Infectious disease dynamics in simple food webs: Dynamic consequences of prey-mediated transmission and infectiousness.
Paul Hurtado, PostDoc
Mathematical Biology Institute, Ohio State
Aug. 28, 2014
Abstract: Parasites are ubiquitous in nature. While it is known that host interactions with other species can affect the timing and progression of epidemics, the body of theoretical work that combines these host-parasite and trophic (e.g., predator-prey) dynamics is relatively underdeveloped. Recent empirical work suggests there are also direct dependencies between epidemiological and trophic-level processes, however existing models typically assume only indirect mechanisms, e.g., that host-parasite (epidemiological) processes and host trophic interactions only affect one another via their effects on population sizes.
In this talk, I'll introduce a family of three-species models (ODEs) -- of a predator population, their parasites, and their prey -- based on studies of Daphnia and their parasites. I'll describe how known "direct" dependencies between the rates of certain epidemiological processes and trophic interactions in this system require that we introduce additional nonlinearities into these models. Using analytical and computational results, I'll also describe the dynamics of these models, and assess the dynamic consequences of those dependencies by comparing the dynamics of models that include or omit those additional nonlinearities. Lastly, I'll explain how our analyses allowed us to greatly clarify how certain biological assumptions yield interesting dynamics in this and other three-species models.
ABC's @ NSF
Sastry Pantula, Dean
College of Science, Oregon State University
September 11, 2014
Abstract: FRG, RTG, MCTP, CDS&E, BIOMAPS, DMREF, several acronyms are used at NSF. In this talk, I will share some of the above and other funding opportunities for students and faculty at NSF. I will also discuss career and CAREER opportunities at NSF.
Multiperiod Integrated Chance Constraints in ALM for Pension Fund
Youssouf A.F. Toukourou
Department of Actuarial Science, Faculty of Business and Economics
University of Laussan UNIL-Dorigny, Switzerland
September 30, 2014
Abstract: The goal of an Asset Liability management (ALM) study of a pension fund is to determine the best asset allocation and/or contribution rate in order to generate high returns on investment and at the same time being able to cover obligations over a medium/long time horizon. For a specific plan, we develop a multi-stage stochastic linear programming model where one seeks to maximize the expected terminal wealth under solvency (i.e. funding ratio), policy, budget and portfolio constraints. The solvency constraints considered are a set of integrated chance constraints (ICCs) for which we distinguish separately short and long term risk approaches. As a starting point, we consider the one-year period risk ICC as presented in . As the pension fund ALM problem is a long term problem, we introduce the multiperiod risk ICC (MICC). A special mention is paid to the modeling of the MICC. The paper concludes with an example which illustrates the importance of implementing MICC in ALM for pension fund.
Increasing the number of Mathematics Majors
Dr. William Velez, Professor
University of Arizona
October 2, 2014
Abstract: In the late 1980's I began my efforts to increase the success rate of minorities in first semester calculus. The interventions that I devised were very time consuming and as the number of minority students increased, I could not manage that kind of effort. I developed my Calculus Minority Advising Program in an effort to meet with scores of minority students each semester. This program consists of a twenty-minute meeting with each student at the beginning of each semester. These meetings with students eventually transformed my own attitude about the importance of mathematics in their undergraduate curriculum.
I took over the position of Associate Head for Undergraduate Affairs in the department in 2003. I set a very modest goal for myself: to double the number of mathematics majors. With more than 600 mathematics majors, and about the same number of mathematics minors, I have reached that goal. I think the next doubling is going to be much harder to achieve. My work with minority students provided me with the tools to accept this new challenge of working with all students.
This talk will describe my own efforts to encourage ALL of our students that a mathematics major, or adding mathematics as a second major, is a great career choice. I will also describe the support that I have from the university and the department that enables me to carry out these tasks.
Title: Randomization Adapted to Continuous and Discrete Covariates in Clinical Trials
Fei Jiang, PostDoc
Department of Biostatistics, Harvard University
October 9, 2014
Abstract: Covariate balance among different treatment arms is critical to clinical trials, as known and unknown confounding effects can be eliminated when patients in different arms are alike. We propose a new dynamic allocation scheme to assign each patient to a treatment arm, such that the prognostic factors can be balanced across different arms. Our method
avoids the arbitrariness in the scoring scheme proposed by Pocock and Simon (1975), and instead provides a more objective covariate adaptive randomization procedure to balance the covariates or prognostic variables among treatment arms. Our approach does not need to classify continuous covariates into multiple categories, and it can handle both the continuous and discrete covariates naturally. We develop a statistical measure to characterize the similarity between the new patient and all existing patients in the trial. We study the theoretical properties of the proposed covariate adaptive randomization design, and conduct extensive simulation studies to examine its operating characteristics, which demonstrate its superior performance over other existing methods.
A Bayesian model for quantifying the change in mortality associated with future ozone exposures under climate change
Stacey Alexeeff, PostDoc
National Center for Atmospheric Research
October 16, 2014
Abstract: Climate change is expected to have many impacts on the environment, including changes in ozone concentrations at the surface level. A key public health concern is the potential increase in ozone-related summertime mortality if surface ozone concentrations rise in response to climate change. Previous health impact studies have not incorporated the variability of ozone into their prediction models. We propose a Bayesian posterior analysis and Monte Carlo estimation method for quantifying health effects of future ozone. The key features of our methodology are (i) the propagation of uncertainty in both the health effect and the ozone projections and (ii) use of the empirical distribution of the daily ozone projections to account for their variation. The use of interpolation to improve the accuracy of averaging over irregular shaped regions helps to derive average exposure for the regions where mortality and demographic information is reported. We also derive an analytic expression for the integral with respect to the mortality parameter, which is useful to reduce the Monte Carlo computational burden associated with this parameter. Using our proposed approach, we quantify the expected change in ozone-related summertime mortality in the contiguous United States between 2000 and 2050 under a changing climate. We also illustrate the results when using a common technique in previous work that averages ozone to reduce the size of the data, and contrast these findings with our own.