Parameter and State Estimation Based on Observer Construction Method for Epidemiological Models: Cholera Dynamics with Threshold Immunity

Abstract: It is often impossible to measure all states and parameters affecting spread of a disease. Our interest lies in estimating such states and the parameters catalyzing the spread using a method from dynamical systems based on construction of an observer. Particularly, cholera transmission dynamics in which asymptomatic and cholera pathogen densities are not practically measurable despite playing a big role in its transmission. They are referred to as inaccessible states of the model and can only be manipulated using the measurable states of the given model. We formulated a mathematical model for cholera dynamics threshold immunity. A method based on observer (from modern control theory) is proposed to estimate the state variables not accessible to measurement and the time
dependent parameters from real data. An auxiliary system is used, an observer whose solutions converge exponentially to those of an original system and solely utilizes known inputs and output of the model. The system together with the observer designed is detectable but is not observable. We derive the expressions for time dependent infection rate, induced cholera death rate and symptomatic recovery rate and their estimations done using real data. The observer delivered estimates reflect a close trend already ascertained by other researchers. Numerical simulations are then performed for the validation of estimation results. We have analytically showed and numerically confirmed the exponential convergence to zero of the estimation errors resulting from the observer model hence the high quality of the estimates.

The link to the video of the talk is LIAM Meeting - Zoom

Presentation by Dr. Woldegebriel Assefa Woldegerima
York University

Date: December 10, 2021
Time: 12:30-1:30 PM
Location: Zoom – link: https://yorku.zoom.us/j/96643509945

Spatial modeling of individual-level infectious disease transmission

Abstract: Geographically-dependent individual level models (GD-ILMs) are a class of statistical models that can be used to study the spread of infectious disease through a population in discrete-time in which covariates can be measured both at individual and area levels. The typical ILMs to illustrate spatial data are based on the distance between susceptible and infectious individuals. A key feature of GD-ILMs is that they take into account the spatial location of the individuals in addition to the distance between susceptible and infectious individuals. In this talk, we propose a GD-ILM for tuberculosis (TB) data analysis which is an infectious disease that can be transmitted through individuals. It is known that certain areas/demographics/communities have higher prevalence of TB. It is also of interest of policy makers to identify those areas with higher infectivity rate of TB for possible preventions. Therefore, we need to analyze this data properly to address those concerns. The Expectation Conditional Maximization algorithm is proposed for estimating the parameters of GD-ILMs to be able to predict the areas with the highest average infectivity rates of TB. We also evaluate the performance of our proposed approach through some simulations.

The link to the video of the talk is LIAM Meeting - Zoom

Presentation by Dr. Leila Amiri
York University

Date: December 3, 2021
Time: 12:30-1:30 PM
Location: Zoom – link: https://yorku.zoom.us/j/96643509945

Assessing the combined Impact of Interventions on the HIV and Syphilis epidemics among gbMSM: a co-interaction model

Abstract: The majority of HIV and infectious syphilis cases (over 80% of all infectious syphilis cases) in British Columbia (BC) were among gay, bisexual and other men who have sex with men (gbMSM). A recent study carried out in a setting where the uptake of pre-exposure prophylaxis (PrEP) is moderate, the authors revealed that the risk of acquiring bacterial sexually transmitted infections (STIs) increases among gbMSM following initiation of PrEP. We therefore developed a mathematical transmission model to assess the impact of different interventions, especially PrEP on HIV and syphilis infections, and showed how the combination of testing and treating syphilis, HIV treatment as prevention (TasP), condom use and PrEP uptake could eliminate both HIV and syphilis epidemics among gbMSM in BC over ten years.
Our findings highlighted how increasing the number of susceptible gbMSM on PrEP can create unexpected negative impact on syphilis incidence, and show the importance of public health policies to addressing the co-interaction of HIV with syphilis, and with other STIs among gbMSM in BC and in other similar settings.
How our methods could be used to assess the combined impact of interventions on the current COVID-19 epidemic, and malaria transmission in low- and middle-income countries (LMIC) will be briefly discussed.

Presentation by Dr. Jummy Funke David
York University

Date: November 26, 2021
Time: 12:30-1:30 PM
Location: Zoom – link: https://yorku.zoom.us/j/96643509945

Coupling epidemiology with mathematical modeling, bioinformatics, and biophysics to predict drug mechanisms

Abstract: The still ongoing COVID-19 pandemic has shown how challenging it can be to design a new molecular entity (NME)/new chemical entity (NCE), especially in case of time constraints and stringent deadlines. From a historical standpoint, the development of NME/NCE has generally relied on serendipity until the emergence of combinatorial and high-throughput screening-assisted chemistry.
Usually, disciplines like pharmacoepidemiology, pharmacovigilance, big data analytics, and bioinformatics/ biophysics have rarely interacted.
In the present talk, we show how to take advantage of the largest pharmacoepidemiology and pharmacovigilance database, VigiBaseTM, the global pharmacovigilance database developed and maintained by the World Health Organization (WHO) Collaborating Centre for International Drug Monitoring, named as the Uppsala Monitoring Centre (UMC). VigiBaseTM contains more than 20 million individual case safety reports (ICSRs) of suspected adverse drug reactions (ADRs), spontaneously forwarded by more than 140 countries that are members of the WHO’s Programme for International Drug Monitoring. We here implement an algorithm that couples cutting-edge, artificial intelligence-enhanced disproportionality analysis (Bayesian Confidence Propagation Neural Network or BCPNN) with bioinformatics, to provide molecular insights on drug mechanisms and the insurgence of their side-effects, as well as to repurpose existing molecular/chemical entities and facilitate drug discovery

Presentation by Dr. Nicola Luigi Bragazzi
York University

Date: November 19, 2021
Time: 12:30-1:30 PM
Location: Zoom – link: https://yorku.zoom.us/j/96643509945

Rotating Waves of Influenza Virus

Abstract: Genetic mutations play an important role in the evolution and spread of Influenza virus. These genetic variations can lead to the emergence of new variants with selection of phenotypes increasing viral fitness (replication, transmissibility, immune escape). The human influenza A has caused four pandemics in the past 100 years and continues to kill tens of thousands of people annually posing a significant and persistent threat to the global public health. In this study (which is in progress), we have used a SIS type of disease transmission model by including mutation in the strain/variant space of the virus. We are interested in looking for the existence of rotating waves of influenza if and when they exist. Basically, our goal is to show that the same influenza strain can come back after some time.

