3 p.m. - 4 p.m. Location: SLC 1.204
myEpi. Epidemiology of one
Recent introduction of technological innovations such as web-based and mobile-based applications provide a novel way to collect and monitor data on risky behaviors within a single individual. For example a number of applications allow one to monitor smoking, drinking alcohol and drug use as well as non-risky behaviors such as exercise, sleep, food consumption. Traditional epidemiology requires that the results should be applicable to some pre-defined population. It often becomes challenging and even unnecessary to define such a population if the focus is on helping a specific individual. I argue that a single individual could be viewed as an entire population of events that describe behavior and health-related outcomes. I will show how traditional statistical methods used in epidemiology (e.g. survival analysis, time series analysis) that are usually applied to populations of humans, could be applicable to a single individual and thus used for self-monitoring and forecasting of epidemic outbreaks within an individual. I will illustrate similarity between the features of traditional epidemiology (e.g. infectious diseases) and studies of within-person population of risky behavior events. I will discuss application of these methods to a number of subject areas.
Refreshments will be served 30 minutes prior to the talk in the alcove outside FO 2.406.
Sponsored by the Department of Mathematical Sciences
John Zweck, 972-883-6699
Questions? Email me.