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.
Vaccine Reluctance Let’s look at Texas counties and test various factors for correlations to the vaccination rate. We’ll primarily look at the rate of the first vaccination, since there are a variety of reasons why someone might not get the second dose. Let’s start with the raw rates of vaccination by county. Vaccine %>% mutate(Pct_one_dose=People_one_dose/Pop_total) %>% ggplot(aes(x=Date, y=Pct_one_dose, color=County)) + geom_line(show.legend = FALSE) + labs(x="Date", y="First Dose Percentage", title="Texas counties Vaccine Progress") Hmmm… let’s do a little cleanup.
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(!is.na(Pct_given)) %>% ggplot(aes(x=Pct_given)) + geom_histogram()+ labs(x="Percent Distributed Administered", title="Distribution of Administered Vaccine") ## `stat_bin()` using `bins = 30`.
Covid tests in Texas The second entry to look at the testing in Texas. This time there is much more data to examine, but also a new and irritating problem. At some point, for some counties, antibody testing got mixed in with the PCR tests, so the numbers are not nearly as good as they should be. Last I read, about 10% of the tests are the wrong test, but I suspect this is not evenly distributed by county, but rather concentrated in a few.
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.
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.
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.