Presentation by Bushra Majeed
York University

Date: November 5, 2021
Time: 12:30-1:30 PM
Location: Zoom – link: https://yorku.zoom.us/j/96643509945

State-of-the-art methods coupling transmission
models and deep learning: a brief introduction

Abstract: Transmission models have proved to be power tool in guiding public health measures in epidemic control. Nonetheless, modeling emerging infectious remains a challenge due to the unknown mechanisms in transmission dynamics, such as nonstandard incidence rate, changing human mobility pattern, shifting contact matrix,
evolution of virus.
Neural networks, though called as black-box uniform approximator and difficult to interpret, have an unreasonable effectiveness in learning unknown mechanisms with bless of dimensionality, and have lots of application. In this talk, I will briefly introduce some methods coupling transmission models and neural networks including universal differential equation (embed neural networks in transmission dynamics) and symbolic regression. Universal differential equation is a knowledge-based and deep learning based method with unreasonable learning ability and a good interpretability.
If we still have time, I will introduce another two methods: physical informed neural network (using neural network to solve forward and inverse problems of differential equation at the meantime); Data driven methods such as sparse identification of nonlinear dynamical systems (SINDys)

Presentation by Dr. Pengfei Song
York University

Date: October 29, 2021
Time: 12:30-1:30 PM
Location: Zoom – link: https://yorku.zoom.us/j/96643509945

Functional differential equations

Abstract: I will talk about my current work on functional differential equations. Specifically, I will present some results related to asymptotic behavior of global solutions for abstract differential equations with state-dependent delay and I will talk about some goals related to neutral differential equations and traveling wave solutions for a viral model.

Presentation by Dr. Denis Fernandes
York University

Date: October 8, 2021
Time: 12:30-1:10 PM
Location: Zoom – link: https://yorku.zoom.us/j/96643509945

Analysis and Dataset of Factors Influencing COVID-19 NPI Decisions across Canadian Post-Secondary Institutions

Abstract: Since January 2020, nonpharmaceutical interventions (NPIs) have consistently been recommended by Canadian public health authorities as one of the primary strategies to mitigate the spread of COVID-19. While research by Cevasco et al. and other groups has been conducted to examine factors influencing the timing of NPI decisions across American universities, similar studies have yet not been done to analyze factors influencing the response of Canadian universities to the dynamic nature of the ongoing COVID-19 pandemic. This study seeks to investigate the factors affecting timing of NPI decisions and community support measures implemented by Canadian post-secondary institutions in response to COVID19, during the period of January 1, 2020 – September 30, 2021. Additionally, this data is compiled into an original dataset.
In RStudio version 4.0.3, Cox proportional hazards modelling (coxph function in survival package) is being used to analyze timing to NPI decisions and implementation of COVID-19 support measures for students and employees. Hazard ratios are used to represent how much of an effect each covariate has on timing of NPI decisions. NPI decisions of interest are when Canadian post-secondary institutions: shifted to remote work for faculty/staff; closed campus to non-essential personnel; suspended classes; transitioned to online learning; discouraged on-campus housing; cancelled international travel; enforced personal protective equipment; limited access to library services; and increased campus sanitation protocol. Community support measures of interest include: distribution of personal protective equipment to students or employees; introduction of new emergency financial support; announcements emphasizing available mental health resources; and measures to support access to technology for remote teaching or learning. The covariates studied are university governance, campus setting, religious affiliation, health infrastructure, faculty diversity, and student demographics.

Presentation by Taylor Cargill
York University

Date: September 24, 2021
Time: 1:00-1:30 PM
Location: Zoom – link: https://yorku.zoom.us/j/96643509945

Comparing Public Sentiment towards COVID-19 Vaccines across Canadian Cities: Analysis of Comments on Reddit

Abstract: Social media enables the rapid consumption of news related to COVID-19, and serves as a platform for discussions. The aim of this study is to use location-based subreddits (Vancouver, Calgary, and Toronto) on Reddit to study city-level variations in sentiments towards vaccine-related topics. Our work demonstrates that data from social media can be used to better understand concerns and sentiments surrounding the pandemic at the local level, which enables more targeted and publicly-acceptable policies.

Presentation by Cathy Yan
York University

Date: August 27, 2021
Time: 9:30-10:30 AM
Location: Zoom – link: https://yorku.zoom.us/j/96643509945

Determinants of COVID-19 vaccine acceptance in 162 countries around the world

Abstract: After COVID-19 pandemic spread across the world, countries adopted containment measures such as social distancing and lockdown strategies to stop the spread and overwhelming of hospitals, which negatively impacted the economies and the lives of their citizens. A COVID-19 vaccine provides a viable possibility for a permanent solution to control the pandemic. However, the uptake rate has become stagnant in several countries despite several appeals by the media policy makers, and community leaders and is dependent on different sociodemographic factors.

Presentation by Mehrdad Kazemi
York University

Date: August 27, 2021
Time: 9:30-10:30 AM
Location: Zoom – link: https://yorku.zoom.us/j/96643509945

Covid-19 Contact Rate Prediction using Multi-Architectural Deep Learning Networks

Abstract: As Covid-19 spread around the globe in early 2020, the world was caught off guard, trying to implement policies and measures as to combat the virus. Daily case numbers was a leading indictor of the success or failure of nations in their efforts to minimize the damage. Models also heavily relied on these data points as to fit, study and predict the dynamics of the outbreaks. A central variable of such models is the contact rate, measuring average
interactions in the populations. Predicting such a variable is difficult, as it is constantly changing, prone to large variations caused by small errors in measurements, requires longer time periods of data, and cannot be measured directly itself. We utilize a data simulation framework to generate many different realizations of an outbreak throughout Ontario, based on random protective policy implementations. Using common neural network
structures, we train a multi-architectural network that is capable of predicting contact rates on the daily scale, using the simulated data. The network is then validated on the Ontario daily case numbers in 2020, providing results inline with other publications, while displaying new interesting phenomena. Such methodology can easily be extended to other regions and municipalities, and be used to estimate other parameters, relevant to behavioural changes, public health interventions, and social-economic activities, that are changing over the course of an outbreak, by adjusting relevant values and targets in the data preparation step.

