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**Seminars will be held In Person unless otherwise noted**
Spring 2026
Seminar One
February 12, 2026
Griffin Floyd Hall, Room 100
Nikolay Bliznyuk
University of Florida
Title:
Efficient Bayesian Semiparametric Modeling for Spatio-Temporal Transmission of Multiple Pathogens
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 to help inform public health policy.
A major challenge in utilizing laboratory test data is that it is not available for every infected person. Appropriate imputation of the missing pathogen information often requires a prohibitive amount of computation. To address it, we extend our earlier hierarchical Bayesian multi-pathogen framework that uses a latent process to link the disease counts and the lab test data. Under the proposed model, imputation of the unknown pathogen-specific cases can be effectively avoided by exploiting the relationship between multinomial and Poisson distributions. A variable selection prior is used to identify the risk factors and their proper functional form respecting the linear-nonlinear hierarchy. The efficiency gains of the proposed model and the performance of the selection priors are examined through simulation studies and on a real data case study from hand, foot and mouth disease (HFMD) in China.
Seminar Two
February 26, 2026
Griffin-Floyd Hall, Room 100
Daniel Nevo
Harvard University
Title:
Causal Inference with Misspecified Network Interference Structure
Abstract:
A prominent assumption when studying treatment effects is the no-interference assumption, stating that treatment applied to one unit does not impact other units. However, interference — an umbrella term for spillover, contagion, peer effects, and related phenomena — is present in many settings. Relaxing the no-interference assumption is often accompanied by an assumed interference structure, commonly represented by a network. Various methods have been developed to address network interference under design-based, frequentist, or Bayesian perspectives.
A key assumption shared by many recently developed methods is that the network is given and correctly specified. We first discuss why this assumption might be violated in practice. Then, we will present the implications of violations of these assumptions and offer some solutions. To this end, we first focus on a design-based approach and derive bounds on the bias arising from estimating causal effects using a misspecified network. We show how the estimation bias grows with the divergence between the assumed and true networks, quantified through their induced exposure probabilities. To address this challenge, we propose a novel estimator that leverages multiple networks simultaneously and remains unbiased if at least one of the networks is correct, even when we do not know which one. If time permits, we will also discuss alternative solutions to related problems under varied probabilistic regimes.
Seminar Three–Cancelled
March 12, 2026
Griffin-Floyd Hall, Room 100
Hiya Banerjee
University of Georgia
Title:
TBD
Abstract:
TBD
Seminar Four
April 2, 2026
Griffin-Floyd Hall, Room 100
Bodhisattva Sen
Columbia University
Title:
TBD
Abstract:
TBD
Seminar Five
April 16, 2026
Griffin-Floyd Hall, Room 100
Joshua Cape
University of Wisconsin-Madison
Title:
TBD
Abstract:
TBD