Digital Epidemiology

Epidemiology is the study of health and disease in populations. Google’s Flu Trends, Flowminder, Healthmap, Biodiaspora are several examples of digital epidemiology already in play.

“In more traditional epidemiological studies, data might be gathered or generated by manual means, such as in-person interviews, voluntary surveys or other types of deliberate, purposeful data collection. Nowadays, researchers and computer scientists are turning to novel sources of data — including cellphone records, blog posts, Tweets and flight data – to draw new, highly experimental, though sometimes questionable, inferences about the world at large.”


– Mathew Braga
The rise of the digital epidemiologist: Using big data to track outbreaks and disasters

A new experimental course in Digital Epidemiology is being offered by the BioSystems Science and Engineering Department at Indian Institute of Science.

Subject: BE 209 Digital Epidemiology 1:0 October 2020
Instructor: Vijay Chandru (, Adjunct Faculty, BSSE
On-campus contact: Narendra Dixit (, Chair, BioSystems Science and Engineering (BSSE)
M-W from 11-12 (Online on Microsoft Teams)
First class: October 5th 2020
The course is limited to 20 students on a first-cum-first-served basis.

Lecture Slides: Download Lecture slides here.

Course Readings: Download/Read Digital Epidemiology Course readings here


Epidemiology is the study of health and disease in populations. The sudden and savage nature of the ongoing COVID-19 pandemic has certainly caught everyone’s attention. The fact that it has happened when the globe is so well connected thanks to information technology has made epidemiologists of just about anyone who has some mathematical ability and appreciation of infectious disease dynamics. However, there are some serious mathematicians and data scientists who have been interested in the power of computational epidemiology in counterfactual reasoning and in the predictive power of data driven models. The prediction by the Global Virome Project that we could have around three zoonotic episodes a year that would have pandemic potential implies that we do need the best minds to help us prepare for the next one. Previous course offerings are archived at the website


Introduction to epidemiology; SIR modelling, from the microscopic to the macroscopic, herd immunity; Compartment models (location compartments, age compartments, disease stage compartments), impact on herd immunity, social distancing, masks; Parameter fitting for SIR models; Clinical studies and disease biology. Agent-based models - general description, network generation and computational aspects, contact tracing, transport, calibration, validation. Data-driven and mathematical modeling for response is going to be specific to the stages of a pandemic – pre-pandemic, acceleration, mitigation, suppression and post-pandemic (peace time).


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


  • Viruses, Pandemics, and Immunity, A K Chakraborty and A Shaw, Illustrated by P J S Stork, MIT Press 2020
  • Epidemiology, A Very Short Introduction, Rodolfo Saracci, Oxford University Press, 2010
  • Statistical models in Epidemiology, D. Clayton and M. Hills, Oxford University Press, 2013
  • Data-driven modeling for different stages of pandemic response
  • Aniruddha Adiga, Jiangzhuo Chen, Madhav Marathe, Henning Mortveit, Srinivasan Venkatramanan, Anil Vullikanti, September 2020
  • City-Scale Agent-Based Simulators for the Study of Non-Pharmaceutical Interventions in the Context of the COVID-19 Epidemic

Course TA:

Ameya Dravid