Data-driven modeling of COVID-19
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Course Description
How to design computational tools to understand the dynamics of the COVID-19 pandemic. Emphasis on mathematical epidemiology, infectious disease models, concepts of effective reproduction number and herd immunity, network modeling, outbreak dynamics and outbreak control, Bayesian methods, model calibration and validation, prediction and uncertaintly quantification; Projects on statistic or mechanistic modeling of COVID-19.
Grading Basis
ROP - Letter or Credit/No Credit
Min
3
Max
3
Course Repeatable for Degree Credit?
No
Course Component
Lecture
Enrollment Optional?
No
Does this course satisfy the University Language Requirement?
No
Programs
ME233
is a
completion requirement
for: