Data science vs the COVID-19 pandemic: Flattening the curve — but how?

Data science vs the COVID-19 pandemic: Flattening the curve — but how?

Whether they are epidemiologists or not, a few people have attempted to use data and predictive models to model the COVID-19 pandemic. Let’s look at the models, the data, and the assumptions and implications that come with them

While things change from day to day, by now we may have enough data, models, and opinions, to make some data-driven observations on how the COVID-19 pandemic is spreading. Perhaps, more importantly, we can venture on what it will take to stop it.

The COVID-19 virus was first detected in late 2019 in China. Since then, it seems like its has stopped spreading in China, while unfortunately it is in different stages of development around the rest of the world. It is questionable whether the data available at this point is enough to draw conclusions.

Machine learning prediction experts from Carnegie Mellon University working on COVID-19 forecasts, for example, acknowledge there is far more uncertainty than usual. Still, they believe their work will be worthwhile in informing the CDC and improving the agency’s preparation. Let’s look at how different people use data for their analyses around the world and try and draw from their insights.

40 million views and 28 translations in a week is a lot, even for an article on a matter of life and death. The author of this Medium post, Tomas Pueyo, is not an epidemiologist. This, however, does not necessarily mean his analysis on epidemiology data is flawed. If nothing else, it is pretty dense, looks convincing, and has been lauded by some health experts and scientists.

Read the full article on ZDNet

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