December 2020 vaccine distribution and administration began. I started trapping the daily spreadsheet from the state health department that tracked progress. This blog entry is really for prototyping some of the data cleanup and displays that I will incorporate into my shiny app. Let’s take a look at data issues. df %>% filter(! %>% ggplot(aes(x=Pct_given)) + geom_histogram()+ labs(x="Percent Distributed Administered", title="Distribution of Administered Vaccine") ## `stat_bin()` using `bins = 30`.

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Introduction Towards the end of May the state of Texas suddenly began adding the number of COVID-19 cases detected amongst prison inmates to the county totals for the counties in which the prisons resided. However, they have not indicated if they did this change on a single day, or if it may have taken place over several days, for different prisons. In this bit of work, I will try to ferret out what they did as best I can, so that I may best correct my own data.

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Piecewise data fitting As the COVID-19 pandemic progresses, the simple exponential and logistic models no longer fit the data very well. As waves of infection and retrenchment occur, it seems likely that the best fits will be done piecewise. For this blog entry I will experiment with various schemes to see if I can get a reasonably good strategy for constrained fitting to the data. As I have a well-structured dataset for all the counties in Texas, that is what I will use for the experiments.

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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