Individual Factors Associated With COVID-19 Infection: A Machine Learning Study

COVID-19 Reinfections in the City of São Paulo, Brazil: Prevalence and Socioeconomic Factors

Background

Identifying those most susceptible to COVID-19 reinfection and understanding the associated characteristics is essential for developing effective prevention and control strategies. We aimed to evaluate the influence of social determinants, regional disparities, and variant evolution on COVID-19 reinfection rates.

Methods

We conducted a retrospective cohort study in São Paulo, Brazil, involving laboratory-confirmed COVID-19 patients. Reinfection was defined as a subsequent positive COVID-19 test at least 90 days after the previous confirmed infection. We assessed socioeconomic indicators, demographic factors, and spatial correlations. Reinfection rates were analyzed across different variants and subvariants.

Results

Among 73 741 patients, 5626 (7.6%) experienced reinfections, with most (95.0%) having 1 reinfection. Reinfection rates increased significantly during the Omicron period, particularly with subvariants BA.1, BA.2/BA.4, BA.5, and XBB/XBB.1.5/XBB.1.16. The highest rates were seen in patients initially infected during the BA.2/BA.4 and BA.5 periods, who were later reinfected by XBB subvariants. Socioeconomic indicators, including lower Human Development Index, higher proportions of informal settlements, and lower employment rates, were significantly associated with higher reinfection rates. Geospatial analysis showed significant clustering of reinfections in areas with higher social vulnerability.

Conclusions

COVID-19 reinfection rates were heavily influenced by socioeconomic disparities and variant-specific factors. Regions with lower Human Development Index and worse socioeconomic conditions experienced higher reinfection rates. These findings highlight the need for targeted public health interventions focused on vulnerable populations, particularly in areas with greater social inequality. As new variants continue to emerge, ongoing surveillance and adaptive public health strategies will be critical to reducing reinfections.

AutoML-Driven Insights into Patient Outcomes and Emergency Care During Romania's First Wave of COVID-19

BACKGROUND

The COVID-19 pandemic severely impacted healthcare systems, affecting patient outcomes and resource allocation. This study applied automated machine learning (AutoML) to analyze key health outputs, such as discharge conditions, mortality, and COVID-19 cases, with the goal of improving responses to future crises.

METHODS

AutoML was used to train and validate models on an ICD-10 dataset covering the first wave of COVID-19 in Romania (January-September 2020).

RESULTS

For discharge outcomes, Light Gradient Boosted models achieved an F1 score of 0.9644, while for mortality 0.7545 was reached. A Generalized Linear Model blender achieved an F1 score of 0.9884 for "acute or emergency" cases, and an average blender reached 0.923 for COVID-19 cases. Older age, specific hospitals, and oncology wards were less associated with improved recovery rates, while mortality was linked to abnormal lab results and cardiovascular/respiratory diseases. Patients admitted without referral, or patients in hospitals in the central region and the capital region of Romania were more likely to be acute cases. Finally, counties such as Argeş (South-Muntenia) and Brașov (Center) showed higher COVID-19 infection rates regardless of age.

CONCLUSIONS

AutoML provided valuable insights into patient outcomes, highlighting variations in care and the need for targeted health strategies for both COVID-19 and other health challenges.

Identification of Biomarkers and Molecular Pathways Implicated in Smoking and COVID-19 Associated Lung Cancer Using Bioinformatics and Machine Learning Approaches

Lung cancer (LC) is a significant global health issue, with smoking as the most common cause. Recent epidemiological studies have suggested that individuals who smoke are more susceptible to COVID-19. In this study, we aimed to investigate the influence of smoking and COVID-19 on LC using bioinformatics and machine learning approaches. We compared the differentially expressed genes (DEGs) between LC, smoking, and COVID-19 datasets and identified 26 down-regulated and 37 up-regulated genes shared between LC and smoking, and 7 down-regulated and 6 up-regulated genes shared between LC and COVID-19. Integration of these datasets resulted in the identification of ten hub genes (SLC22A18, CHAC1, ROBO4, TEK, NOTCH4, CD24, CD34, SOX2, PITX2, and GMDS) from protein-protein interaction network analysis. The WGCNA R package was used to construct correlation network analyses for these shared genes, aiming to investigate the relationships among them. Furthermore, we also examined the correlation of these genes with patient outcomes through survival curve analyses. The gene ontology and pathway analyses were performed to find out the potential therapeutic targets for LC in smoking and COVID-19 patients. Moreover, machine learning algorithms were applied to the TCGA RNAseq data of LC to assess the performance of these common genes and ten hub genes, demonstrating high performances. The identified hub genes and molecular pathways can be utilized for the development of potential therapeutic targets for smoking and COVID-19-associated LC.

