Mathematical modeling is an increasingly widespread tool for understanding infectious disease transmission and informing public health decision making. This course provides an in depth overview of the practical uses of mathematical modeling for a range of diseases and scenarios. We cover the basic principles of infectious disease models, how models are adapted to be specific to the disease and question of interest, and important assumptions of different models.
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We will discuss how data informs models, how models can be used to interpret data, uncertainty in model results, and pros and cons of different modeling and analytic approaches. In each module we focus on a use case for models: evaluating potential control strategies, quantifying and comparing the transmission rate of infections, and for forecasting future disease burden.
Learners will get the most out of this course if they are familiar with the basics of infectious disease epidemiology and some university level mathematics background. Those who are interested in a more basic overview of model uses in public health decision-making without the requirement for any mathematics background are recommended to view our companion course, Infectious Disease Transmission Models for Decision Makers. Learners who have a stronger quantitative background and are interested in learning to construct models themselves are suggested to consider the Infectious Disease Modeling Specialization developed by Imperial College London.
Syllabus
The Basics of Building Infectious Disease Transmission Models
Inferring Infectious Disease Transmission Rates from Outbreak Data
Prediction of Infectious Disease: Forecasting and Scenario Projection
