data journalism, data visualization, infographic, Interactivity, journalism

Visualising the #COVID19 (II): 5 good examples

On this post last week, I gathered 6 good examples of how journalists and researchers have been visualising the #COVID19 and providing different angles of the story.

Below I’m providing 5 more examples (some shared as feedback after the first post):

1. How a virus became a pandemic (New York Times)

The New York Times showcases through a long story format how the virus started on the  ‘worst’ timing ever, when hundreds of millions of people were traveling back to their hometowns for the Lunar New Year from Wuhan.

It is said that about 7 million people left in January, before the travel was restricted. And when China limited local travels, international trips still continued as normal.

Webp.net-gifmaker

2. Who is facing the greatest coronavirus risk? 

Lazaro Gamio visualises on the New York Times all workers (from the Department of Labor database) and the risk level of these with the #COVID19.

This database gathers aspects such as the number of times that a telephone is used or how often one needs to bend their body.

The Y axis measures how often workers are exposed to the disease and the X axis the physical proximity to others.

You can look for a particular job or explore the different bubbles:

Webp.net-gifmaker (1)

3. Are the U.S. hospitals ready for Coronavirus? 

ProPublica presents nine different scenarios where the U.S. health care system is (or is not) able to cope with multiple people infected from the Coronavirus.

At first glance, you can review if your region will be collapsed with the situation or not while the percentage of infected people increase.

Do you want to know more? You can look for a specific hospital nearby you:

maps

4. How can a passenger get infected?

This article on the South Morning Post explains that one person can infect others even if they are more than 4 m away.

Taking a bus as an example, the graphic represents the infected passenger, other people using a mask or not, and how easily can these be infected being 4.5 metres away.

SMP

5. The global spread over time

Tagesspiegel publishes a long format to visualise the spread of the virus. On the animated map, they highlight in yellow countries that were reporting first cases while the cases of people infected were rapidly increasing:

taggespiegel

 

Any more examples? Let me know in the comments or at @mcrosasb

Standard

Leave a Reply

Please log in using one of these methods to post your comment:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out /  Change )

Google photo

You are commenting using your Google account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s