5 Wrong Ways to Do Covid-19 Data Smoothing. Much of Covid-19's data analysis is based on flawed smoothing techniques
You might think that raw data is more accurate than smoothed data. But in the case of the Covid-19 pandemic, smoothed data reduces reporting anomalies and is a more accurate representation of timing than the raw data is. But only if the smoothing is done correctly.
Raw state-level data is noisy, and it’s difficult to see trends in raw data. The example below shows the current raw data report from Hawaii. The light blue lines represent positive tests, and the red lines represent deaths.
Are tests going up or down? It’s virtually impossible to tell from this depiction of the data.
In contrast, what does the figure below tell you about whether positive tests are currently up or down? Visually, it’s clear that positive tests have been flat to slightly increasing for about a week.
In this article, I wish to share my thoughts on what challenging data science problems we can solve which have business value amid
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The presence or absence of comorbidities is not just a big deal. It’s a Godzilla-eating-a-major-city size deal. Why hasn’t anyone quantified how it affects risk to individuals?
Data analysis helps us to understands how Coronavirus is spreading all over the world. I am using three datasets (confirmed cases, recovered cases, deaths) for analysis.