2.0

credits

Average Course Rating

Infectious diseases remain a major threat to health globally, and the risk of emerging pandemics like HIV/AIDS and SARS-CoV-2/COVID-19 persists. This course will equip students with the tools to effectively use models to guide clinical and public health decision making for infectious diseases. Topics covered include understanding the complex systems that govern disease transmission, designing and interpreting mathematical models of disease processes at individual patient and population scales, stochastic and deterministic model formulation, use of networks in disease modeling, computational approaches to model simulation, statistical inference methods for timeseries, and data science methods for dealing with common limitations of infectious disease data. Examples will be taken from a range of endemic and epidemic diseases and will include disease evolution and drug resistance. Students will develop and analyze models to inform disease prediction, development of therapeutics, implementation of vaccination, and other control programs. Recommended background: Students should have prior college-level coursework in calculus, linear algebra, probability, statistics, and differential equations. Being comfortable coding in a language such as Python, R, or Matlab is required. No specific academic background in biology or epidemiology is required, but a strong interest and willingness to learn is. Co-listed with EN.580.673