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A new concept of within-individual epidemiology termed 'myEpi' is introduced. It is argued that traditional epidemiological methods, which are usually applied to populations of humans, can be applicable to a single individual and thus used for self-monitoring and forecasting of 'epidemic' outbreaks within an individual. Traditional epidemiology requires that results be generalizable to a predefined population. The key component of myEpi is that a single individual may be viewed as an entire population of events and thus, the analysis should be generalizable to this population. Applications of myEpi are aimed for, but not limited to, the analysis of data collected by individuals with the help of wearable sensors and digital diaries. These data can include physiological measures and records of healthy and risky behaviors (e.g., exercise, sleep, smoking, food consumption, alcohol, and drug use). Although many examples of within-individual epidemiology exist, there is a pressing need for systematic guidance to the analysis and interpretation of intensive individual-level data. myEpi serves this need by adapting statistical methods (e.g., regressions, hierarchical models, survival analysis, agent-based models) to individual-level data
