hospitalized patients with SARS-CoV-2 taking into account the Human Development Index (HDI). A search was performed in the PubMed, Virtual Health Library (Lilacs/Bireme/VHL Brazil), Embase, Web of Science, and Scopus databases from inception to May 2022. To be eligible, studies had to have cohort or case-control designs, be conducted with hospitalized adults (≥18 years old), and evaluate mortality rates between with obesity and without obesity individuals with SARS-CoV-2 confirmed by laboratory tests. The analyses were performed in Stata 12.0 using relative risk (RR) as a summary measure. Heterogeneity was explored by meta-regression and subgroup analyses considering the HDI, age, sex, and follow-up period. Out of 912 studies screened, 49 studies were eligible for qualitative synthesis, and 33 studies were eligible for quantitative analysis, representing 42,905 patients. The mortality risk from SARS-CoV-2 was higher in individuals with obesity compared with without obesity individuals only in the subgroups of patients <60 years old (RR = 1.31; 95% CI 1.18-1.45, I2  = 0.0%) and living in countries with a low HDI (RR = 1.28; 95% CI 1.10-1.48, I2  = 45.4%).

Spatiotemporal evolution of COVID-19 in Portugal's Mainland with self-organizing maps

BACKGROUND

Self-Organizing Maps (SOM) are an unsupervised learning clustering and dimensionality reduction algorithm capable of mapping an initial complex high-dimensional data set into a low-dimensional domain, such as a two-dimensional grid of neurons. In the reduced space, the original complex patterns and their interactions can be better visualized, interpreted and understood.

METHODS

We use SOM to simultaneously couple the spatial and temporal domains of the COVID-19 evolution in the 278 municipalities of mainland Portugal during the first year of the pandemic. Temporal 14-days cumulative incidence time series along with socio-economic and demographic indicators per municipality were analyzed with SOM to identify regions of the country with similar behavior and infer the possible common origins of the incidence evolution.

RESULTS

The results show how neighbor municipalities tend to share a similar behavior of the disease, revealing the strong spatiotemporal relationship of the COVID-19 spreading beyond the administrative borders of each municipality. Additionally, we demonstrate how local socio-economic and demographic characteristics evolved as determinants of COVID-19 transmission, during the 1st wave school density per municipality was more relevant, where during 2nd wave jobs in the secondary sector and the deprivation score were more relevant.

CONCLUSIONS

The results show that SOM can be an effective tool to analysing the spatiotemporal behavior of COVID-19 and synthetize the history of the disease in mainland Portugal during the period in analysis. While SOM have been applied to diverse scientific fields, the application of SOM to study the spatiotemporal evolution of COVID-19 is still limited. This work illustrates how SOM can be used to describe the spatiotemporal behavior of epidemic events. While the example shown herein uses 14-days cumulative incidence curves, the same analysis can be performed using other relevant data such as mortality data, vaccination rates or even infection rates of other disease of infectious nature.

Research Trends in Artificial Intelligence-Associated Nursing Activities Based on a Review of Academic Studies Published From 2001 to 2020

The present study referred to the technology-based learning model to conduct a systematic review of the dimensions of nursing activities, research samples, research methods, roles of artificial intelligence, applied artificial intelligence algorithms, evaluation measure of algorithms, and research foci. Based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses procedure, this study obtained and analyzed a total of 102 high-quality artificial intelligence-associated nursing activities studies published from 2001 to 2020 in the Web of Science database. The results showed: (1) In terms of nursing activities, nursing management was explored the most, followed by nursing assessment; (2) quantitative methods were most frequently adopted in artificial intelligence-associated nursing activities studies to investigate issues related to patients, followed by nursing staff; (3) the most adopted roles of artificial intelligence in artificial intelligence-associated nursing activities studies were profiling and prediction, followed by assessment and evaluation; (4) artificial intelligence-associated nursing activities studies frequently mixed applied artificial intelligence algorithms and evaluation measure of algorithms; (5) in the dimension of research foci, most studies mainly paid attention to the design or evaluation of the artificial intelligence systems/instruments, followed by investigating the correlation and affect issues. Based on the findings, several recommendations are raised as a reference for future researchers, educators, and policy makers.

