COVID-19 Data Visualization
The purpose of this exercise was to understand the 2019 Novel Coronavirus COVID-19 Data Repository by Johns Hopkins CSSE and explore it using some foundational packages in the Scientific Python Data Science stack without intention to create or publish my own data vizuations, to not dilute those that experts with domain expertise are publishing.
The COVID-19 pandemic generated a massive amount of complex and rapidly evolving data. For the general public, decision-makers, and healthcare professionals, it was difficult to track the pandemic's evolution and understand its trends through simple numerical tables.
There was a critical need to transform this raw, often opaque data into clear, accessible, and actionable information to facilitate understanding of the pandemic's dynamics.
Available Data: A public dataset compiling daily pandemic figures (confirmed cases, deaths, recoveries) by country and date.
My main objective was to create an interactive dashboard and data-driven visualizations to effectively explore and communicate key trends of the COVID-19 pandemic.
This main task was broken down into several concrete objectives:
-
Clean and Preprocess Data: Ensure the quality and consistency of raw data to make it usable.
-
Create Relevant Metrics: Calculate key indicators such as the case fatality rate (CFR) and recovery rate to provide more nuanced analysis than raw numbers.
-
Develop Interactive Visualizations: Build charts allowing users to filter and explore data by country and time period.
-
Synthesize and Communicate Insights: Highlight global trends and specific national situations in a visual and understandable manner.
I first cleaned and prepared the data by calculating key indicators like fatality and recovery rates. Then, I created several interactive visualizations with Plotly: world maps to see the spread, curves to track evolution over time, and comparative charts between countries, all enhanced with filters for personalized exploration.
This approach produced an interactive dashboard centralizing all pandemic information. The tool made complex data easily understandable, clearly revealing major trends such as successive waves and differences between countries, thus demonstrating the utility of data visualization for informing decisions during a crisis.
In summary, this scenario solved the problem of COVID-19 data opacity by transforming it, through rigorous analysis and visualization, into an informative interactive dashboard, thereby facilitating pandemic understanding for a broad audience.
Technologies Used:
Terms of Use
▸ This data set is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) by the Johns Hopkins University on behalf of its Center for Systems Science in Engineering. Copyright Johns Hopkins University 2020.
▸ Attribute the data as the “COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University” or “JHU CSSE COVID-19 Data” for short, and the url: https://github.com/CSSEGISandData/COVID-19.
▸ For publications that use the data, please cite the following publication: “Dong E, Du H, Gardner L. An interactive web-based dashboard to track COVID-19 in real time. Lancet Inf Dis. 20(5):533-534. doi: 10.1016/S1473-3099(20)30120-1”
SHARE
Create applications and maps to tell the story of data and transform it into action levers