Eileen Dobzynski - ANT/STS 109

Part 1

Measles is a highly contagious, viral disease that wreaks havoc on vulnerable populations around the globe, Outbreaks happen everywhere and unlike climate-driven diseases, outbreak are not isolated to certain geographic regions. Rather, the largest outbreaks occur in areas with low vaccination rates. There is no direct cure for measles, but getting vaccinated is the best way to protect yourself from the virus. This article will discuss and visualize data about global measles cases and global vaccination rates between the years of 1980 and 2017 to show the effectiveness of the vaccine. The first bar graph below shows the decline in measles cases between 1980 and 2017 as a result of global efforts to slow the spread of this epidemic (The World Health Organization).

Figure 1

dat <- read.csv("~/Desktop/ANT 109/Week 4/measles.csv", stringsAsFactors=TRUE)
dat$year <- as.factor(dat$year)
barplot((dat$measles.cases)/1000000~dat$year, width=1, main="Annual Measles Cases (1980-2017)",xlab= "Year", ylab="Measles Cases (Million)", col="slateblue2")

In this graph, you can see the overall trend for cases is decreasing across the 37-year-window. However, because measles is outbreak driven, there are years on the graph where the following year’s cases are higher than the year prior, as represented by a higher bar. For example, the number of cases in 1980 was 3,852,242 whereas the following year, in 1981, there were 4,078,455 cases. Another notable part of this graph and how it reflects the data, is the sharp change from the mid 1980s into the early 1990s. The difference in bar height, reflects the efforts of vaccination efforts targeted toward children during this time and was instrumental in preventing serious contraction and complications later on in life (The Journal of Infectious Diseases).

Part 2

To understand better the drastic impact of increasing measles vaccination to slow the spread and number of outbreaks, it is needed to look beyond the 1990s and more in depth into the 2000s to see how the trend of cases continued downwards as the vaccination rates increased. The number of measles cases in 1985 was 2,819,553. In contrast, in 2005, it was 585,701. That is a difference of 2.3 million cases. A large factor of why the number of annual cases changed so drastically, is increased support of vaccinating children globally in programs led by the CDC and WHO especially. Inserted below is an interactive graph showing the change in cases and vaccination rates between 1985 and 2005 (The Journal of Infectious Diseases).

Figure 2

Resources (for code):

https://stackoverflow.com/questions/1299871/how-to-join-merge-data-frames-inner-outer-left-right

https://plotly.com/r/hover-text-and-formatting/

dat <- read.csv("~/Desktop/ANT 109/Week 4/measles.csv", stringsAsFactors=TRUE)
library(plotly)
dat$year <- as.integer(dat$year)
dat2 = dat[dat$year>=1985,]
dat3 = dat[dat$year<=2005,]
InnerJoinTable <- merge(dat2,dat3)
dat4 <- InnerJoinTable
p <- plot_ly(data = dat4, x=~vaccination.coverage, y = ~measles.cases, type = 'scatter', mode = 'markers',
    text = c('1985', '1986', '1987','1988','1989','1990','1991', '1992', '1993', '1994', '1995', '1996', '1997', '1998', '1999', '2000', '2001', '2002', '2003', '2004', '2005'),
    marker = list(size = 8, color = "darkorange", line = list(color="black", width = 1))) %>%
  layout(title = 'Vaccination Coverage Relative to Annual Measles Cases (1985-2005)', 
         xaxis = list(title = 'Vaccination Coverage %'), 
         yaxis = list(title = 'Measles Cases'))
p

This scatterplot’s interactive features allow you to hover over each point and see (1) the percentage of the vaccinated population, (2) the number of annual cases and (3) the year of the data. As you hover over the points, you can see how not all the years are linear. Some years the vaccination rates are lower and others are higher. This is because of birth rates, vaccination eligibility, and program funding. Another area to note is the cluster formed between the years with approximately 70-75% vaccination rate. This cluster is where the margins become the smallest and follow the trend shown in the previous bar graph (figure 1). What can be interpreted from these results is that the higher the vaccination rate, the lower the number of annual cases is to be consistently expected (The World Health Organization).

Part 3

Figure 3

Resources (for code):

https://stackoverflow.com/questions/74369159/r-plot-xlab-for-side-3-and-side-4

dat <- read.csv("~/Desktop/ANT 109/Week 4/measles.csv", stringsAsFactors=TRUE)
par(mar = c(5,4,4,5))
barplot((dat$measles.cases)/1000000~dat$year, width=1, main="Measles Cases and Vaccination Rates (1980-2017)",xlab= "Year", ylab="Measles Cases (Million)", col="slateblue2")
par(new=TRUE)
plot(dat$vaccination.coverage~dat$year, type = "l", col="darkorange", lwd = 4, xaxt = "n", yaxt = "n", ylab = "", xlab = "")
axis(side = 4, mtext("Percent of Vaccinated Population", side = 4, line = 2.5))
legend("right",title = "Legend", legend = c("Measles Cases", "Percent of Vaccinated Population"), col = c("slateblue2", "darkorange"), pch = 1, cex=0.6)

General Sources

Measles Information (The Whole Health Organization): https://www.who.int/news-room/fact-sheets/detail/measles

Outbreak Containment and Vaccination Efforts (The Journal of Infectious Diseases): https://academic.oup.com/jid/article/189/Supplement_1/S17/821924