The Coronavirus Outbreak
Track the earliest days of the Covid-19 outbreak in the provinces of China.
——— Updated April 16, 2020 ———
How fast did it spread in the beginning of the pandemic?
The Novel Coronavirus virus, specifically known as 2019-nCov, is responsible for a flu-like respiratory illness and has caused the most widespread global pandemic since influenza in 1918.
What hints of the pandemic to come where present in the early data? How fast did it first spread within China? Before your students dive into a pair of datasets to help answer those questions let's cover some important facts around the news reports.
According to the US Centers for Disease Control and Prevention, coronaviruses are a group of viruses that cause diseases in many species of mammals and birds. Rarely, these coronaviruses can evolve and infect humans and then spread between them.
The incubation period of the virus (from exposure to symptoms) is between 2 and 10 days and it remains contagious during this time. Symptoms include fever, coughing and breathing difficulties.
The coronavirus, designated 2019-nCoV, was first identified in Wuhan, a city of 11 million in the Hubei province of China, after people developed pneumonia without a clear cause and for which existing vaccines or treatments were not effective.
The virus is spread by human-to-human transmission via coughing as it is found in respiratory fluids.
Activity 1: Spread of Covid-19 within China in the earliest days of the pandemic
Make a data visualization to show the growth in the number of confirmed cases in different provinces within China. Start by showing Time on your X axis and Confirmed Cases on your Y axis. Then show Province on your Z axis (this will color code by province) and check the Connect dots box to make a line graph with a line for each province.
1) Describe the growth in number of confirmed cases across the provinces of China in the earliest days of the pandemic? How would you describe the growth in the Hubei province as compared to all others?
2) Show the dataset as a table. Use the check boxes on the right side of the rows to exclude all the rows of data from the Hubei province. Then go back to the graphing view to see the data from all of the provinces with Hubei excluded.
How does the information that you are able to see for the other provinces change when you view it without the data from Hubei? How does the scale of the Y-axis influence what you are able to easily see in the graph.
3) Look at the map of the provinces of China. Find the six provinces with the highest recorded numbers of confirmed cases? How strongly does the proximity to Hubei predict the number of confirmed cases? Does this make sense with what you know about how the virus is spread?
4) Based on this dataset does it surprise you how fast Covid-19 spread throughout the rest of the pandemic outside of China? Why or why not?
Confirmed Cases in Mainland China by Province
As of February 24, 2020
Activity 2: Spread of the virus outside of China in the earliest days of the pandemic
Make a data visualization to show the growth in the number of confirmed cases in China and elsewhere in the world. Start by showing Time on your X axis and Confirmed Cases on your Y axis. Then show Location on your Z axis (this will color code by province) and check the Connect dots box to make a line graph with separate lines for mainland China and other locations.
Tip: After you add a variable with the Show button beneath the column headers, you can change the variable on any axis by pressing X,Y, and Z buttons on the lower right portion of the screen. These buttons turn red when a variable is active on the plot.
As of April 16, 2020
1) Describe the growth in number of confirmed cases within China? How would you describe the spread of the virus within China as compared to the rest of the world at this point in time (less than 100 days into the pandemic).
2) Now show the dataset as a table. Click on the Values button beside the Location variable name. Exclude all the rows of data from Mainland China by checking the exclude box. Then go back to the graphing view to see the data only from Other Locations around the world. Add a regression line to the new graph. Try both the Linear and Exponential Regression Lines.
Which appears to be a better fit? What does that tell you about how fast the virus was spreading in the beginning of the pandemic.
Additional Resources
Updated data-dashboard from Johns Hopkins University with the latest data on the number of confirmed cases around the world and the source of these data: https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6
Everything you need to know about coronavirus, the deadly illness alarming the world: https://www.usatoday.com/in-depth/news/2020/01/29/coronavirus-what-are-symptoms-of-wuhan-china-novel-virus/4563892002/