BE 218 : Computational Epidemiology (3:1)

Instructor

Rajesh Sundaresan (ECE and RBCCPS, IISc)

Teaching Assistants

S Tejashree
Sudhanshu Bharadwaj

Lecture Hours

Lectures: Tuesdays and Thursdays, 08:30 - 10:00 hrs (Fixed)
Location: MP20 (Microelectronics and Photonics Building of the ECE Department)
First class: Tuesday 08:30 - 10:00 hrs 10 January 2024, at MP20

Office Hours

Mondays 6:00 - 7:00 pm, at MP20

Examinations

Mid-term: Thurssday 29 February 2024, 08:30 - 10:00 hrs
Final: Afternoon, 14:00 - 17:00 hrs, 25 April 2024 -- as set by the Academic Section

Course 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); miscellaneous topics (mobility modelling, communication about the pandemic, behavioural changes and modelling, contact tracing apps, testing logistics, workplace readiness).

Course Description

Epidemiology is the study of health and disease in populations. The sudden and savage nature of the COVID-19 pandemic has highlighted how connected we are and how an initial infection can spread quickly and assume pandemic proportions. Digital and computational technologies have evolved significantly in the last few decades. They have enabled us to gather and analyse data in near-real-time thereby helping us understand, track, and manage the pandemic. Moreover, advances in computational epidemiology have provided us with sophisticated tools for modelling the pandemic and the impact of various interventions. Given that the Global Virome Project has predicted that we could have around three zoonotic episodes a year that would have pandemic potential, we need to train our best minds to help us prepare against the next pandemic. This aim of this course is to do just that.

Prerequisites

• The only prerequisite for this course is a reasonable preparation in computational mathematics – modelling and analysis.

Course Grade

• 30/100 : Mid-term examination
  
• 20/100 : Assignments pre mid-term
  
• 20/100 : Assignments post mid-term
  
• 30/100 : Final examination
  

Reference Texts

• 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 articles.