Computational Epidemiology

Venue: TBA

Class Timings: TBA 

First Meeting: TBA

Syllabus:

Introduction to epidemiology; SIR modelling from the microscopic to the macroscopic, herd immunity; parameter fitting for SIR models; compartmental models (location, age, and disease compartments); clinical studies and disease biology; agent-based models (general description, network generation; contact tracing; transport modelling; calibration; validation);stages of the pandemic (pre-pandemic, mitigation and suppression, endemic stages) and associated modelling (non-pharmaceutical interventions, therapeutics, vaccinations); seroprevalence studies, sampling methods, biases, and how to handle them; optimal design of serosurveys; genomic epidemiology (sequencing, phylogenetic tree, surveillance); 

Prerequisites:

A reasonable background in computational mathematics. Introductory Statistics (can take a course that I am teaching at ISI concurrently at that time).

References:

• Saracci, R., 2010. Epidemiology, A Very Short Introduction. Oxford University Press.
• Clayton, D. and Hills, M., 2013. Statistical models in Epidemiology. Oxford University Press.
• Chakraborty A. K. and Shaw A., 2021. Viruses, Pandemics, and Immunity. A K Chakraborty and A Shaw, Illustrated by P J S Stork, MIT Press.
• Rothman, K.J., Greenland, S. and Lash, T.L., 2008. Modern epidemiology (Vol. 3). Philadelphia: Wolters Kluwer Health/Lippincott Williams & Wilkins.
• Published article

Outcomes: 

Students will learn the essentials of modelling epidemiology and conduct serological surveys.