Networks, Modeling, and Public Health Decision Making

5:00–7:00 pm
UChicago Center in Delhi


Public health decision making is a complex process, involving input from clinicians, epidemiologists, biologists, funding agencies and high-risk populations. Often, optimal use of these inputs and available resources are not obvious, and setting realistic prevention goals become separated from rigorous scientific methods. One reason for these difficulties is public health decision making does not lend itself easily to a physical laboratory-based setting, where optimal approaches can be assessed. The ethical and logistical challenges are immense, input parameters can sometimes conflict, and the scientific method constrained.

Mathematical models provide one powerful approach to marry decision making with scientific rigor. The computer is a mathematical modeler’s laboratory, where different inputs can be synthesized, and scenarios experimented with. Constructing and validating good models remains a challenge, but as data science, the study of networks and computing have all progressed, making to possible to harness these methods to rigorously assess prevention targets, and optimal use of existing resources. Some of the data and network collection procedures that go into modeling approaches were discussed at this talk.  Additionally, mathematical modeling approaches that are influencing decision making, and ways forward to improve prevention of HIV among men at high-risk for HIV in India, including novel biomedical prevention were also discussed.


John Schneider MD, MPH
Associate Professor of Medicine and Epidemiology
Director of Global Health Programs
Departments of Medicine and Public Health Sciences
University of Chicago, Chicago, USA

Aditya Khanna PhD
Staff Scientist
Director of Network Modeling
Chicago Center for HIV Elimination
University of Chicago, Chicago, USA