Analysis of Data Visualization in Pandemic Situation (original) (raw)

Data visualization in the time of coronavirus

Strategic Design Research Journal, 2021

Currently, we observe a proliferation of data visualizations about Covid-19 in the media, which makes it a convenient time to study the topic from the perspective of different disciplines, including information design and mathematics. If, on the one hand, the abundance of such pandemic representations would already be a legitimate reason to address the issue, on the other hand, it is not the central motivation of the present discussion. The uniqueness of the epidemiological phenomenon that we are experiencing highlights new aspects regarding the production and use of data visualizations, one of which is its diversification beyond counting and visual representation of events related to the virus spread. In this sense, the article discusses, through the analysis of examples, three different approaches for this type of schematic representation, namely: visualization of hypothetical data, visualizations based on secondary data, and visualization for social criticism and self-reflection. Ultimately, we can argue that design contributes to the production of data visualizations that can help people to understand the causes and implications involved in the new coronavirus and encourage civic responsibility through self-care and the practice of social distancing.

Analysis of COVID-19 Impact using Data Visualization

International Journal for Research in Applied Science and Engineering Technology IJRASET, 2020

The world has suffered from many crises and pandemics in the past but it's the creativity and inventiveness of its people and their rigorous efforts with the capacity to think out of the box which has made them combat and overcome those situations. The world is facing a similar situation with the inception of COVID-19. The deadly new coronavirus first detected in Wuhan; the capital of China's Hubei province has sat its foot across the globe by infecting millions of people worldwide. Nevertheless, tough times ask for draconian measures and smart solutions, so the objective of our research work is to analyse the data with the help of visualization using Python to delineate and bring out a result by comparing the COVID-19 outbreak in different continents and countries like the USA, China, India, Italy, and Taiwan. For differentiation, we have used the total number of confirmed cases, the total number of casualties, and the total number of recovered cases of the COVID-19. Additionally, we have also compared the total number of tests conducted by the countries mentioned above. Our research will also concentrate on what makes COVID-19 pandemic different from other epidemics like SARS (2003), MERS (2012), Ebola (2014) by comparing their mortality rate, contagiousness, and symptoms. For all the comparison we have used data visualization. This research provides a comprehensive understanding of COVID-19 and compares its impact on different regions of the world with the help of Data Visualization and it will also help to derive a better solution for future emergencies.

A Study of Real World Data Visualization of COVID-19 dataset using Python

International Journal of Management and Humanities, 2020

The importance of data science and machine learning is evident in all the domains where any kind of data is generated. The multi aspect analysis and visualizations help the society to come up with useful solutions and formulate policies. This paper takes the live data of current pandemic of Corona Virus and presents multi-faceted views of the data as to help the authorities and Governments to take appropriate decisions to takle this unprecedented problem. Python and its libraries along with Google Colab platform is used to get the results. The best possible techniques and combinations of modules/libraries are used to present the information related to COVID-19.

Data Visualization and Analysis of COVID-19 Data

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022

During the COVID-19 pandemic, many data visualizations were created to alert the public to the rapidly growing threat. Statistics on the spread of COVID-19 have been displayed on data dashboards, a mechanism for sharing information throughout the pandemic, which has aided in this process. When developing the visuals for COVID-19, the majority of time was spent on the technical aspects of designing and evaluating various visualization methods. Little is understood about the inner workings of visualization production processes due to the complex sociotechnical environments in which they are embedded. However, such ecological data is necessary for identifying the particulars and tendencies of visualization design practices in the wild and generating insights into how artists learn to perceive and approach visualization design on their terms and for their contextual aims. We conducted in-depth interviews with dashboard designers from federal and state health departments, major news media outlets, and other firms that created (often widely used) COVID-19 dashboards to gain insight into the following areas. What kind of problems, disagreements, and conflicts arose during making the COVID-19 dashboard because of the participation of visualization creators? The trajectory of design practices-from genesis to expansion, maintenance, and termination-is determined by the complex interconnections between design goals, design tools and technologies, labour, emerging crisis circumstances, and public participation. We zeroed in on these procedures' tensions between designers and the general public. Conflicts frequently arose due to a chasm between public demands and prevailing policies. They typically centred on the types and amounts of information that should be visualized and how public perceptions shape and are shaped by visualization design. The strategies used to deal with (potential) misinterpretations and misuse of visualizations. Our findings and takeaways offer fresh viewpoints on visualization design by highlighting the bundled activities typically linked with human and nonhuman participation along the entire trajectory of design practice.