‎Presentation by Svetozar Zarko Valtchev
LIAM, York University

Date: July 16, 2021
Time: 9:30-10:30 AM
Location: Zoom – link: https://yorku.zoom.us/j/96643509945

Control and Statistical Analysis of Covid-19 in York University and Ontario

Abstract: Optimal control theory is applied to the York University setting to find an optimal opening strategy to maximize in-person hours. Basic statistical analysis is performed on Ontario Google mobility data for predictions in COVID and VOC incidence. A novel approach to topic modeling of the virus through Wikipedia and article links is also presented.

‎Presentation by Colin Pierce
LIAM, York University

Date: May 21, 2021
Time: 9:30-10:00 AM
Location: Zoom – link: https://yorku.zoom.us/j/96643509945

Disease Models and Economic Incentives

Abstract: This talk presents an overview of a compartmental economic epidemiological model with vaccination. We make a case of how individual behavioral responses to disease transmission impacts disease prevalence. The talk will also present my current proposed project with the IDRC Research Team.

Presentation by Wisdom Stallone Avusuglo
IDRC, York University

Date: May 14, 2021
Time: 9:30-10:30 AM
Location: Zoom – link: https://yorku.zoom.us/j/96643509945

An age-structured model for pertussis transmission with multiple infections studying the effects of childhood DTaP and adolescent Tdap vaccines

Abstract: An age-structured deterministic model with multiple infections that accounts for decaying maternal antibody, waning infection-acquired and vaccine-induced immunity is formulated to study the transmission dynamics of pertussis and the effect of childhood DTaP and adolescent Tdap vaccines. The expression of the reproduction number R is derived for the ODE model in the case of proportionate mixing. Estimated age-dependent transmission probability and empirical contact data are used in the simulation of the ODE model from which the basic reproduction number R0 is estimated to be around 15. The combination of DTaP and Tdap vaccines fails to bring R0 under one and thus pertussis remains endemic despite sustained high coverage of vaccination. While both DTaP and Tdap vaccines have remarkable effect on reducing the incidences of the age groups being directly vaccinated, the adolescent booster dose Tdap is also found to provide some indirect protection for infants though very limited (< 5% incidence).

Presentation by Dr. Qing Han
LIAM, York University

Date: April 16, 2021
Time: 9:00-10:00 AM
Location: Zoom – link: https: //yorku.zoom.us/j/4167365757

Unfolding determinants of COVID-19 vaccine acceptance in China

Abstract: The research to be presented was conducted before the availability of COVID-19 vaccines for emeregency use. At the time, China was at the forefront of global efforts to develop COVID-19 vaccines and had five fast-tracked candidates at the final-stage, large-scale human clinical trials testing phase. Vaccine-promoting policymaking for public engagement is a prerequisite for social mobilization. However, making an informed and judicious choice is a dilemma for Chinese policymaking in the vaccine promotion context. Through a network dynamics model, we found three topics that affect mass vaccination rollout: vaccine prices, side effects, and types. The choice of a vaccine by an ordinary Chinese is subject to vaccine pricing, and policymaking therefore should take the price factor into consideration. Our survey also finds that many netizens have wrong views on the side effects of the vaccine, however there are also many netizens who spread the correct views through the Internet. Policymaking can guide the netizens for a smooth vaccination rollout. We also found that netizens have different levels of attention to different vaccine names. The Chinese meaning of "inactivated", for example, may mislead people's understanding of vaccine safety. The government needs to popularize science in time to eliminate possible vaccine hesitation. This study applied tailed models to practical issues relevant to vaccine hesitation and misinformation, focusing on quantifying the information processing dynamics to provide mechanistic explanations of possible outcomes.

Presentation by Zhaoliang Wu
Communication University of China

Date: April 9, 2021
Time: 9:00-10:00 AM
Location: Zoom – link: https: //yorku.zoom.us/j/4167365757

Modeling and Analyzing Information Propagation Dynamics Driven by Public Sentiments in Chinese Sina-Microblog

Abstract: Social networks are flooded with different pieces of emotional information which can be divided into positive, neutral and negative generally. Compared with the other two sentiments, negative sentiments attract more public attention and the public is more willing to vent negative sentiments on social media, which leads to emotional contagion between netizens so that easily creates a negative emotional climate. The propagation of emotional information helps to shape the development of public sentiment and provides the direction of designing communication strategies. We proposed the emotion-based susceptible-forwarding-immune (E-SFI) model to promote the emergence of a desired public sentiment and proposed the multiple-negative-emotional susceptible-forwarding-immune (MNE-SFI) model to provide leading strategies for negative sentiments at different angles.

Presentation by Xinyu Xia
Communication University of China

Date: March 26, 2021
Time: 9:00-10:00 AM
Location: Zoom – link: https://yorku.zoom.us/j/96643509945

Effect of diapause and migration in tick demographics: A two-patch model

Abstract: We consider a simple spatial model to study tick population growth in a heterogeneous two-patch environment, where each patch provides different environmental conditions for tick growth. In particular, we focus on how diapause – a hormone-controlled state of dormancy in ticks, and migration can affect tick persistence using a system of Delay Differential Equations (DDEs). We focus on the equilibria of the model and give conditions on parameters for tick extinction, coexistence and for the existence of attracting periodic solutions using Hopf bifurcations.

Presentation by Marco Tosato
LIAM, York University

Date: March 26, 2021
Time: 9:00-10:00 AM
Location: Zoom – link: https://yorku.zoom.us/j/96643509945

Cost and social distancing dynamics in a mathematical model of COVID-19

Abstract: We present an SEIAR mathematical model of COVID-19 which includes social distancing and relaxation. Our model has a dynamic behavioural influence where the decision for susceptible people to isolate is a function of the total and active cases, but the decision to stop isolating is a function of the perceived cost of isolation. Along with this social distancing cost, we define an overburden healthcare cost due to the strain placed on the healthcare system with a high caseload. We demonstrate that, non-intuitively, increasing either isolation activity or incentive to isolate do not always lead to optimal health outcomes.

