How many infected people are still in our near environment, in our country, and further to neighboring countries?

Note from the editors:Towards Data Science_ is a Medium publication primarily based on the study of data science and machine learning. We are not health professionals or epidemiologists, and the opinions of this article should not be interpreted as professional advice. To learn more about the coronavirus pandemic, you can click here._

The COVID-19 pandemic looks like a world war, spreading to 213 countries and regions around the world [], bringing deaths, sickness, fear, sadness, disaster, and chaos to the world. An immense volume of COVID-19 data streams daily to us as messages from the front of the battle with our invisible enemy, SARS-CoV-2 virus. From this data volume, I have asked for myself a question:

“How many infected people are still in our near environment, in our country, and further to neighboring countries?”

The number of currently infected patients is important; it is helpful for our living, planning, working, and preventing. From this motivation, I am extending my research interest from the visualization of data, and the estimation of the undiscovered infection cases, to the comparison of the active infection cases from different locations.

In this article, I want to share with you my method “Normalization of accumulated active cases” for analysis data from multi-country.

Because of my data resource, which delivers data from many countries, I could be working at a “high level”: compare data from the countries around the world. However, you could use my methods and my open-source software package writing in Python to analyze data from the other geographic locations too.

I presented here some showcases to demonstrate my developing method. They are not professional reports (as found in WHOCDCRKI), but it could be useful to help us to understand what is going on by COVID-19 Pandemic, beyond the immense volume of data.

#data-science #towards-data-science #programming #visualization #covid19

Comparison of COVID-19-Data From Different Locations
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