Several factors contribute to computational complexity in epidemiology, including:
Data Volume: The sheer volume of data from different sources (e.g., clinical reports, genomic sequences, contact tracing) can be overwhelming. Model Complexity: Advanced models that incorporate multiple variables and interactions tend to be computationally intensive. Real-time Processing: The need for real-time or near-real-time analysis adds another layer of complexity. Algorithm Efficiency: Inefficient algorithms can significantly increase computational time and resource consumption.