Visualization and Semantic Labeling of Mood States Based on Time-Series Features of Eye Gaze and Facial Expressions by Unsupervised Learning

This study is intended to develop a stress measurement and visualization system for stress management in terms of simplicity and reliability. We present a classification and visualization method of mood states based on unsupervised machine learning (ML) algorithms. Our proposed method attempts to examine the relation between mood states and extracted categories in human communication from facial expressions, gaze distribution area and density, and rapid eye movements, defined as saccades. Using a psychological check sheet and a communication video with an interlocutor, an original benchmark dataset was obtained from 20 subjects (10 male, 10 female) in their 20s for four or eight weeks at weekly intervals. We used a Profile of Mood States Second edition (POMS2) psychological check sheet to extract total mood disturbance (TMD) and friendliness (F). These two indicators were classified into five categories using self-organizing maps (SOM) and U-Matrix. The relation between gaze and facial expressions was analyzed from the extracted five categories. Data from subjects in the positive categories were found to have a positive correlation with the concentrated distributions of gaze and saccades. Regarding facial expressions, the subjects showed a constant expression time of intentional smiles. By contrast, subjects in negative categories experienced a time difference in intentional smiles. Moreover, three comparative experiment results demonstrated that the feature addition of gaze and facial expressions to TMD and F clarified category boundaries obtained from U-Matrix. We verify that the use of SOM and its two variants is the best combination for the visualization of mood states.

Human Development Index Is Associated with COVID-19 Case Fatality Rate in Brazil: An Ecological Study

The Human Development Index measures a region's development and is a step for development debate beyond the traditional, economic perspective. It can also determine the success of a country's response to the COVID-19 pandemic, mainly affecting the case fatality rate among severe cases of SARS-CoV-2 infection. We aimed to associate the Human Development Index with the case fatality rate due to COVID-19 in each Brazilian state and the Federal District, taking into account comorbidities and the need for invasive mechanical ventilation. We also evaluated the influence of the GINI index, number of intensive care unit beds, and occupied households in subnormal clusters on the case fatality rate. We performed an ecological study including two populations: COVID-19 individuals that did not require the mechanical ventilation protocol; and COVID-19 individuals under invasive mechanical ventilation. We performed a Pearson correlation test and a univariate linear regression analysis on the relationship between Human Development Index, Human Development Index-Education Level, Human Development Index-Life Expectancy, and Human Development Index-Gross National Income per capita and COVID-19 deaths. The same analyses were performed using the other markers. We grouped the patients with COVID-19 according to comorbidities and the need for invasive mechanical ventilation. Alpha = 0.05. We included 848,501 COVID-19 individuals, out of which 153,710 needed invasive mechanical ventilation and 314,164 died, and 280,533 COVID-19 individuals without comorbidity, out of which 33,312 needed invasive mechanical ventilation and 73,723 died. We observed a low negative Pearson correlation between the Human Development Index and death and a moderate negative Pearson correlation between the Human Development Index and deaths of individuals on invasive mechanical ventilation, with or without comorbidity. The univariate linear analysis showed the case fatality rate depends on at least 20-40% of the Human Development Index. In Brazil, regions with a low Human Development Index demonstrated a higher case fatality rate due to COVID-19, mainly in individuals who needed invasive mechanical ventilation, than regions with a higher Human Development Index. Although other indexes studied, such as intensive care unit beds and GINI, were also associated with the COVID-19 case fatality rate, they were not as relevant as the Human Development Index. Brazil is a vast territory comprising cultural, social, and economic diversity, which mirrors the diversity of the Human Development Index. Brazil is a model nation for the study of the Human Development Index's influence on aspects of the COVID-19 pandemic, such as its impact on the case fatality rate.

