I decided to take a deeper look at the COVID-19 cases in Singapore based on publicly available data. In the process, I came up with some interesting visualization and observations.
Here’s what I’ve been able to do:
Network Graph of Local Clusters
One of the more worrying features of COVID-19 is its ability to spread in a community. We see that despite strong and proactive approaches taken by the Singapore government, COVID-19 clusters continue to pop in Singapore. Many of these cases are unlinked to imported cases and other local clusters. View the interactive graph here.
Social distancing is important. In the snapshot below, we see a part of the graph that tells a concerning story: how an imported case (#192, an American), went to a bar and led to a string of local transmissions.
In this crucial time, safeguarding one segment of Singapore’s society is becoming ever more crucial. That is the foreign worker population in Singapore, long kept mostly out of sight, living in cramped dormitories. Now, they are starting to form a large portion of new COVID-19 cases in Singapore, as their dormitories and work sites become clusters for spreading the disease. The diagram below shows the linked COVID-19 cases related to work site (Project Glory) and 4 other worker dormitories. Most of the cases here are Indian foreign workers, here on a long-term work permit.
Other Visualizations, Data and Source Code
Some simple overall statistics are visualized in this Jupyter Notebook. One particularly interesting result was the age distribution of imported and locally transmitted cases. It seems that majority of the imported cases are students returning from overseas, while majority of locally transmitted cases are middle-aged people.
For the cleaned data in a CSV format and the source code for the material shown here, visit my GitHub repository. Stay safe, and stay home!