Interventions and Calibration
Master advanced infectious disease modeling with focus on intervention analysis and model calibration using real-world data.
Master advanced infectious disease modeling with focus on intervention analysis and model calibration using real-world data.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Infectious Disease Modelling Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
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What you'll learn
Model vaccination and treatment interventions in SIR framework
Calibrate models against epidemiological data
Implement computer-based calibration techniques
Perform least-squares and maximum-likelihood estimation
Evaluate model fit and parameter selection
Skills you'll gain
This course includes:
0.78 Hours PreRecorded video
25 ungraded labs, 2 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This advanced course focuses on incorporating interventions into SIR models and calibrating them against real-world data. Students learn to model treatment and vaccination effects, perform manual and computer-based model calibration, and use both least-squares and maximum-likelihood approaches. The course emphasizes practical implementation in R programming, with extensive hands-on exercises in model parameter adjustment and goodness-of-fit optimization.
Modelling Interventions
Module 1 · 10 Hours to complete
Confronting Models with Data - Part A
Module 2 · 3 Hours to complete
Confronting Models with Data - Part B
Module 3 · 4 Hours to complete
Confronting Models with Data - Part C
Module 4 · 6 Hours to complete
Fee Structure
Instructor
Innovator in Mathematical Epidemiology
Dr. Nimalan Arinaminpathy is a Reader in Mathematical Epidemiology at Imperial College London's Faculty of Medicine, specifically within the School of Public Health. His research focuses on applying mathematical and statistical tools to understand the transmission dynamics of infectious diseases, with a particular emphasis on tuberculosis and public health policy. Dr. Arinaminpathy's work bridges the gap between basic scientific research and practical applications, aiming to inform and enhance public health strategies through rigorous modeling.
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