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[CT080]

Session Date & Time : 2D (Aug.22, 15:30-17:10)
Type : Contributed Talk
Abstract : Using the most complete nationwide COVID-19 database, we estimated the key epidemiological distributions associated with COVID-19. We determined the best model to fit the data using a Bayesian model comparison, and estimated the model parameters at the region level using a hierarchical Bayesian model with partial pooling. We found that the COVID-19 pandemic in the Republic of Korea was characterized by relatively short onset-to-diagnosis and onset-to-report intervals but by long serial intervals.
Classification : 62F15
Author(s) :
- Eunha Shim (Department of Mathematics, Soongsil University, Republic of Korea)
- Wongyeong Choi (Department of Mathematics, Soongsil University, Republic of Korea)
- Youngji Song (Department of Mathematics, Soongsil University, Republic of Korea)

Session Date & Time : 2D (Aug.22, 15:30-17:10)
Type : Contributed Talk
Abstract : Car following (CF) models play an important role in traffic simulation software. Estimating their parameters is necessary to enhance predictive performance and is traditionally accomplished through optimisation. In this research, we adopted Bayesian inference which is advantageous for uncertainty quantification. As the CF model depends on its parameters through solution of a delay differential equation, the likelihood is analytically intractable so we employed an adaptive Markov chain Monte Carlo algorithm to sample from the posterior.
Classification : 62F15, 65Cxx
Author(s) :
- Samson Ting (The University of Western Australia)
- Michael Small (The University of Western Australia)
- Thomas Stemler (The University of Western Australia)
- Chao Sun (The University of Western Australia)
- Thomas Lymburn (The University of Western Australia)

Session Date & Time : 2D (Aug.22, 15:30-17:10)
Type : Contributed Talk
Abstract : The Fisher-KPP model is one of the simplest partial differential equation models exhibiting travelling wave behaviour, and has been widely used to model the growth and spread of populations in biology. When applying the model to experimental data, it is often tempting to generalize the model with additional parameters to obtain a better fit. However, this increase in model complexity also increases the difficulty of estimating the parameter values. In this study, we use a profile likelihood approach to investigate parameter identifiability in extensions of the Fisher-KPP model on both simulated data, and experimental data from a cell invasion assay. We focus on the effects of the forms of the kinetic terms, model misspecifications, and amount of data. We also quantify the amount of data required to justify a more complex model, and explore ways to design experiments to yield data more useful for parameter identification.
Classification : 62fxx, 62p10, 92cxx
Author(s) :
- Yue Liu (University of Oxford)
- Philip K Maini (University of Oxford)
- Ruth E Baker (University of Oxford)

Session Date & Time : 2D (Aug.22, 15:30-17:10)
Type : Contributed Talk
Abstract : The Paterson--Stockmeyer method is an evaluation scheme for matrix polynomials with scalar coefficients that arise in many state-of-the-art algorithms based on polynomial or rational approximants, for example, those for computing transcendental matrix functions. We derive a mixed-precision version of the Paterson--Stockmeyer method that can be faster and use less memory than its fixed-precision counterpart while delivering the same level of accuracy.
Classification : 65G50, 65F45, 65F60
Author(s) :
- Nicholas J. Higham (The University of Manchester)
- Xiaobo Liu (The University of Manchester)