ABSTRACT
This article presents a multi-stage guided technical project coding Python scripts for utilizing Amazon Web Services (AWS) to work with a document-store database called DynamoDB. Students doing this project should have taken an introductory programming class (ideally in Python) and a database class to have experience with Python coding and database manipulation/querying in a relational environment. Students learn new data formats (Python dictionaries, JSON text data, keyvalue storage structures) and learn how to transform data from one format to another. They also gain experience with data visualization. The project was first carried out in a business intelligence (BI) course during Spring 2020 semester in the midst of COVID and included video tutorials. Since then, it has been refined and used each semester the BI course is taught.
ABSTRACT
Intervention policies against COVID-19 have caused large-scale disruptions globally, and led to a series of pattern changes in the power system operation. Analyzing these pandemic-induced patterns is imperative to identify the potential risks and impacts of this extreme event. For this purpose, we developed an open-access data hub (COVID-EMDA+), an open-source toolbox (CoVEMDA), and a few evaluation methods to explore what the U.S. power systems are experiencing during COVID-19. These resources could be broadly used for research, public policy, and educational purposes. Technically, our data hub harmonizes a variety of raw data such as generation mix, demand profiles, electricity price, weather observations, mobility, confirmed cases and deaths. Typical methods are reformulated and standardized in our toolbox, including baseline estimation, regression analysis, and scientific visualization. Here the fluctuation index and probabilistic baseline are proposed for the first time to consider data fluctuation and estimation uncertainty. Furthermore, we conduct three empirical studies on the U.S. power systems, and share new solutions and findings to address several issues of public concerns. This conveys a more complete picture of the COVID-19 impact and also opens up several attractive topics for future work. Python, Matlab source codes, and user manuals are all publicly shared on a Github repository.
ABSTRACT
Complexity, heterogeneity, schemaless-ness, data visualization, and extraction of consistent knowledge from Big Data are the biggest challenges in NoSQL databases. This paper presents a general semantic NoSQL Application Program Interface that integrates and converts NoSQL databases to semantic representation. The generated knowledge base is suitable for visualization and knowledge extraction from different Big Data sources. The authors use a case study of the COVID-19 pandemic prediction and other weather occurrences in various parts of the world to illustrate the suggested API. The Authors find a correlation between COVID-19 spread and deteriorating weather. According to the experimental findings, the API's performance is enough for heterogeneous Big Data.
ABSTRACT
Introduction: Medsafe, the New Zealand Pharmacovigilance Centre (NZPhVC) and the Covid Vaccine Immunisation Programme (CVIP) created a vaccine pharmacovigilance strategy. We report pros and cons of the strategy. Objective: We aimed to use existing systems and expertise but leverage new technologies to manage an expected increase in reporting to support signal detection activities and requests for data. [1] Methods: A new reporting form was included in the new Covid Immunisation Register (CIR) so events occurring at the vaccination centre could then be easily reported into a database. A new on-line COVID-19 vaccine reporting form was constructed which populated the database. This form incorporated a list of common vaccine side effects and AESIs that could be selected by the reporter [2]. A new COVID-19 vaccine database for storing reports was built in the same electronic environment as the CIR. This facilitated data linkage, report triaging, dedicated access and analysis. Importantly this database was accessible remotely through secure VPN. Qlik Sense data visualisation and analytics were used to report on data incorporating daily data transfers to allow tracking of reporting and daily updates to support policy and communications. Results: From start to 29 April 2022, 62,427 cases of adverse events were received in New Zealand, more than 12 times the normally expected number. The average reporting rate was 5.7 reports per 1000 vaccinations. The CIR reporting option was used over 15,000 times and the online form was used more than 28,000 times by consumers. The CIR linkage to the AEFI database allowed duplicate and fake reports to be quickly detected and ensured vaccine administration data was high quality. Rapid processing of the massive increase in reports was possible, but there were issues due to limitations of the database and the volume of reports received. Event tick boxes in the reporting form had advantages for reporters but impacted the granularity of the data. The AESI event tick boxes were undesirable. These were often selected by vaccinees based on self diagnosis, in error and not supplemented by supporting evidence. This resulted in an increase in work to follow up these reports. The Qlik app facilitated rapidly identifying trends in the data and AEFI report updates. Conclusion: The system enabled close to real time data for signal analysis, public reassurance and communication. Data-linkage supported accurate determination of data. However, an increase in staffwas still required and it was impossible to review all reported cases for accuracy or need for further information.
