BE 218 : Computational Epidemiology (3:1)
Vijay Chandru (Adjunct Professor BSSE, IISc) and Rajesh Sundaresan (ECE and RBCCPS, IISc)
Lectures: Mondays, Wednesday, Fridays, 10:00 - 11:00 hrs
Location: Online in January 2022, thereafter MP 30 (Microelectronics and Photonics Building of the ECE Department)
First class: Wednesday 10:00 - 11:00 hrs, 05 January 2022.
Mid-term Monday 28 February 2022 (10:00 - 11:00 hrs)
Final Wedensday 20 April 2022 (9:00 - 12:00 hrs)
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; miscellaneous topics (mobility modelling, communication about the pandemic, behavioural changes and modelling, contact tracing apps, testing logistics, workplace readiness).
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.
- The only prerequisite for this course is a reasonable preparation in computational mathematics – modelling and analysis.
This is a 3:1 course. Assignments will carry a significant weight.
- 30/100 : Mid-term examination
- 40/100 : Assignments
- 30/100 : Final examination
- 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.