In light of the COVID-19 pandemic, this interactive project accounts for more than 1,000 measures taken from December 2019 to May 2020 in seven major countries: China, the United States, South Korea, the United Kingdom, Italy, France, and Germany. The data covers several perspectives, such as the number of confirmed cases, anti-epidemic policies, and medical research. This project aims to show how different countries dealt with this “super virus,” besides analyzing different countries’ policies and implementation.
The “Super Virus” organically combines in-depth news reporting with interactive multimedia visualization, thereby enhancing communities’ understanding of the novel coronavirus and the measures they should take to protect themselves and their fellows. This social impact can be gauged by the popularity of the project: At the time of making this report, its total views had reached 12.02 million, with 8.92 million unique user visits and 487,000 comments and reposts.
The data team used Google Sheet to collect and update the data. Moreover, Beautiful Soup and API were used to get news headlines from various countries’ mainstream media, which were later fact checked.
In addition, we used D3.js for data visualization and combined high-performance visual animation algorithms to allow readers to have a smooth experience. At the same time, we also use parallax scrolling to help readers interact with content and data, allowing them to easily “see the content” in the process of reading “charts” and “data”. In terms of dissemination, the work is bilingual in both Chinese and English, adapted for both PC and smartphone/pad.
What was the hardest part of this project?
The most challenging part of producing this project was data collection and sorting, especially when it came to anti-pandemic measures taken by different governments. We first scraped data from media in several countries to give us an initial look of each country’s measures. To guarantee accuracy, we had to extensively go through official government websites as well as press releases, documents and papers for fact checking. After getting all the data, the problem became how to categorize it. Since countries having taken some similar measures, we could categorize them as one type of category, such as school closure. However, measures like lockdowns were pretty different from country to country. Therefore, before categorizing, we standardized the definition of each measure, which have been explained at the end of the project page.
What can others learn from this project?
We believe the technique of dealing with timeline is the most valuable lesson we’ve learned from this project. To calculate the speed of countries taking measures against COVID-19, we transformed the natural date to the the first confirmed case appearance in each country, standardizing the start of COVID-19 in each country. Thus, the readers can easily understand how countries reacted to this pandemic.