Dr. Iain Moyles
Professor
York University

Date: March 12, 2021
Time: 9:00-10:00 AM
Location: Zoom – link: https://yorku.zoom.us/j/96643509945

Using Structured Equations to Control Tumor Evolution and Avoid Chemotherapeutic Resistance

Abstract: Intra-tumour heterogeneity is a leading cause of treatment failure in cancer. While genetic mutations have long been accepted as a cause of this heterogeneity, the role of phenotypic plasticity in drug resistance is becoming increasingly apparent. We study the phenotypic evolution of drug tolerant and drug sensitive sub-populations through an age structured non-local partial differential equation. We clarify the role of phenotype plasticity on tumour persistence by recasting the model as a renewal equation and calculating the Malthusian parameter and basic reproduction number. As a concrete application, we model a population of drug resistant lung cancer cells, demonstrate the role of phenotype heterogeneity in therapy resistance and use the renewal equation formulation to derive a model informed therapy schedule. We show that this model informed therapeutic schedule results in increased treatment efficacy when compared against the standard of care and drives tumour decay while avoiding the development of drug resistance.

Dr. Tyler Cassidy
Post-doctoral fellow
Los Alamos National Laboratory, NM, USA

Date: May 29, 2020
Time: 2:30-3:30 PM
Location: Zoom – link: https://yorku.zoom.us/j/92560607033

Understanding unreported cases in the 2019 n-Cov epidemic outbreak and the importance of major public health interventions

Abstract: We develop a mathematical model to provide epidemic predictions for the 2019-nCov epidemic in China. We use reported case data from the Chinese Center for Disease Control and Prevention and the Wuhan Municipal Health Commission to parameterize the model. From the parameterized model we identify the number of unreported cases. We then use the model to project the epidemic forward with varying level of public health interventions. The model predictions emphasize the importance of major public health interventions in controlling 2019-nCov epidemics. Next we will apply it to the data from South Korea, Italy, France and Germany.

Dr. Pierre Magal
Professor
Université de Bordeaux

Date: March 31, 2020
Time: 10:30-11:30 AM
Location: Zoom – link: https://yorku.zoom.us/j/4167365757

Exponential separation for non-compact operators

Abstract: Roughly speaking, exponential separation (abbr. ES) describes the growth rate of linearization of nonlinear dynamical systems. It is understood that ES would be an important tool to analyze local behaviors of systems near by their invariant sets. Moreover, It also has closed connections to many other topics in different disciplines of dynamical systems, such as Dominated splitting in differential systems, Exponential dichotomy in differential equations, Multiple ergodic theorems in ergodic theory, and so on. Before utilizing it, We will naturally ask if the ES property can be established. Motivated by this question, there are several famous theorems to answer partly. For finite dimensional systems, it is the Perron-Frobenius theorem. For infinite compact dimensional systems, it's the Krein-Rutman theorem. But in the case of non-compact systems, Can we establish ES property? I will talk about parts of our works.

Dr. Lirui Feng
Post-doctoral fellow
York University

Date: Feb 27, 2018
Time: 10:30-11:30 AM
Location: Kinsmen 277

Risk of Tick-borne Encephalitis transmission in Hungary

Abstract: Tick-borne encephalitis (TBE) is a central nervous system infection which is endemic in many European countries including Hungary. In this study, we estimate the ecological/epidemiological parameters for TBE transmission using climate data and TBE incidence data. With the resulted TBE transmission model, we assess the risk of TBE transmission in Hungary.

Dr. Kyeongah Nah
Post-doctoral fellow
York University

Date: Feb 20, 2018
Time: 10:30-11:30 AM
Location: Kinsmen 277

Inferring epidemiological dynamics of infectious diseases using Tajima’s D statistic on nucleotide sequences of pathogens

Abstract: The estimation of the basic reproduction number is essential to understand epidemic dynamics, and time series data of infected individuals are usually used for the estimation. However, such data are not always available. Methods to estimate the basic reproduction number using genealogy constructed from nucleotide sequences of pathogens have been proposed so far. Here, we propose a new method to estimate epidemiological parameters of outbreaks using the time series change of Tajima's D statistic on the nucleotide sequences of pathogens. To relate the time evolution of Tajima's D to the number of infected individuals, we constructed a parsimonious mathematical model describing both the transmission process of pathogens among hosts and the evolutionary process of the pathogens. As a case study we applied this method to the field data of nucleotide sequences of pandemic influenza A (H1N1) 2009 viruses collected in Argentina. The Tajima's D-based method estimated basic reproduction number to be 1.55 with 95% highest posterior density (HPD) between 1.31 and 2.05, and the date of epidemic peak to be 10th July with 95% HPD between 22nd June and 9th August. The estimated basic reproduction number was consistent with estimation by birth–death skyline plot and estimation using the time series of the number of infected individuals. These results suggested that Tajima's D statistic on nucleotide sequences of pathogens could be useful to estimate epidemiological parameters of outbreaks.

Dr. Kiyeon Kim
Visitor Scholar
York University

Date: Feb 6, 2018
Time: 10:30-11:30 AM
Location: Kinsmen 286

An agent-based modelling (ABM) tool for forced migration scenario simulation

Abstract: We present an agent based modelling tool which could be used to address complex reasons for movements of people, particularly in context of mixed movements (e.g. economic factors, conflict, human rights violations/persecution/torture, environmental change, human trafficking etc.) in order to be better prepared for assisting and protecting displaced populations. This research is a part of the main project “Building Bridges across Social and Computational Sciences: Using Big Data to Inform Humanitarian Policy and Interventions”. If interested, more details can be found at http://fmbd.info.yorku.ca/

Dr. Kazi Rahman
Post-doctoral fellow
York University

Date: Jan 30, 2018
Time: 10:30-11:30 AM
Location: Kinsmen 286

Dynamical behaviors of antimicrobial continuation and de-escalation models

Abstract: In our previous modelling work, "Benefits and unintended consequences of antimicrobial de-escalation: implications for stewardship programs", we observed that de-escalation can be beneficial in terms of reducing strain transmissions under certain parameter settings. However, due to the complexity of the model, we were not able to mathematically characterize the impacts of these parameters on the model dynamics. In this talk, I will show some recent mathematical analysis of two simplified models of antimicrobial de-escalation and continuation, so as to better explain and further understand our prior results.

Dr. Xi Huo
Assistant Professor
University of Miami

Date: Jan 9, 2018
Time: 10:30-11:30 AM
Location: Kinsmen 286

Self-Excited Periodic and Quasi-Periodic Vibrations for Higher
Dimensional Damped Wave Equations.

