Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for coronavirus disease 2019 (COVID-19), has had an unprecedented effect, especially among under-resourced minority communities. Surveillance of those at high risk is critical for preventing and controlling the pandemic. We must better understand the relationships between COVID-19-related cases or deaths and characteristics in our most vulnerable population that put them at risk to target COVID-19 prevention and management efforts. Population characteristics strongly related to United States (US) county-level data on COVID-19 cases and deaths during all stages of the pandemic were identified from the onset of the epidemic and included county-level socio-demographic and comorbidities data, as well as daily meteorological modeled observation data from the North American Regional Reanalysis (NARR), and the NARR high spatial resolution model to assess the environment. Advanced machine learning (ML) approaches were used to identify outbreaks (geographic clusters of COVID-19) and included spatiotemporal risk factors and COVID-19 vaccination efforts, especially among vulnerable and underserved communities. COVID-19 outcomes were found to be negatively associated with the number of people vaccinated and positively associated with age, the prevalence of cardiovascular disease, diabetes, and the minority population. There was also a strong positive correlation between unauthorized immigrants and the prevalence of COVID-19 cases and deaths. Meteorological variables were also investigated, but correlations with COVID-19 were relatively weak. Our findings suggest that COVID-19 has had a disproportionate impact across the US population among vulnerable and minority communities. Findings also emphasize the importance of vaccinations and tailored public health initiatives (e.g., mask mandates, vaccination) to reduce the spread of COVID-19 and the number of COVID-19 related deaths across all populations.

COVID-19 has harshly impacted communities globally. This study provides relevant information for creating equitable policy interventions to combat the spread of COVID-19. This study aims to predict the knowledge, attitude, and practice (KAP) of the COVID-19 pandemic at a global level to determine control measures and psychosocial problems. A cross-sectional survey was conducted from July to October 2020 using an online questionnaire. Questionnaires were initially distributed to academicians worldwide. These participants distributed the survey among their social, professional, and personal groups. Responses were collected and analyzed from 67 countries, with a sample size of 3031. Finally, based on the number of respondents, eight countries, including Bangladesh, China, Japan, Malaysia, Mexico, Pakistan, the United States, and Zambia were rigorously analyzed. Specifically, questionnaire responses related to COVID-19 accessibility, behavior, knowledge, opinion, psychological health, and susceptibility were collected and analyzed. As per our analysis, age groups were found to be a primary determinant of behavior, knowledge, opinion, psychological health, and susceptibility scores. Gender was the second most influential determinant for all metrics except information about COVID-19 accessibility, for which education was the second most important determinant. Respondent profession was the third most important metric for all scores. Our findings suggest that health authorities must promote health educations, implement related policies to disseminate COVID-19-awareness that can prevent and control the spread of COVID-19 infection.

Many countries wish to achieve digital transformation, especially during the COVID-19 pandemic. The digital skills demand is changing fast. The time-series online job portal data for the ICT industry in Bangladesh provides an opportunity to analyze high demand job titles and skills over time. These time-series data address the question of the speed of changes in job titles and skills and responsiveness of computer science and engineering (CSE) curricula. This study gathers online job portal data of the ICT industry in Bangladesh from 2016 to 2021. Natural language processing is used to group similar skills and job titles following the O*NET Online taxonomy. In addition to the descriptive statistics, the statistical significance test and correlation analysis are conducted. The analysis could identify high demand ICT job titles (Software Developers, Computer System Engineers/Architects, Web Developers, Project Management Specialists) and skills (API, Database, JavaScript) but Computer System Engineer/Architect job titles and API skills are increasing fast. The shift from networking to JavaScript and UI Design is also noteworthy after COVID-19. The preliminary curricula analysis suggests the responsiveness of the CSE program, but online job portal data analysis might provide opportunities for developing unique CSE specialization, courses and curricula.

Delineating the mechanisms underlying cell state changes is key to gaining insights into organismal development and disease prognosis. Various methods have been proposed to study cellular differentiation and cell fate specification. However, they either do not incorporate temporal information or do not consider the vital role of intercellular communication in cellular differentiation and cell fate determination. Furthermore, many of these methods lack interpretability, making it difficult to identify the critical genes and pathways that influence the differentiation process. Here we propose CC-Tempo, a cell-cell communication-aware model of cellular dynamics that leverages intercellular communication scores and can help identify important genes and pathways crucial for different stages of differentiation in various lineages. While previous studies have indicated that scRNA-seq data alone may not suffice for accurately predicting cell fates, CC-Tempo demonstrates that incorporating intercellular communication significantly enhances the performance of such models. CC-Tempo can predict the significance of genes and pathways at different stages of the differentiation process. By perturbing these genes in silico, CC-Tempo reveals their efficacy for manipulating cell fate, which can be crucial for defining efficient reprogramming factors.