Date(s) - November 19, 2021
11:45 am - 12:35 pm
Nikolay Bliznyuk, Ph.D.
Associate Professor of Statistics
University of Florida
Title: Hierarchical Bayesian Spatio-Temporal Modeling for Multi-Pathogen Transmission of Hand, Foot, and Mouth Disease
Abstract: Mathematical modeling of infectious diseases plays an important role in the development and evaluation of intervention plans. These plans, such as the development of vaccines, are usually pathogen-specific, but laboratory confirmation of all pathogen-specific infections is rarely available. If an epidemic is a consequence of co-circulation of several pathogens, it is desirable to jointly model these pathogens in order to study the transmissibility of the disease. Our work is motivated by the hand, foot and mouth disease (HFMD) surveillance data in China. We build a hierarchical Bayesian multi-pathogen model by using a latent process to link the disease counts and the lab test data. Our model explicitly accounts for spatio-temporal disease patterns. The inference is carried out by an MCMC algorithm. We study operating characteristics of the algorithm on simulated data and apply it to the HFMD in China data set.
About Nikolay Bliznyuk, Ph.D.
Dr. Nikolay Bliznyuk is an Associate Professor of Statistics at the University of Florida, with appointments in the Departments of Agricultural & Biological Engineering (main), Biostatistics, Statistics and Electrical & Computer Engineering (affiliate/courtesy). He earned his doctoral degree in Operations Research & Information Engineering from Cornell University in 2008, concentrating in computational statistics. Prior to joining UF in 2011 as a tenure-track Assistant Professor in the Department of Statistics, he held a postdoctoral researcher appointment in the Department of Biostatistics at the Harvard University and a research assistant professor in the Department of Statistics at Texas A&M University. His research has four tightly intertwined themes: (i) hierarchical Bayesian modeling strategies (ii) spatio-temporal modeling, (iii) methodology and applications of statistical (machine) learning, and (iv) computationally expensive inverse problems (also known as Bayesian calibration of computer models).
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Meeting ID: 967 1217 5263