Mathematical models shed light on the dynamics of infectious diseases and consequently inform public health policies. The last decade has seen an emergence of multiscale disease models linking the number of viral particles (within-host) to transmission rates (between-host). In this talk, we propose a simple dynamical system along this line that accounts for immune response. We characterize stability with a basic reproduction number (R0) for each of the within- and between-host scales. Our analysis culminates in a function that relates the two reproduction numbers.
FIGSS Seminar - winter semester 17/18
For the first time scientists have measured gravitational waves from a merging binary system of neutron stars. Remarkably, this event was also accompanied by a short gamma-ray burst and an optical observation (kilonova). In this seminar I will show how all the data from this event GW170817 can be combined with universal relations - relations between dimensionless neutron star parameters that make all these stars "look alike" - in order to set a tight constraint on the maximum mass of nonrotating neutron star models. This has immediate impact on the equation of state of nuclear matter, as any such equation that yields a maximum mass above the new limit can be ruled out.
In many machine learning tasks, we have often to deal with very complex and high dimensional data. Many of the dimensions can be redundant and our goal can be to take care of this redundancy by learning a low dimensional manifold in the data-space. Many machine learning models have been proposed to address this problem and are known as Latent Variable Models (LVMs). A well known algorithm for that purpose is the Principle Component Analysis (PCA), which tries to find a linear manifold that captures the data and minimize the reconstruction error. Keeping the number of latent dimensions fixed one can even further decrease the reconstruction error by allowing for non-linear manifolds. In this talk I am introducing a framework called Gaussian Process Latent Variable Model (GP-LVM), which is well applicable to non-linear dimensionality reduction. It is a combination of LVMs and Bayesian non-parametric Gaussian Processes (GPs). After a short introduction to GPs and LVMs, I will give an intuitive interpretation to GP-LVMs and talk about their application in real-world problems especially in finance for portfolio optimization.
During the last decades, the complex intercommunication between the neuroendocrine and the immune (NEI) system and its regulatory role in homeostasis in the human body have been extensively studied. Dysfunctions in any of these systems and their interconnections or disruption of the circadian behavior of the signaling molecules can result in imbalances in homeostatic mechanisms and might be one of the risk factors in the pathogenesis of auto-inflammatory disorders such as rheumatoid arthritis (RA). Due to the nonlinear behavior of the NEI system, understanding its circadian dynamic is complex. Therefore, we employed the descriptive and predictive power of mathematical modeling to investigate our hypothesis. This study provides an interdisciplinary framework to investigate the complex NEI system, for a deeper understanding of how it is functioning and for determination of causative agents of NEI dysfunction.
The threat of infectious diseases epidemics has persisted throughout the human history---and it still does today. An epidemic can set human developments back to decades but their crisis nature often leaves scientists no better option than learning from the past. Confronting an ongoing epidemic, however, requires swift responses and thus the abilities to evaluate quickly potential epidemic outcomes. As such, simulations of epidemic models hold the potential as the first-aid toolbox for decision making amid the crisis. In the linger threats of potential upcoming epidemics, this talk presents a promising approach towards holistic understanding of epidemic processes, fostering the use and exploration of within-host infection dynamics in epidemic assessments, heightening our epidemic preparedness capability.
Throughout history, we have witnessed alarming high death tolls derived from infectious diseases around the globe.
One of the deadliest natural disasters in human history was caused by a viral infection, the 1918 flu pandemic, which killed approximately 50 million people.
Infectious diseases are latent threats to humankind - killing annually 16 million people worldwide.
The magnitude of the threat represented by emerging virus diseases is immense, for example, HIV/AIDS characterized in the early 1980s has resulted in more than 30 million deaths from all socio-economic backgrounds.
Furthermore, re-emerging viruses like Ebola in 2014 and Zika in 2016 have baffled us with their threat to humans and health care systems around the globe. The consequences of epidemics can be devastating, infecting not only thousands of people but also animal populations and the food chain. Our research group seeks new avenues for holistic understanding against infections and disease transmission at the meeting point of mathematics, infection biology, immunology and epidemiology. In this talk, some examples and open problems of our research will be discussed to welcome collaboration and ideas from our FIAS/FIGSS colleagues.