Abstract: Using techniques from local bifurcation theory, we prove the
existence of various types of temporally periodic and quasi-periodic
waves for damped wave and beam equations, in higher dimensions. The
emphasis is on understanding the role of external bifurcation
parameters and symmetry, in generating the periodic/quasi-periodic
motion. Most of the work presented is joint with Brian
Pigott.

Dr. Nemanja Kosovalic
Assistant Professor
University of South Alabama

Date: Dec 15, 2017
Time: 11:30 AM-12:30 PM
Location: Kinsmen 286

Estimating reproduction numbers using nucleotide sequence data by the "Bayesian evolutionary analysis by sampling trees"(BEAST)

Abstract: Bayesian phylogenetic methods are commonly used for rapidly mutated viruses, which can affect reconstructed tree structure, to infer epidemiological processes from genetic data. Here I will introduce basic of the Bayesian theorem with BEAST briefly and Birth-death(BD) model which make the BEAST available to estimate reproduction number. After the introduction, I will show examples of its application to real sequence data, pandemic(H1N1) 2009 in Argentina and Hepatitis C virus(HCV) in Egypt.

Dr. Kiyeon Kim
Visitor Scholar
York University

Date: Nov 28, 2017
Time: 10:00-11:00 AM
Location: Kinsmen 286

Study on data mining method of gene transcriptome in tuberculosis

Abstract: This study will utilize the characteristics of transcriptome data related with TB, through significant analysis, association rule analysis, multi-level genetic model and dynamic time warping model to discover more key host gene associated with tuberculosis and their categories; to research the spatial correlation of all kinds of key genes and their roles in the formation of tuberculosis; to analysis dynamic changes of gene transcription on different time scales and the transcription rules in various conditions; to predict the potential expressions of pathogenic genes.

Dr. Xu Zhang
Post-doctoral Fellow
York University

Date: Nov 21, 2017
Time: 10:00-11:00 AM
Location: Kinsmen 286

Agent-Based model development for with-in host dynamics of L. monocytogenes

Abstract: The case fatality and illness rates associated with L. monocytogenes remain unchanged over the decades despite the significant efforts and control protocol obtained by private and public sectors.In order to demonstrate the human gastro-intestinal pathway of L. monocytogenes, we develop an agent based model. I will demonstrate the impact of food intake pattern, stomach pH variation and storage condition on the survival of L. monocytogenes in the stomach. The model will also illustrate the role of immune potential to prevent intestinal infections.

Dr. Ashrafur Rahman
Post-doctoral Fellow
York University

Date: Nov 14, 2017
Time: 10:00-11:00 AM
Location: Kaneff Tower

Sleep Duration and Chronic Condition Among Canadian Adults: Do Mental Illness Play a Mediating Role?

Abstract: Chronic condition has been major contributors to reduced quality of life, loss of productivity, and increased hospitalization and health care costs as well as premature death in Canada.For better chronic condition prevention, this large-scale study was designed to explore the potential association of sleep duration with chronic condition and mediation by mental illness. We obtained data from the 2011-2012 Canadian Community Health Survey. A total of 40,614 participants aged 18 years or older from four provinces (Nova Scotia, Quebec, Manitoba, and Alberta) that participated in the sleep survey module were selected for the study. Logistic regressions were performed to assess the mediation of mental illness on the association between sleep duration and chronic condition. The age- and sex- standardized prevalence of any chronic condition in four provinces of Canada was 54.5%. Compared to those sleep 7 to < 9 h, participants in both short (< 5 h, and 5 to < 7 h) and long (9 to < 11 h, and ≥ 11 h) sleep duration reported a higher prevalence of any chronic condition. After adjusting for all potential confounders, the “U-shaped” association between sleep duration and any chronic condition persisted. Following the criteria of examining mediating effects, mental illness was found significantly mediated the relationships between sleep duration and any chronic condition(all Sobel P< 0.001). However, the mediated effect size of mental illness was obviously higher in long sleep duration (32.8% and 33.4%) than short sleep duration (14.0% and 9.5%). Sleep duration had U-shaped relationships with the presence of chronic condition. Mental illness play a mediating role on the relationships between sleep duration and chronic condition, especially in long sleep duration.

Dr. Haijiang Dai
Post-doctoral Fellow
York University

Date: Nov 7, 2017
Time: 10:00-11:00 AM
Location: Kaneff Tower

Understanding the dynamics of West Nile Virus in Emilia-Romagna, Italy

Abstract: West Nile Virus (WNV) has been identified for the first time in Italy in 1998, and more continuously since 2008 with a total of 173 neurological human cases between 2008 and 2015. Still the circulation of the virus appears to have been episodic with most cases concentrated in a few years and a few hotspots shifting in different years. The region Emilia Romagna, which is one of the most affected areas, has set up since 2009 a systematic program of mosquito and corvids (known to be among the most competent bird species for WNV) trapping and testing. Data collected through this program have been analysed through a mathematical model in order to understand the main drivers of the observed dynamics. The analysis has mainly been based on an SIR (for competent birds)-SI (mosquitoes) model, with an environmentally driven population model, validated on independent data, for mosquito dynamics, and a simple population model for bird dynamics, in which the free parameters were the mosquito biting rate and the host-vector ratio. Our results showed that simplest models with constant mosquito feeding behaviours are incompatible with the observed seasonal patterns of infected mosquitoes and birds. On the other hand, including a seasonal shift in mosquito feeding behaviour makes model outputs much more consistent with observed data. Our findings can be of particular interest for public health policy makers, as they provide important insights on WNV dynamics in order to improve surveillance, and risk assessment of WNV in the area.

Marco Tosato
PhD Candidate
York University

Date: Oct 24, 2017
Time: 10:00-11:00 AM
Location: Kaneff Tower

pH dependent C. jejuni thermal inactivation models and application to poultry scalding

Abstract: Campylobacter jejuni related outbreaks and prevalence on retail poultry products pose threats to public health and cause financial burden worldwide. To resolve these problems, it is imperative to take a closer look at poultry processing practices and standards. Using available data (D-values) on the thermal inactivation of C. jejuni we develop a comprehensive inactivation model, taking into account the variation of strain-specific heat resistance, experimental method, and suspension pH. Utilizing our C. jejuni thermal inactivation model, we study the poultry scalding process. We present a mechanistic model of bacteria transfer and inactivation during a typical immersion scald
in a high-speed industrial plant. Integration of our C. jejuni inactivation model into the scalding model culminates in validation against industrial processing data. In particular, we successfully predict bacteria concentrations in the scald water and link key factors such as scald water pH and temperature to cross-contamination and overall microbiological quality of carcasses. Furthermore, we demonstrate the applicability of our inactivation model for scalding operations at seven Canadian poultry plants. In addition to providing recommendations for best-practice and a review of scalding research, our work is intended to act as a modular foundation for further research in the interest of public health and financial well-being.

