Updated: Jan 14
One common form of collective intelligence is knowledge sharing via the internet. We are now so accustomed to Wikipedia, Amazon reviews, Yelp, Waze, and other technologies that aggregate and share data from many sources that we sometimes forget these technologies are all examples of Collective Intelligence in action!
Today I released a new video explaining how to find and use the Covid-19 dashboard from Johns Hopkins University. (See Episode #3 in the videos section of this site)
Chances are you have seen the graphics from the dashboard even if you haven't explored the interactive dashboard yourself. Many news organizations and media outlets are using it. It has great graphics and provides an easy way to monitor the spread of the Coronavirus. Because the functionality is based on data aggregation and sharing, I view the dashboard as one of the great success stories of how Collective Intelligence is being applied to overcoming the Covid 19 pandemic.
You can find the dashboard tool at this link: https://coronavirus.jhu.edu/map.html
…. or by just Googling "Coronavirus dashboard" and looking for the dashboard to come up in the first few search results.
In the three decades I've spent analyzing datasets, I've learned one very important thing: GIGO. Garbage in, garbage out.
This means that no matter how fancy the graphics are, or how easy to use the interface is, the results coming out of the Covid 19 dashboard are only as good as the data on which the dashboard is based. Johns Hopkins does a good job of identifying and linking to its sources of data. That said, the University has no control over what other governments and health departments are saying or how they count their cases. As I explain in the video, there may be reasons to suspect some countries of falsifying data and, even if we generously assume that every country is being 100% honest, much of the data is still very noisy.
Fortunately, there are enough different countries reporting data that we can get a pretty good idea of how the virus is spreading generally. I plan to revisit the dashboard in a few weeks to see if my comments about the effectiveness of various countries' approaches hold true as we get more trend data.
The main conclusion I draw at this point is that we are still very far from herd immunity.
Secondarily, using data sources like the dashboard are going to be important as we decide how and when to open up and/or tighten restrictions. Hats off the Johns Hopkins team, and specifically to the engineers at CSSE, for providing this terrific tool!