Graph and Network Seminar Series Featuring Nicholas Landry

Modeling contagion processes with noisy and uncertain networks

Accurately modeling the spread of contagion requires accurate models of how people interact and how diseases and ideologies are transmitted. This can be a challenge because both the underlying network structure and the contagion dynamics can only be measured indirectly through observations. I will describe the limitations of obtaining networks from observational data and traditional methods that are used. In addition, I will discuss the implications that network modeling choices, both ontological and representational, have on the resulting networks. I will use this discussion as a springboard for discuss three projects that leverage the modeling and inference of complex networks to describe the spread of contagion. First, I will talk about the effectiveness of inferring a contact network from time-series data derived from both simple and complex contagion processes. I will conclude by talking about the role of network structure in the spread of hospital-acquired infections and the effective screening of malaria in Guyana.
 

RSVP