Zack McCarthy
PhD Candidate
York University

Date: Oct 24, 2017
Time: 10:00-11:00 AM
Location: CB 126

Lone star tick and its dynamics

Jemisa Sadiku , Zilong Song,
PhD Candidate
York University

Date: Oct 10, 2017
Time: 10:00-11:00 AM
Location: CB 126

Effect of Host Resistance on Tick Population Dynamics

Abstract: The purpose of this model is to demonstrate the tick population dynamics when we consider bitten host with resistance. The reason why we consider such observation it's because there is evidence of tick's population size to be reduced if they are biting hosts with resistance.The reaction of sensitized host when a tick starts feeding is very different from that of susceptible 'naive' host. For instance, the feeding site of sensitized host is characterized by erythema and also by intra-epidermal vesicles packed with basophils which will result in immediate death of the tick. On the other hand susceptible hosts have very little and slow reaction to the tick feeding site allowing the tick to fully finish the feeding process and hence to achieve the disease transmission to the host.

In addition, we will observe how the values of parameters are correlated with tick's population dynamics and also observe if these biological factor are supported by the mathematical model.

Jemisa Sadiku (Joint work with Mahnaz Alavinejad),
PhD Candidate
York University

Date: Oct 3, 2017
Time: 10:00-11:00 AM
Location: CB 126

Bayesian Inference of Multiple Gaussian Graphical Models

Abstract: I will present a Bayesian approach to inference of multiple networks that has been proposed by Stingo et al. (JASA, 2015). The Bayesian approach readily allows the incorporation of crucial features into a model, including sharing of graph structure across related sample groups and providing a means for obtaining a measure of relative network similarity across groups. The approach also provides the the ability to include prior knowledge of edge-specific interactions and to encourage the degree of similarity to an established network. I will give a brief background on Bayesian inference and graphical modeling of network data and then describe the model specification, including the choice of prior distributions in the model by Stingo et al. I will present the numerical methods used for posterior inference and model selection and conclude with simulation results and an application of the methods to modeling protein networks.

Chris Prashad
PhD Candidate
York University

Date: June 19, 2017
Time: 3:00 PM – 4:00 PM
Location: CB 126

A Model of Chikungunya Transmission with Virus Mutation

Xiaomei Feng
Lecturer of Applied Mathematics
Yuncheng University, China

Date: April 17, 2017
Time: 12:30 PM – 1:30 PM
Location: CB 126

Population Dynamics and Genetic Studies of Sex-Determining Alleles in Honey Bees

Abstract:Crossing of different races of honeybees has become a common practice in these days. But many breeders fear that no pure races will be available in future to conserve the local honeybee ecotypes. Therefore population genetic studies are necessary to establish the expected viability of brood in such populations. Allele frequencies are responsible for viability and is used to characterize the genetic diversity in population. In this talk I will present some work of Jerzy Woyke, ''Population Genetic Studies on Sex Alleles in Honey Bee Using the example of the Kangroo Island Bee Sanctuary'', Journal of Apicultural Research 15(3/4), 1976, in which he formulated and analyzed the mathematical models and analytical expression for calculating the frequencies of sex alleles in subsequent generations and two sexes of honeybees. Also, I will discuss some mathematical theories of the population dynamics of the sex determining alleles in honeybees, developed by ShozoYokoyama and Mastoshi Nei, ''Population Dynamics of Sex-Determining Alleles in Honey Bees and Self-Incompatibility Alleles in Plants'', Genetics 91(3), 1978, where they proved that in an infinitely large populations with n number of alleles, the equilibrium frequency of sex alleles is 1/n and the asymptotic rate of approach to this equilibrium is 2/3n per generation.

Bushra Majeed
M.A Student
York University

Date: April 3, 2017
Time: 12:30 PM – 1:30 PM
Location: CB 126

Network of neurons with delayed feedback and dynamic memory enhancement.

Abstract:Delayed negative feedback, coupled with absolute refractory period, can generated a large amount of stable periodic orbits for associate memory storage and retrieval.

Dr. Jianhong Wu
University Distinguished Research Professor
Canada Research Chair in Industrial and Applied Mathematics
Laboratory for Industrial and Applied Mathematics (LIAM)
Department of Mathematics and Statistics
York University, Toronto, Canada

Date: March 20, 2017
Time: 12:30 PM – 1:30 PM
Location: CB 126

Complementary Sex Determination Substantially Increases Extinction Proneness of Haplodiploid Populations

Abstract: Haplodiploid insects such as ants, bees, and wasps are very important components of terrestrial ecosystems, and their conservation is essential for economic as well as ecological reasons. The conservation genetics of haplodiploids has received very little attention. Haplodiploidy is the property of hymenoptera due to complementary sex determination. In this unique system females develop from the fertilized egg while haploid males are produced from unfertilized egg, however diploid male are also produced but homozygous at the sex-determining locus. In this talk, I will present the work of Amro Zayed and Laurence Packer in which they discussed that the complementary sex determination mechanism in hymenoptera through homozygosity, leading to the production of more sterile and inviable diploid males, due to which haplodiploids are substantially more, rather than less, prone to extinction. Some results on the base of stochastic modelling will also be provided to see the effect of diploid male production (DMP) on the extinction dynamics of haplodiploid populations.

