Visual Exploratory Data Analysis of the Covid-19 Pandemic in Nigeria: Two Years after the Outbreak

The outbreak of the coronavirus disease in Nigeria and all over the world in 2019/2020 caused havoc on the world's economy and put a strain on global healthcare facilities and personnel. It also threw up many opportunities to improve processes using artificial intelligence techniques like big data analytics and business intelligence. The need to speedily make decisions that could have far-reaching effects is prompting the boom in data analytics which is achieved via exploratory data analysis (EDA) to see trends, patterns, and relationships in the data. Today, big data analytics is revolutionizing processes and helping improve productivity and decision-making capabilities in all aspects of life. The large amount of heterogeneous and, in most cases, opaque data now available has made it possible for researchers and businesses of all sizes to effectively deploy data analytics to gain action-oriented insights into various problems in real time. In this paper, we deployed Microsoft Excel and Python to perform EDA of the covid-19 pandemic data in Nigeria and presented our results via visualizations and a dashboard using Tableau. The dataset is from the Nigeria Centre for Disease Control (NCDC) recorded between February 28th, 2020, and July 19th, 2022. This paper aims to follow the data and visually show the trends over the past 2 years and also show the powerful capabilities of these data analytics tools and techniques. Furthermore, our findings contribute to the current literature on Covid-19 research by showcasing how the virus has progressed in Nigeria over time and the insights thus far. © 2022 IEEE.

The effectiveness analysis of selected IT tools for predictions of the Covid-19 pandemic

The article presents the problem of the complexity of prediction and the analysis of the effectiveness of selected IT tools in the example of the Covid-19 pandemic data in Poland. The study used a variety of tools and methods to obtain predictions of extinct infections and mortality for each wave of the Covid-19 pandemic. The results are presented for the 4th wave with a detailed description of selected models and methods implemented in the prognostic package of the statistical programming language R, as well as in the Statistica and Microsoft Excel programs. Naive methods, regression models, exponential smoothing methods (including ETS models), ARIMA models, and the method of artificial intelligence - autoregressive models built by neural networks (NNAR) were used. Detailed analysis was performed and the results for each of these methods were compared. © 2022 Polish Information Processing Society.

An Examination of Professor-Student Interactions, Stem Learning Challenges, and Student Adaptation Decisions During Covid-19 Pandemic

The COVID-19 pandemic transformed STEM learning environments across U.S. institutions. However, the impact of this pandemic on learning and decision-making in students are yet to be fully understood. It is important to gain insights into student experiences during COVID-19 pandemic so that student and institutional resiliency can be improved during future pandemics. This research is part of a larger nationwide inductive research project with the purpose of developing theories to explain the learning experiences and decisions of undergraduate STEM students during the COVID-19 pandemic. A mixed-methods approach with purposive sampling was utilized to enroll 63 undergraduate STEM students from six U.S institutions. Data was collected through recruitment surveys, academic transcripts, and interviews. One-hour ZOOM interviews, gave research participants the opportunity to narrate their salient STEM learning experiences during the spring 2020 semester. Data was analyzed using the NVivo qualitative analysis software and Microsoft Excel for coding, categorizing, memo-ing, constant comparative analysis, and theme development. Also, Microsoft Excel was used to analyze demographic data from recruitment surveys and GPA data from the academic transcripts. Results from the analysis of 30 coded interview transcripts revealed an emergent theme - Professor-Student Interactions Impact Learning and Adaptation Decisions. The three key categories of this theme are: Professor-Student Interactions and Learning Challenges;Adaptation Decisions;and STEM Performance. The seven categories of Professor-Student Interactions are coded as: Online Instructional Delivery Methods;Professor Caring Attitudes;Professor Leniency;Professor Availability;Student Workloads;Professor Technology Proficiency;and Professor Teaching Resources. Positive professor-student interactions improve student learning experiences. Negative professor-student interactions worsen student learning challenges and are coded as: Illusion of Time, Procrastination;Lack of Focus;Challenge of Asking Questions;Poor Understanding;Poor Quality Assignments;Poor Intermediate Grades;Stresses;and Lowered Motivation. While most research participants experienced high stresses, a few of them experienced low or no stresses. To minimize the impact of COVID-related learning challenges on their STEM learning and performance, research participants made effective adaptation decisions coded as: Refined Scheduling;Alternate Learning Resources;Professor Office Hours;Teaching Assistants;Peer Collaboration;Relaxation Strategies;and Pass/Fail Options. Compared to the fall 2019 GPAs, the improved spring 2020 GPAs of research participants may be partially attributed to professor leniency, pass/fail option, and cheating. Findings indicate that while STEM professors were adjusting to COVID-modified teaching and learning environments, many STEM students were developing a sense of self-discipline, self-teaching, and independence. They relied on both professor and non-professor generated resources to improve their own STEM learning and performance. Lessons learned and best practices for improved professor-student interactions and student adaptation decisions are discussed for potential replication in STEM communities for improved adaptability and resiliency during future pandemics. Future research will focus on quantifying the long-term effect of the COVID-19 pandemic on STEM performance. © American Society for Engineering Education, 2021

Supercomputing Enabled Deployable Analytics for Disaster Response

First responders and other forward deployed essential workers can benefit from advanced analytics. Limited network access and software security requirements prevent the usage of standard cloud based microservice analytic platforms that are typically used in industry. One solution is to precompute a wide range of analytics as files that can be used with standard preinstalled software that does not require network access or additional software and can run on a wide range of legacy hardware. In response to the COVID-19 pandemic, this approach was tested for providing geo-spatial census data to allow quick analysis of demographic data for better responding to emergencies. These data were processed using the MIT SuperCloud to create several thousand Google Earth and Microsoft Excel files representative of many advanced analytics. The fast mapping of census data using Google Earth and Microsoft Excel has the potential to give emergency responders a powerful tool to improve emergency preparedness. Our approach displays relevant census data (total population, population under 15, population over 65, median age) per census block, sorted by county, through a Microsoft Excel spreadsheet (xlsx file) and Google Earth map (kml file). The spreadsheet interface includes features that allow users to convert between different longitude and latitude coordinate units. For the Google Earth files, a variety of absolute and relative colors maps of population density have been explored to provide an intuitive and meaningful interface. Using several hundred cores on the MIT SuperCloud, new analytics can be generated in a few minutes. © 2021 IEEE.