More Select COVID-19 Resources

[This article was first published on R Views, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

We are over five months into this pandemic, and it is pretty clear that almost everyone is really tired of hearing about it. I myself am totally zoomed out and have already seen too many dashboards. Nevertheless, we are in this for the long run. So from time-to-time, I think it worthwhile to continue to look for tools that can help us make some sense of the continuing stream of incoming data.

First, I would like to draw your attention to the Covid Trends animated dashboard from Physics teacher Aatish Bhatia. The epidemiologists are the experts in this domain, but is just like a physicist to deliver on insight.

What’s unique about this dashboard is how it beautifully illustrates the consequence of exponential growth. Notice that there is no time axis on the graph. Total confirmed cases are plotted on the x axis, and new confirmed cases in the past week are plotted on the y axis. In this setup, doubling times are represented as straight lines. As you run the animation, time passes and you observe the various countries hugging the seven day doubling time line and then dropping down as the whatever counter measures they are taking get the epidemic under control. This plot makes it clear that while things are opening up in the U.S. we do not quite have the disease under control. Please do watch the short video explaining the graph.

Next, please have a look at the COVID-19 Data Hub, an open source project started by Finance Ph.D. student Emanuele Guidotti with initial IVADO arranged by David Ardia that may very well become the main repository for epidemiologists working with COVID-19 case data. Currently over sixty data sets are available.

All data sets are in a standardized format, and are well documented. Additionally, the site provides R, Python, MatLab, Julia, Node.js, Scala and Excel code to access the data. This project is an extraordinary effort that deserves community support.

Finally for today, I recommend the video recording from the first COVID-19 Data Forum webinar held on May 14, 2020. After the opening remarks by Michael Kane, Assistant Professor, Department of Biostatistics, Yale University which begin at one minute and forty seconds (1:40) into the video, there are four talks, each approximately fifteen minutes long.

The first talk: Modeling COVID19 spread and control: Data needs and challenges by Alison Hill of the Department of Organismic & Evolutionary Biology, Harvard University begins at (5:33). The second talk: Collecting and Visualizing COVID-19 Case Count Data from Multiple Open Sources by independent consultant Ryan Hafen begins at (21:26). The third talk: Spatial and Space-Time Data on COVID-19 by Orhun Aydin of esri and the Environmental Systems Research Institute University of Southern California begins at (38:49). The final talk by Noam Ross of the EcoHealth Alliance and rOpenSci which begins at (56:23), focuses on the genomic data that enables scientists to study the emergence of new diseases.

Enjoy the videos.

To leave a comment for the author, please follow the link and comment on their blog: R Views. offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)