Distinct weather conditions and human mobility impacts on the SARS-CoV-2 outbreak in Colombia: Application of an artificial neural network approach

The coronavirus disease 2019 (COVID-19) is still spreading fast in several tropical countries after more than one year of pandemic. In this scenario, the effects of weather conditions that can influence the spread of the virus are not clearly understood. This study aimed to analyse the influence of meteorological (temperature, wind speed, humidity and specific enthalpy) and human mobility variables in six cities (Barranquilla, Bogota, Cali, Cartagena, Leticia and Medellin) from different biomes in Colombia on the coronavirus dissemination from March 25, 2020, to January 15, 2021. Rank correlation tests and a neural network named self-organising map (SOM) were used to investigate similarities in the dynamics of the disease in the cities and check possible relationships among the variables. Two periods were analysed (quarantine and post-quarantine) for all cities together and individually. The data were classified in seven groups based on city, date and biome using SOM. The virus transmission was most affected by mobility variables, especially in the post-quarantine. The meteorological variables presented different behaviours on the virus transmission in different biogeographical regions. The wind speed was one of the factors connected with the highest contamination rate recorded in Leticia. The highest new daily cases were recorded in Bogota where cold/dry conditions (average temperature <14 °C and absolute humidity >9 g/m³) favoured the contagions. In contrast, Barranquilla, Cartagena and Leticia presented an opposite trend, especially with the absolute humidity >22 g/m³. The results support the implementation of better local control measures based on the particularities of tropical regions.

Recent Applications of Mixture Designs in Beverages, Foods, and Pharmaceutical Health: A Systematic Review and Meta-Analysis

Design of Experiments (DoE) is a statistical tool used to plan and optimize experiments and is seen as a quality technology to achieve products excellence. Among the experimental designs (EDs), the mixture designs (MDs) stand out, being widely applied to improve conditions for processing, developing, or formulating novel products. This review aims to provide useful updated information on the capacity and diversity of MDs applications for the industry and scientific community in the areas of food, beverage, and pharmaceutical health. Recent works were selected following the Preferred Reporting Items for Systematic Review and Meta-Analyses statement (PRISMA) flow diagram. Data analysis was performed by self-organizing map (SOM) to check and understand which fields of application/countries/continents are using MDs. Overall, the SOM indicated that Brazil presented the largest number of works using MDs. Among the continents, America and Asia showed a predominance in applications with the same amount of work. Comparing the MDs application areas, the analysis indicated that works are prevalent in food and beverage science in the American continent, while in Asia, health science prevails. MDs were more used to develop functional/nutraceutical products and the formulation of drugs for several diseases. However, we briefly describe some promising research fields in that MDs can still be employed.

The application of artificial intelligence and data integration in COVID-19 studies: a scoping review

OBJECTIVE

To summarize how artificial intelligence (AI) is being applied in COVID-19 research and determine whether these AI applications integrated heterogenous data from different sources for modeling.

MATERIALS AND METHODS

We searched 2 major COVID-19 literature databases, the National Institutes of Health's LitCovid and the World Health Organization's COVID-19 database on March 9, 2021. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline, 2 reviewers independently reviewed all the articles in 2 rounds of screening.

RESULTS

In the 794 studies included in the final qualitative analysis, we identified 7 key COVID-19 research areas in which AI was applied, including disease forecasting, medical imaging-based diagnosis and prognosis, early detection and prognosis (non-imaging), drug repurposing and early drug discovery, social media data analysis, genomic, transcriptomic, and proteomic data analysis, and other COVID-19 research topics. We also found that there was a lack of heterogenous data integration in these AI applications.