Superspreading of SARS-CoV-2: a systematic review and meta-analysis of event attack rates and individual transmission patterns

SARS-CoV-2 superspreading occurs when transmission is highly efficient and/or an individual infects many others, contributing to rapid spread. To better quantify heterogeneity in SARS-CoV-2 transmission, particularly superspreading, we performed a systematic review of transmission events with data on secondary attack rates or contact tracing of individual index cases published before September 2021 prior to the emergence of variants of concern and widespread vaccination. We reviewed 592 distinct events and 9,883 index cases from 491 papers. A meta-analysis of secondary attack rates identified substantial heterogeneity across 12 chosen event types/settings, with the highest transmission (25-35%) in co-living situations including households, nursing homes, and other congregate housing. Among index cases, 67% reported zero secondary cases and only 3% (287) infected >5 secondary cases ("superspreaders"). Index case demographic data were limited, with only 55% of individuals reporting age, sex, symptoms, real-time polymerase chain reaction (PCR) cycle threshold values, or total contacts. With the data available, we identified a higher percentage of superspreaders among symptomatic individuals, individuals aged 49-64 years, and individuals with over 100 total contacts. Addressing gaps in the literature regarding transmission events and contact tracing is needed to properly explain the heterogeneity in transmission and facilitate control efforts for SARS-CoV-2 and other infections.

An adaptive data-driven architecture for mental health care applications

Background

In the current era of rapid technological innovation, our lives are becoming more closely intertwined with digital systems. Consequently, every human action generates a valuable repository of digital data. In this context, data-driven architectures are pivotal for organizing, manipulating, and presenting data to facilitate positive computing through ensemble machine learning models. Moreover, the COVID-19 pandemic underscored a substantial need for a flexible mental health care architecture. This architecture, inclusive of machine learning predictive models, has the potential to benefit a larger population by identifying individuals at a heightened risk of developing various mental disorders.

Objective

Therefore, this research aims to create a flexible mental health care architecture that leverages data-driven methodologies and ensemble machine learning models. The objective is to proficiently structure, process, and present data for positive computing. The adaptive data-driven architecture facilitates customized interventions for diverse mental disorders, fostering positive computing. Consequently, improved mental health care outcomes and enhanced accessibility for individuals with varied mental health conditions are anticipated.

Method

Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, the researchers conducted a systematic literature review in databases indexed in Web of Science to identify the existing strengths and limitations of software architecture relevant to our adaptive design. The systematic review was registered in PROSPERO (CRD42023444661). Additionally, a mapping process was employed to derive essential paradigms serving as the foundation for the research architectural design. To validate the architecture based on its features, professional experts utilized a Likert scale.

Results

Through the review, the authors identified six fundamental paradigms crucial for designing architecture. Leveraging these paradigms, the authors crafted an adaptive data-driven architecture, subsequently validated by professional experts. The validation resulted in a mean score exceeding four for each evaluated feature, confirming the architecture's effectiveness. To further assess the architecture's practical application, a prototype architecture for predicting pandemic anxiety was developed.

A large-scale machine learning study of sociodemographic factors contributing to COVID-19 severity

Understanding sociodemographic factors behind COVID-19 severity relates to significant methodological difficulties, such as differences in testing policies and epidemics phase, as well as a large number of predictors that can potentially contribute to severity. To account for these difficulties, we assemble 115 predictors for more than 3,000 US counties and employ a well-defined COVID-19 severity measure derived from epidemiological dynamics modeling. We then use a number of advanced feature selection techniques from machine learning to determine which of these predictors significantly impact the disease severity. We obtain a surprisingly simple result, where only two variables are clearly and robustly selected-population density and proportion of African Americans. Possible causes behind this result are discussed. We argue that the approach may be useful whenever significant determinants of disease progression over diverse geographic regions should be selected from a large number of potentially important factors.