Personalized epidemiology works by integrating a variety of data sources, including genetic information, lifestyle factors, environmental exposures, and socioeconomic status. Advanced analytical tools are then used to identify patterns and correlations that may not be apparent through traditional methods. For example, machine learning algorithms can analyze large datasets to predict disease outbreaks or identify at-risk individuals based on their unique characteristics.
