Mapping the outbreak

Here’s a new tool, updated daily, to help us visualise the spread of the Covid-19 coronavirus.

2019-nCoV-tracker
[H]eadlines can be hard to interpret. How fast is the virus spreading? Are efforts to control the disease working? How does the situation compare with previous epidemics? This site is updated daily based on the number of confirmed cases reported by the WHO. By looking beyond the daily headlines, we hope it is possible to get a deeper understanding of this unfolding epidemic.

mapping-the-outbreak

You can overlay the data from previous epidemics, too, as this summary from The Conversation explains.

Coronavirus outbreak: a new mapping tool that lets you scroll through timelineThe Conversation
Comparisons with other recent outbreaks are also revealing. At one end of the spectrum, the 2014 Ebola epidemic can be distinguished by its devastating virulence (killing nearly 40% of the 28,600 people infected) but narrow geographic range (the virus was largely confined to three countries in West Africa). On the other hand, the 2009 swine flu pandemic was far less virulent (with an estimated mortality rate of less than 0.1%), but reached every corner of the globe.

mapping-the-outbreak-2

A very useful resource. This is exactly the kind of context our news needs to be providing.

Cases Deaths Countries affected Case fatality rate
2003 SARS 8,096 774 26 9.56%
2009 H1N1 (swine flu) 60,800,000 18,499 214 0.1%
2014 Ebola 28,646 11,323 10 39.53%
2019 nCoV:
12 Feb
45,171 1,115 26 2.5%
2019 nCoV:
2 Mar
88,913 3,043 62 3.4%
2019 nCoV:
13 Mar
125,048 4,613 118 3.7%

Update 13/02/2020

Thanks to China’s fast response, are we about to turn the corner?

mapping-the-outbreak-3

A ray of hope in the coronavirus curveThe Economist
Trying to forecast the trajectory of a new virus is complex, with scant initial information about how infectious it is. Several scientists made valiant attempts based on early data from China. Some warned that it might not peak until May, but that was before China implemented strict containment measures. The more pessimistic ones now look too gloomy. Cheng-Chih Hsu, a chemist at National Taiwan University, plugged different scenarios into a simple model for estimating the spread of epidemics (the incidence of daily infections typically resemble bell curves, with slightly fatter tails as transmissions peter out). The tally of confirmed cases so far closely fits a seemingly optimistic forecast by Zhong Nanshan, a Chinese respiratory expert, who said on January 28th that transmissions would peak within two weeks.

The end can’t come soon enough.

The coronavirus is the first true social-media “infodemic”MIT Technology Review
On February 2, the World Health Organization dubbed the new coronavirus “a massive ‘infodemic,’” referring to “an overabundance of information—some accurate and some not—that makes it hard for people to find trustworthy sources and reliable guidance when they need it.” It’s a distinction that sets the coronavirus apart from previous viral outbreaks. While SARS, MERS, and Zika all caused global panic, fears around the coronavirus have been especially amplified by social media. It has allowed disinformation to spread and flourish at unprecedented speeds, creating an environment of heightened uncertainty that has fueled anxiety and racism in person and online.

Update 02/03/2020

I’ve updated the figures in the table above, using data from the tracker. Whilst the numbers of new cases in China is slowing down, they’re increasing everywhere else. And so too is the fatality rate, worryingly.

Update 13/03/2020

And still climbing.

Author: Terry Madeley

I work with student data and enjoy reading about art and design, data, education and technology.

7 thoughts on “Mapping the outbreak”

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