Data Science and COVID-19The Data Science and COVID-19 Blog
This blog discusses trends and patterns regarding the COVID-19 pandemic in terms of infection rates, etc. Specific observations regarding the spread of the virus are illustrated with supporting graphs and statistics.
The blog also includes ongoing examples of how to use the freely available tools to perform your own analysis of COVID data. These tutorials will allow you to employ Data Science techniques to analyse your own datasets.
The blog also includes news updates on changes to the website.
Blog posts by category
Blog posts by date
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Is San Marino really the centre of the coronavirus?
The onset of COVID-19 has led to a torrent of statistics and graphs as the virus wreaks havoc across the world. A number of initiatives have been setup to filter and centralise data across the globe. To name but a few:
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When is a peak not a peak?
Epidemiological spread often starts slowly by infecting a small number of carriers, but quickly takes hold as these people go on to infect multiple other people. A virus that is highly contagious will have a high R0 value, meaning that each person transmits the infection to many other carriers. The infection curve rapidly increases as the virus takes hold in the population. Only as it works its way through a population will the virus eventually slow down before tapering off.
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Web-site reorganisation
Even small amounts of data can generate many different types of graphs and results. The number of graphs is becoming too much for a single page so I’ve made a major re-organisation of the web site. Graphs are now organised into sub-categories and navigation is made easier. The blog will still continue in parallel but the graphs sections will be expanded.
All of this using the Liquid templating language inside the Jekyll framework.
See the git repo for more details (especially the scripts in the _bin folder) and the new markdown pages. -
Getting started with pandas and COVID-19 data
Python is a simple and elegant programming language used widely in the scientific community. There are thousands of libraries and frameworks that extend the reach of the language even further. Pandas is a data analysis framework that can be used to easily read data files then manipulate them to analyse the information. You can even plot graphs of your results. Here is a simple example. The European Centre for Disease Prevention and Control centralise data on the worldwide COVID-19 infections and deaths. You can download this in Excel format here.
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Website moved to Jekyll
I have migrated these pages to use the Jekyll web site generator. Pages are now written in Markdown which is easier to use than HTML. I can also focus on the content instead of page layout.
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