Bushra Majeed
M.A Student
York University

Date: January 30, 2017
Time: 12:30 PM – 1:30 PM
Location: CB 126

Modelling the Evolution of Influenza towards to its Prediction

Abstract: The transmission dynamics of infectious disease is non-linear, since the expected number of transmission events is proportional to the number of both susceptibles and invectives. Mathematical modelling has been playing a key role in understanding and predicting the transmission dynamics. An important disease which is difficult to understand and predict is Influenza. The difficulty in predicting influenza dynamics arises from i). the host of influenza is not only human, the most strains causing pandemic come from the non-human population, but the field data of non-human cases is not enough for predicting the invasion dynamics of influenza from non-human hosts; ii) Even in human population influenza virus evolves quickly and the efficacy of vaccine wanes quickly, so modelling the evolution of influenza in human population is challenging; iii) The transmission probability of influenza is changing
over time, it correlates with the absolute humidity and so the model coefficients in the nonlinear term is a function of time. In this talk, I will introduce some progress how mathematical modelling contributes to the understanding the transmission dynamics of influenza, especially for ii) and iii).

Dr. Ryosuke Omori
Assistant Professor
Research Centre for Zoonosis Control, Hokkaido University, Japan

Date: January 09, 2017
Time: 2:30 PM – 5:30 PM
Location: North Ross 201

Descriptive versus mechanistic dose-response modeling of L. monocytogenes infection in human population

Abstract: Dose-response relationship of L. monocytogenes infection is fundamental in evalution of risk analysis. Descriptive models (exponential, log-logistic and betarpoisson) describing the dose-response relationships have been widely used in L. monocytogenes outbreaks. These models, unfortunately, lack the insights of host-pathogen interaction that drive the response outcomes. Recently, we have developed a mechanistic model to account for the host-pathogen interaction in mouth to gut pathway that provides a mechanistic basis of dose-response relationship for L. monocytogenes infection. Our current study looks into the differences and similarities of two modeling approaches. In particular, we identify the key parameters and their relative ranges that differentiate the models in L. monocytogenes outbreak to human population.

Dr. Ashrafur Rahman
Post-doctoral fellow

Date: December 15, 2016
Time: 10:30 am to 11:30 am
Location: CB 126

The Lyapunov's functions of some epidemic models of control with vaccine: Case of Tuberculosis and Polio

Abstract: For the TB, the spread dynamics of the tuberculosis through mathematical models that incorporates both latent and clinical stages has been proposed by HONGBIN GUO and MICHAEL Y. LI.
In this work, we modified the precedent model and we add a strategy of control with vaccine. Then we calculated the basic reproduction number (R0). If R0 ≤ 1, the TB always dies out, otherwise the tuberculosis becomes endemic. The global stability of endemic equilibrium is established through direct Lyapunov and Lasalle Methods. We confirmed our analytics results through numerical simulations.
Talking about Polio, because of the lack of treatment of that disease, the only mean of prevention is immunization through live oral polio vaccine (OPV) or/and inactivated polio vaccine ( IPV).
Poliomyelitis is a very contagious viral infection caused by poliovirus. Children are principally targeted.
In this paper, we assessed the impact of vaccination in the control of spread of poliomyelitis. We used the deterministic SVEIR model of infectious disease transmission (Susceptible-Vaccinated-Latent-Infectious-Removed), where vaccinated individuals are also susceptible even in a lesser degree.
Using Lyapunov-Lasalle methods, we proved the global asymptotic stability of the unique endemic equilibrium whenever Rvac > 1. Some numerical simulations based on poliomyelitis data from Cameroon, conducted us to approve analytic results and demonstrated the importance of vaccinate coverage when it comes to controlling the spread of that disease.
In terms of perspectives, a study related to Polio, of multi group cases with migration between compartments of the same epidemic status is to be issue.

Dr Léontine NKAMBA
Senior Lecturer
University of Yaounde
Higher Teacher Training College
Department of Mathematics

Date: December 8, 2016
Time: 10:30 am to 11:30 am
Location: CB 126

Does two lags really make a trouble for stability analysis?

Seminar Notes

Date: December 1, 2016
Time: 10:30 am to 11:30 am
Location: CB 126

Investigating the optimal dengue vaccination to mitigate Zika cases

Biao Tang
Visiting PhD Candidate
Xi’an Jiaotong University, China,
York Univerisy and Fields Institution, Toronto, Canada

Abstract: This is a follow-up study of our previous model on implication of dengue vaccination for Zika outbreak, in which we found that due to the antibody-dependent enhancement, vaccinated individuals with antibody against dengue could have higher chance to be contaminated by Zikavirus, thus dengue vaccination could induce more Zika cases. In this study, we perform numerical experiments to seek the optimal dengue vaccination ratio that could benefit the control of both diseases. Ultimately, we aim to provide vaccination guidelines for dengue prevalent regions to reduce local Zika cases.

Date: November 17, 2016
Time: 10:30 to 11:30 am
Location: CB 126

Basic Introduction of LDA Topic Model on Text Mining

Bowen Sun
Master of Science Candidate
Simon Fraser University, British Columbia, Canada.

Abstract: In machine learning and natural language processing, a topic model is a type of statistical model for discovering the abstract ‘topics’ that occur in a collection of documents. Intuitively, given a document is about some topic, the occurrence of some particular words is expected to be more or less frequent. Thus, the topic model is very useful in the content exploration or key point extraction from large document corpora. In this presentation, I will give an introduction of some basic process in text mining and the LDA topic model theory

Date: November 10, 2016
Time: 10:30 to 11:30 am
Location: CB 126

Modelling antibody enhancement and its consequence for integrated vector borne disease control

Biao Tang
Visiting PhD Candidate
Xi’an Jiaotong University, China,
York Univerisy and Fields Institution, Toronto, Canada

Abstract: We present our recent work on modelling co-infection of diseases sharing the same vector, and the consequence of antibody enhancement for integrated intervention including vaccination, using dengue and Zika as references. This is based on joint work with Yanni Xiao and Jianhong Wu.