ABSTRACT
Environmental problems due to human activities such as deforestation, urbanisation, and large scale intensive farming are some of the major factors behind the rapid spread of many infectious diseases. This in turn poses significant challenges not only in as regards providing adequate healthcare, but also in supporting healthcare workers, medical researchers, policy makers, and others involved in managing infectious diseases. These challenges include surveillance, tracking of infections, communication of public health knowledge and promotion of behavioural change. Behind these challenges lies a complex set of factors which include not only biomedical and population health determinants but also environmental, climatic, geographic, and socioeconomic variables. While there is broad agreement that these factors are best understood when considered in conjunction, aggregating and presenting diverse information sources requires effective information systems, software tools, and data visualisation. In this article, we argue that interactive maps, which couple geographical information systems and advanced information visualisation techniques, provide a suitable unifying framework for coordinating these tasks. Therefore, we examine how interactive maps can support spatial epidemiological visualisation and modelling involving distributed and dynamic data sources and incorporating temporal aspects of disease spread. Combining spatial and temporal aspects can be crucial in such applications. We discuss these issues in the context of support for disease surveillance in remote regions, utilising tools that facilitate distributed data collection and enable multidisciplinary collaboration, while also providing support for simulation and data analysis. We show that interactive maps deployed on a combination of mobile devices and large screens can provide effective means for collection, sharing, and analysis of health data.
ABSTRACT
The Environmental Health Tracking Podcast Series features four different environmental health departments and their tracking programs. Each podcast shares the history, challenges, and successes of the program and goals for the future. Topics include how the health department increased their tracking data content and awareness, how tracking data is used to address health inequities, evaluation strategies of the program, and different examples of data visualization.
ABSTRACT
The novel coronavirus disease (COVID-19) has currently affected millions of people, claiming more than 4,000,000 lives all over the world. Several dashboards have been created to analyze the present situation and get a better grasp of the current status of COVID-19. As the situation unfolded, infections caused by species of fungi, Mucormycosis (commonly called black fungus), have affected patients treated for COVID-19. Therefore, to facilitate information and to create awareness, it would be better to have a dashboard that display trends and data on COVID-19 and associated related diseases. In the proposed work, a dashboard has been created to visualize how COVID-19 epidemic has an impact in the global scenario. With the present work, the spread of diseases associated with COVID-19 (fungi variants) can be visualized. The data visualization is performed using Python. The tool kits and packages used for this purpose is Dash by Plotly. The acquired data is classified and filtered with interesting criteria in the ranging process stage. Using specific tools, data representations like line chart, bubble map, heat map, choropleths, tree map and, folium map are plotted to visualize the data.
ABSTRACT
The scholarly output of the new coronavirus research has been proliferating. During five months, an amount of 14,588 scientific publications about nCoV-2 and COVID-19 has been generated intensively (as indexed in Scopus on 31 May 2020). Such a knowledge outburst has created ample interest in understanding the research landscape of this newly configured area. This paper demonstrates on scientometric dimensions of the novel coronavirus (2019-nCov) research using quantifiable characteristics of the publication dataset. Findings reveal that the rate of publication growth (1600%) is very significant to a synergic response of the researchers to combat with the most extended sequence of an RNA virus. Indeed their response has geared up to an average of 100 articles per day. Many scholarly publishers have disclosed their preprint servers to make the publications available immediately, even by enabling Open Access. The scientific contents have published in more than 500 journals from 240 academic publishers. While the top-ten publishers occupied almost 70% of the articles, then about 25% of the studies were sponsored by 300 funding agencies. Among the notable journals Lancet, Nature, BMJ, JAMA, JMV, and NEJM are prominent. Findings also reveal that majority of the contributions have occurred in Medical Science, focusing on virology, immunology, epidemiology, pharmacology, public health, critical care, and emergency medicine. However, the closely associated terms are virus transmission, infection control, asymptomatic, quarantine, pneumonia, human, disease severity, clinical trials, viral pathogenesis, pandemic, risk, and mortality. The study suggests that academic hubs are located mostly in the USA, China, Italy, and the UK. Among the productive institutions; Huazhong Univ (China), Tongji Med. College (China), Harvard Med. School (USA), Univ of Milan (Italy), INSERM (France), UCL (UK) are outstanding. The G7 countries together produced 50% of the global research output on nCov-2. It also noted an encouraging trend of collaborative research across many countries and disciplines, where the values of CI (6.46), DC (0.79), and CC (0.59) are very significant. It examines the geographical diversity of the collaborating authors, thereby visualized their linkages via co-authorship occurrences. Finally, it analyzed the publications' impact to showcase the most influential contributions of the new coronavirus research.