Covid and Politics Let’s take a look at Texas politics and Covid deaths. The CDC Wonder data has preliminary deaths and preliminary covid deaths, by county through April of 2022. We can combine this data with the votes from the presidential race and look for correlations. But first, I will calculate the “excess” deaths using a bog simple approach, I will simply assume that for each county the deaths in years 2018 and 2019 represent a somewhat constant background, and any increase in the years 2020 and 2021 will be considered excess deaths likely due to Covid.

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Harris County COVID-19 data I have a very nice (I hope) dataset consisting of number of positive COVID-19 cases per day in Harris county by zipcode. In this blog entry I would like to study this dataset and look at comparisons with various other data. Initial look First off, let’s explore the data for issues, and for ideas about what might be interesting. # How is the data distributed? Let's look at the most recent day Harris %>% group_by(Zip) %>% summarize(Cases_today=last(Cases)) %>% ggplot(aes(x=Cases_today)) + geom_histogram() So over 20 zipcodes have no cases (but they may also have no people), and it looks like most zipcodes are in the 250-750 range.

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Miscellaneous analyses related to the Covid-19 pandemic After reading the paper this morning about a county nearby (Houston county) with zero reported cases, I got curious. What does the distribution of test coverage look like, i.e., number of tests per capita? And also, what is the rate of tests that come back positive? So let’s look at the data. We can now grab an excel spreadsheet from the state that gives number of tests per county.

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Author's picture

Alan Jackson

Retired Geophysicist, geophysical consultant, budding data scientist, trouble-maker.

Consultant and Chief Bottle Washer

Houston and Seattle