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 1:0 Aug
Instructor: Vijay Chandru (, Adjunct Faculty, BSSE
On-campus contact: G. K. Ananthasuresh (, Co-chair, BioSystems Science and Engineering (BSSE)
M-W from 10-11 am in the BSSE Meeting Room, 3rd Floor, Biological Sciences, IISc
First class: Aug. 9, 2017 10 am
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

Course Description:

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. Engineered systems that are built from and depend upon, the seamless integration of computational algorithms and physical components is how National Science Foundation defines the field of cyber physical systems (CPS). Digital Epidemiology can be viewed as a health care application of CPS.  The foundations of CPS includes a focus on the modeling of dynamic systems with attention to integrating computing, communication and control in uncertain and heterogeneous environments. Modeling paradigms include linear and non-linear, stochastic, discrete-event and hybrid models that are analyzed by methods of optimization, probability theory and dynamic programming. The purpose of this course is to introduce this emerging discipline of digital epidemiology to students at IISc. This offering of the course will be limited to a class size of 20 students.


Introduction to epidemiology;  statistical models and epidemiology with a lab component on outbreak analysis; Applications of models and physical components: a CPS perspective; Case Studies with a lab component; Data science and epidemiology, including precision medicine; lab component; Compartmental models in epidemiology; Spatial models – patch based, cellular automata; Network models for communicable diseases; Graphical dynamical systems models in epidemiology; A case study of dengue: models and measures of control; guest lectures on current research topics.


The only prerequisite for this course is a reasonable preparation in computational mathematics.


  • Epidemiology, A Very Short Introduction, Rodolfo Saracci, Oxford University Press
  • Statistical models in Epidemiology, D. Clayton and M. Hills, Oxford University Press
  • Statistical Methods in Epidemiology, the Environment and Clinical Trials, Halloran, M. Elizabeth, Berry, Donald
  • Marcel Salathé et al., Digital epidemiology, PLoS Computational Biology, 8(7), 2012.
  • M. Newman. The structure and function of complex networks. SIAM Review, 45, 2003.
  • F. Brauer, P. van den Driessche, and J. Wu, editors. Mathematical Epidemiology. Springer Verlag, Lecture Notes in Mathematics 1945.
  • R.M. Anderson and R.M. May. Infectious Diseases of Humans. Oxford University Press, Oxford, 1991
  • N. T. J. Bailey. The Mathematical Theory of Infectious Diseases and Its Applications. Hafner Press, New York, 1975.
  • M. Gersovitz and J. S. Hammer. Infectious diseases, public policy, and the marriage of economics and epidemiology. The World Bank Research Observer, 18(2):129–157, 2003.