Data science is crucial in epidemiology for several reasons:
Data Collection: Efficiently gathering vast amounts of health-related data from various sources such as hospitals, surveys, and laboratories. Data Analysis: Applying statistical methods to identify trends, correlations, and outliers in the data. Predictive Modeling: Using machine learning and other techniques to forecast disease outbreaks and the potential impact of public health interventions. Decision-Making: Providing evidence-based insights to inform policy decisions and resource allocation.