Visualisation System of COVID-19 Data in Malaysia

Trends in Undergraduate Research, 2021

Pandemics are highly unlikely events, therefore, we need a system to understand the statistics about the pandamic. Machine learning algorithms can analyse the data and then we can plan for handling the pandamic. To date, many people are suffering because of the lack of reliable information system. The problem is that there is no integrated system to use the data and plan for pandemic management to minimise social panic. This study aims to provide a system, using COVID-19 data as a sample to visualise and analyse cases, deaths, discharged ICU cases updates in Malaysia as a whole state wise of COVID-19 daily statistics. The results provide visualisation and case comparison among states in Malaysia to easily and quickly understand the situation. This will help and assist the management in decision-making.

Data Visualization and Analyzation of COVID-19

Journal of Scientific Research and Reports

Since December 2019 the world is experiencing a deadly disease caused by a novel coronavirus termed as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The disease associated with this virus is known as COVID-19. This paper focuses on COVID-19 based on freely available datasets including the ones in Kaggle repository. Data analytics is provided on a number of aspects of COVID-19 including the symptoms of this disease, the difference of COVID-19 with other diseases caused by severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), and swine flu. The impact of temperature on the spread of COVID-19 is also discussed based on the datasets. Moreover, data visualization is provided on the comparison of infections in males/females which shows that males are more prone to this disease and the older people are more at risk. Based on the data, the pattern in the increase of confirmed cases is found to be an exponential curve in nature. Finally, the relat...

Data visualization in diseases epidemiology

GSC Advanced Research and Reviews, 2021

Data visualization has transformed diseases epidemiology by empowering researchers, practitioners, and policymakers to unleash complex patterns, trends, and relationships. It uncovers hidden correlations and clusters, tracks disease outbreaks and transmission dynamics, identifies high-risk populations and areas, evaluates intervention effectiveness, and communicates complex findings to diverse audiences. Through exemplary visualizations, data visualization distills complex epidemiological data into actionable insights, informing data-driven decisions that promote public health and well-being. As advanced visualization techniques continue to evolve, they accelerate the understanding of disease dynamics, aid in the allocation of resources, and drive proactive strategies for disease mitigation. However, barriers such as data quality, infrastructure limitations, and the need for skilled personnel persist, especially in under-resourced settings. This paper presents a critical evaluation of the role of data visualization in epidemiological practice, discussing its implications for risk identification, policy formulation, and the proactive management of health crises. The insights gained from this paper will illuminate pathways for future innovations in disease surveillance and control.

Interactive Data Driven Visualization for COVID-19 with Trends, Analytics and Forecasting

2020 24th International Conference Information Visualisation (IV)

Interactive dashboards process and present raw data in the form of visuals, graphs, and text along with various options for user interactions. The dashboards allow for extracting valuable information and showcase the data in an intuitive and easy to understand manner. As the world is battling with the COVID-19 pandemic, we developed an interactive data-driven dashboard to not only view the current trends, but to also display important analytics and projections for the upcoming week. Built using python modules Dash and Plotly for visualization, the proposed dashboard utilizes the data analytic capabilities of the Pandas python library to structure and organize the raw data efficiently. Our dashboard is lightweight and designed for optimal performance. It can update values dynamically and be loaded onto any web server. Moreover, our proposed solution performed the best when compared to three other COVID-19 solutions, in terms of performance and speed, page size, and the number of HTTP requests.