SeeSick: A Mobile Application for Tracking and Visualizing the Spread of Illnesses in Real Time

With the population size growing rapidly at Arizona State University, students are more likely to get sick and miss school when living on campus. The purpose of this project was to design a mobile web application called, SeeSick, that would visualize the spread of illness on the ASU Tempe campus. This application would provide students with information that could help prevent the spread of illness and allow them to take actionable steps for staying healthy. To accomplish the design and testing of this application, research was conducted on how technology is currently used by students when they are sick, how to design an effective user interface for ASU students, how to physically visualize the spread of the flu on an app, and if an application like this would be useful. The visualizations are created from a user input form and from Twitter data scraping and are displayed on a heat map of the Tempe campus. 126 students were surveyed before the development of the application and once the application was functional, 87 students were interviewed for user testing. Through trial-and-error design and testing, the application was analyzed to determine if it would be used and change behavior. The design of SeeSick successfully provided users with a way to visualize the spread of symptoms on campus and presented them personalized feedback about their symptoms. 62% of students interviewed found the application to be useful and 84% of participants found it easy to use. However, 57% of students said their behavior would not change while using SeeSick. Of the students who tested the application, SeeSick was found to be useful, easy to use, but would not cause behavior change. The current version supports the goal to create a mobile application that tracks the spread of the flu on campus, however it was not tested enough to determine if it would change behavior. With further development and larger testing groups, SeeSick could be improved to not only track the spread of illness on a hyper-local level, but also create actionable steps to prevent the spread of illness.