DISCUSSION

Risk factors relevant to COVID-19 outcomes exist in heterogeneous data sources, including electronic health records, surveillance systems, sociodemographic datasets, and many more. However, most AI applications in COVID-19 research adopted a single-sourced approach that could omit important risk factors and thus lead to biased algorithms. Integrating heterogeneous data for modeling will help realize the full potential of AI algorithms, improve precision, and reduce bias.

CONCLUSION

There is a lack of data integration in the AI applications in COVID-19 research and a need for a multilevel AI framework that supports the analysis of heterogeneous data from different sources.

An overview of research of essential oils by self-organizing maps: A novel approach for meta-analysis study

Essential oils (EOs) are commercially important products, sources of compounds with antioxidant and antimicrobial activities considered indispensable for several fields, such as the food industry, cosmetics, perfumes, pharmaceuticals, sanitary and agricultural industries. In this context, this systematic review and meta-analysis, a novel approach will be presented using chemometric tools to verify and recognize patterns of antioxidant, antibacterial, and antifungal activities of EOs according to their geographic, botanical, chemical, and microbiological distribution. Scientific papers were selected following the Preferred Reporting Items for Systematic Review and Meta-Analyses statement flow diagram, and the data were evaluated by the self-organizing map and hierarchical cluster analysis. Overall, this novel approach allowed us to draw an overview of antioxidants and antimicrobials activities of EOs reported in 2019, through 585 articles evaluated, obtaining a dataset with more than 10,000 data, distributed in more than 80 countries, 290 plant genera, 150 chemical compounds, 30 genera of bacteria, and 10 genera of fungi. The networks for geographic, botanical, chemical, and microbiological distribution indicated that Brazil, Asia, the botanical genus Thymus, species Thymus vulgaris L. "thyme," the Lamiaceae family, limonene, and the oxygenated monoterpene class were the most representative in the dataset, while the species Escherichia coli and Candida albicans were the most used to assess the antimicrobial activity of EOs. This work can be seen as a guide for the processing of metadata using a novel approach with non-conventional statistical methods. However, this preliminary approach with EOs can be extended to other sources or areas of food science.

Dynamic Network Analysis of COVID-19 with a Latent Pandemic Space Model

In this paper, we propose a latent pandemic space modeling approach for analyzing coronavirus disease 2019 (COVID-19) pandemic data. We developed a pandemic space concept that locates different regions so that their connections can be quantified according to the distances between them. A main feature of the pandemic space is to allow visualization of the pandemic status over time through the connectedness between regions. We applied the latent pandemic space model to dynamic pandemic networks constructed using data of confirmed cases of COVID-19 in 164 countries. We observed the ways in which pandemic risk evolves by tracing changes in the locations of countries within the pandemic space. Empirical results gained through this pandemic space analysis can be used to quantify the effectiveness of lockdowns, travel restrictions, and other measures in regard to reducing transmission risk across countries.

The Spread of the COVID-19 Outbreak in Brazil: An Overview by Kohonen Self-Organizing Map Networks

Background and objective: In the current pandemic scenario, data mining tools are fundamental to evaluate the measures adopted to contain the spread of COVID-19. In this study, unsupervised neural networks of the Self-Organizing Maps (SOM) type were used to assess the spatial and temporal spread of COVID-19 in Brazil, according to the number of cases and deaths in regions, states, and cities. Materials and methods: The SOM applied in this context does not evaluate which measures applied have helped contain the spread of the disease, but these datasets represent the repercussions of the country's measures, which were implemented to contain the virus' spread. Results: This approach demonstrated that the spread of the disease in Brazil does not have a standard behavior, changing according to the region, state, or city. The analyses showed that cities and states in the north and northeast regions of the country were the most affected by the disease, with the highest number of cases and deaths registered per 100,000 inhabitants. Conclusions: The SOM clustering was able to spatially group cities, states, and regions according to their coronavirus cases, with similar behavior. Thus, it is possible to benefit from the use of similar strategies to deal with the virus' spread in these cities, states, and regions.