Real-world examples of computational overhead in epidemiology include:
COVID-19 modeling efforts, which required immense computational resources to simulate transmission dynamics and evaluate intervention strategies. Genomic epidemiology studies, where large genomic datasets are analyzed to trace pathogen evolution and spread. Climate change impact assessments on disease patterns, which involve complex models integrating environmental and epidemiological data.
