Date/Time
Date(s) - 24/01/2025
3:30 pm - 4:30 pm
Location: Hamilton Hall, Room 305
Speaker: Richard Zhao
Title: Edge-based Modeling for Disease Transmission on Random Graphs – an Application to Mitigate a Syphilis Outbreak
Abstract: Edge-based random network models, especially those based on bond percolation methods, can be used to model disease transmission on complex networks and accommodate social heterogeneity while keeping tractability. Here we present an application of an edge-based network model to the spread of syphilis in the Kingston, Frontenac and Lennox & Addington (KFL&A) region of Southeastern Ontario, Canada. We compared the results of using a network-based susceptible-infectious-recovered (SIR) model to those generated from using a traditional mass action SIR model. We found that the network model yields very different predictions, including a much lower estimate of the final epidemic size. We also used the network model to estimate the potential impact of introducing a rapid syphilis point of care test (POCT) and treatment intervention strategy that has recently been implemented by the public health unit to mitigate syphilis transmission.
Speaker: Kursat Sozer
Title: Quantum Invariants and Their Generalizations
Abstract: Topological Quantum Field Theories (TQFTs) provide a framework connecting quantum topology with representation theory. These theories have become central to understanding quantum invariants in low-dimensional topology. In this talk, I will introduce these theories and present recent work with Alexis Virelizier extending these ideas to Homotopy Quantum Field Theories (HQFTs). Our construction provides new state-sum invariants while unifying several previously studied quantum invariants in the HQFT setting.
Speaker: Eman Alamer
Title: Clustering Contaminated Mixed-Type Data
Abstract: Many real data are considered mixed-type, where two or more types of variables are in the same data. Moreover, real data is usually contaminated with noise or outlier observations. In the cluster paradigm, more work needs to be done to model this type of data, especially contaminated mixed-type data. Herein, a mixture of contaminated mixed-type distributions is proposed to identify “outliers” or atypical points in mixed-type data. The contaminated Gaussian distribution is used along with latent models to develop the proposed model. An expectation-maximization algorithm is used to estimate the model parameters. Real and simulated data are used for illustration.
Coffee will be served in the same room, HH 305 at 3:00pm. All are welcome.