The folks at Wall Street Journal took CDC disease data (by state, by year, courtesy of Project Tycho ) as well as information on when various vaccines were introduced to the public. And the data tells a compelling story about the importance of vaccinations. Below, the story of measles. How to use in class: -Using archival data to educate and make a point (here, vaccine efficacy) -Visualizing many data points (infections x state x year) effectively -Interactive: You can cursor over any cube to see the related data. Below, I've highlighted Pennsylvania data from 1957. -Since you can cursor over any data point to see the data, you can ask your students to pull data for use in class. -The present data were drawn from Project Tycho , a University of Pittsburgh initiative to better share public health data. This resource may be useful for your classes as well. -This data is good for Stats class, as well as Developmental, Health, Public Health, etc.

Statistics aficionados over at FiveThirtyEight applied statistics (specifically, tools used by epidemiologists) to the Summer of 2015  outbreak of Legionnaires' Disease  in New York. This story can be specifically used in class as a way of discussing how simple bar graphs can be modified as to display important information about the spread of disease. This news story also includes a link to a learning module  from the CDC. It takes the user through the process of creating an Epi curve. Slides 1-8 describe the creation of the curve, and slides 9-14 ask questions and provide interactive feedback that reinforces the lesson about creating Epi curves. Graphs are useful for conveying data, but even one of our out staples, the bar graph, can be specialized as they share information about the way that disease spread. 1) Demonstrates statistics being used in a field that isn't explicitly statistics-y. 2) A little course online via the CDC for your students to learn to...

Ben Blatt of Slate mined through Center for Disease control data in order to provide us with 13 different maps of the United States and mortality information for each state . Below, information on disproportionately high cases of death in each state. While the maps are morbid and interesting, the story behind the maps ( read the story here about how data can be easily misrepresented by maps ) make this a good example of how easily data can be distorted. The story along with the maps unveils several issues that statisticians/researchers must consider when they are presenting descriptive statistics. In this instance, Blatt had to sort through the data to eliminate the most common causes of death (heart disease, cancer, etc.) in order to uncover unique data for each state. Relatedly, he highlights the fact that "disproportionately" does not mean "most": "But this map—like many maps which purport to show attributes meant to be “distinct” or “disproporti...

This Scientific American podcast by Diane Fine Maron describes how the Chicago Department of Public Health (CDPH) used Twitter data to shut down restaurants with health code violations. Essentially, the CDPH monitored Tweets in Chicago, searching for the words "food poisoning". When such a tweet was identified, an official at CDPH messaged the Twitterer in question with a link to an official complain form website. The results of this program? "During a 10-month stretch last year, staff members at the health agency responded to 270 tweets about “food poisoning.” Based on those tweets, 193 complaints were filed and 133 restaurants in the city were inspected. Twenty-one were closed down and another 33 were forced to fix health violations. That’s according to a study in the journal  Morbidity and Mortality Weekly Report.  [Jenine K. Harris et al,  Health Department Use of Social Media to Identify Foodborne Illness — Chicago, Illinois, 2013–2014 ]" I think this is ...