10-748 Machine Learning in Epidemiology

12 units, Carnegie Mellon University

Topics include: an accelerated computational introduction to viruses, viral evolution, immunology and infectious disease epidemiology. The forecastability of epidemics. Epidemic models: compartmental, auto-regressive, equation-based, network-based, and simulations. Bayesian and non-Bayesian methods for parameter estimation and inference in epidemic processes. Data assimilation methods. The forecasting and nowcasting problems. Probabilistic forecasting and scoring rules. Auxiliary data sources and epidemic indicators. Digital phenotype optimization. Extracting epidemic indicators from unstructured data. Leading and lagging indicators and signal deconvolution. Epidemic interventions: pharmaceutical, non-pharmaceutical and informational. Targeting and resource allocation. The role of deep learning and generative AI in epidemic forecasting. Time permitting, we may also discuss the application of ML to other problems in public health.

Prerequisites: 10-301 (min C) or 10-315 (min C) or 07-280 (min C) or 10-701 (min C) or 10-601 (min C) or 10-715 (min C)

Offered: Fall 2025

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