Statistical inference for infectious disease modeling
We discuss two recent results concerning disease modeling on networks. The infection is assumed to spread via contagion (e.g., transmission over the edges of an underlying network). In the first scenario, we observe the infection status of individuals at a particular time instance and the goal is to identify a confidence set of nodes that contain the source of the infection with high probability. We show that when the underlying graph is a tree with certain regularity properties and the structure of the graph is known, confidence sets may be constructed with cardinality independent of the size of the infection set. In the scenario, the goal is to infer the network structure of the underlying graph based on knowledge of the infected individuals. We develop a hypothesis test based on permutation testing, and describe a sufficient condition for the validity of the hypothesis test based on automorphism groups of the graphs involved in the hypothesis test.
This is joint work with Justin Khim (UPenn).
Bio:
Po-Ling Loh is an assistant professor in the ECE department at the UW-Madison, with a secondary appointment in the statistics, computer science, and industrial and systems engineering departments. From 2014-2016, Po-Ling was an assistant professor in the statistics department at the Wharton School at the University of Pennsylvania. Po-Ling received an M.S. in computer science and a Ph.D. in statistics from Berkeley in 2013 and 2014, and a B.S. in math with a minor in English from Caltech in 2009. She was the recipient of the 2014 Erich L. Lehmann Citation from the Berkeley statistics department for an outstanding Ph.D. dissertation in theoretical statistics and a best paper award at the NIPS conference in 2012. Po-Ling is a recipient of an NSF CAREER award in statistics.