Date: October 13, 2016
Time: 10:30 to 11:30 am
Location: CB 126

Mathematical modelling of cross-diffusion in biofilms

Kazi Rahman
Post-doctoral Fellow
York University and Ryerson University
Toronto, Canada

Abstract: We propose a deterministic continuum model for mixed culture biofilms where movement of one species is affected by the presence of the other. Two derivations of this new model are presented. One derivation is based on the continuous time, discrete space master equation and the other one is based on the equations of conservation of mass and momentum. Starting from both viewpoints, we derive the same dual-species diffusion-reaction model for biofilms that comprises three non-standard diffusion effects: (i) degeneracy as the local biomass density vanishes, (ii) a super-diffusion singularity as the local biomass density approaches its a priori known maximum, and (iii) non-linear cross-diffusion. (i) describes the finite speed of propagation of the biofilm/water interface, (ii) describes volume filling effects, and (iii) describes the mixing of both biomass species. We present a numerical method for this highly nonlinear PDE model of biofilm that can tackle these three nonlinear diffusion effects. To investigate the effect of the new model feature, we study the role of the cross-diffusion terms in numerical simulations of three biofilm models: competition, allelopathy, and a mixed system formed by anaerobic and an anaerobic species. In all three systems we observe that accounting for cross-diffusion affects local biofilm structure, in particular the relative local distribution of biomass, but it does not affect overall lumped quantities such as the total amount of biomass in the system. As an application, our highly nonlinear density dependent cross-diffusion model is used in order to incorporate an experimental observation in models of disinfection of microbial biofilms. An extended reaction kinetics based on carbon consumption during disinfection is introduced. Our simulations show that the extended model captures the experimental observation, and suggest that the consumption of carbon substrates during inactivation due to antibiotics helps biofilms to survive and re-grow. Finally, as an extension of dual-species model, a generalized cross-diffusion model of k interacting species is derived considering the continuous time and discrete space master equation passing to the continuous limit. Moreover, a criterion for preserving the positivity of the solution of this type of generalized cross-diffusion model is presented.

Date: October 6, 2016
Time: 10:30 to 11:30 am
Location: CB 126

Neural Dynamics and Optimization of Online Advertising Systems

Yong Yang
PhD Candidate
York University
Toronto, Canada

Abstract: Real time Bidding (RTB) is a relatively new advertising technology that allows online advertising to be purchased and served on the fly. RTB ingests and distributes impressions from thousands of parameters simultaneously. Infersystems is an algorithm provider whose technologies apply nonparametric statistics to improve the performance of both Demand Side Platforms (DSPs) and Supply Side Platforms (SSPs). InferSystems generates media buying and optimization rules to minimize the cost per action (CPA) and cost per click (CPC) of a digital advertising campaign. These results are accomplished by using InferSystems proprietary, predictive analytic and decision engine (i.e., Infer Engine) that is able to predict super rare events from sparse data. Based on a variety of data training techniques, the Infer Engine automatically outputs a decision table of media buying rules. Buying rules are the number of combinations of parameters, such as country, banner code, banner position, landing url and bidding time. When users access the web page, all user information is uploaded to the web server. Infer Engine attempts to match impressions and the rules in the decision table. These generated media buying rules are able to predict which impressions are the most likely ones to be the clicked advertisements. As time goes by, these buying rules will fail to work and hence we expect to see a dramatic drop in the number of impressions if these buying rules are not revised / replaced. The objective of this thesis is two folds: to predict click impression dynamics, and to quantify the effectiveness of media buying rules and the decay of these rules. The model proposed is similar to, but different from, the classical epidemiological models for infectious diseases, this is due to the similarity between the considered click impression dynamics and the disease infection exposure dynamics.

Date: September 22, 2016
Time: 10:30 to 11:30 am
Location: CB 126

LIAM Initiatives during 2016-2017

Date: September 16, 2016
Time: 11:30 am to 12:30 pm
Location: CB 126

Neural Network Based Approach for Subspace Clustering of High Dimensional Data

Bowen Sun
Visiting M.Sc. Student
Simon Fraser University
Vancouver, Canada

Abstract: We are living in a data/information driven society, and every aspect of our life greatly depends on our ability to collect, analyze and understand large sets of data and information. These large data sets with high dimensions arise naturally from a variety of fields, such as bioinformatics, text mining. And the traditional clustering algorithms can’t work effectively for these types of data because of the well- known problem, the curse of dimensionality. In this talk, I am going to introduce a neural network architecture, PART developed by Professor Wu and his LIAM team, which is based on the ART developed by Carpenter and Grossberg. I will also discuss the algorithm for clustering high dimensional data sets.

Date: April 22, 2016
Time: 11:30 am to 12:30 pm
Location: CB 126

Mathematical Solutions of Pharmacokinetic Models When Nonlinearity Is An Issue

Xiaotian Wu
Visiting Professor
University of Montreal
Montreal, Canada

Abstract: Analytical solutions of pharmacokinetic models are appealing since they provide a clear and direct way to reveal the relationship between different model components, and greatly improve the process of drug development and drug design. In this talk, I will present mathematical solutions, including time-course of drug concentration and estimation of key pharmacokinetics parameters, of some pharmacokinetic models where nonlinear elimination is an important factor for some drug disposition, typically hormone drugs such as granulocyte colony-stimulating factor (G-CSF). This is a joint work with Professors Fahima Nekka and Jun Li at Université de Montréal.

Date: April 8, 2016
Time: 11:30 am to 12:30 pm
Location: CB 126

Predictive Modelling of Health Clusters for Chronic Disease Management

Yawen Xu
Postdoctoral fellow
York University and Manifolds Data Mining Inc.
Toronto, Canada

Abstract: Join Yawen Xu, York University, and Ted Hains & Zhen Mei, Manifold Data Mining Inc. lecture on health clusters of patients with chronic diseases, particularly diabetes and heart diseases. They will introduce a predictive modelling technique for classifying a patient into clusters, based on their demographics, mental health and stress, health outcomes, social connection, motivation and lifestyles.

Date: April 1, 2016
Time: 2:30 pm to 4:30 pm
Location: York Lanes Room 280N

Mathematical Modeling of Infectious Diseases From the Pre-Vaccine to Vaccine Era

Felicia Maria G. Magpantay
Assistant Professor
University of Manitoba
Manitoba, Canada

Abstract: The dynamics of vaccine-preventable diseases depend on the underlying disease process and the nature of the vaccine. In this talk I will discuss imperfect vaccines and the epidemiological consequences of different modes of vaccine failure. In particular, I will focus on the dynamics during the transition from the pre-vaccine to vaccine era and some new methodologies for dealing with incomplete data during this period.

I will also present an application to pertussis, a childhood disease that was once considered a candidate for eradication. This highly infectious disease is still a significant cause of child mortality in the world, and has been reemerging in some countries that maintain high vaccination coverage (e.g. USA, UK). Recent events have highlighted how much we still do not know about the mechanics of this disease and the type of immunity rendered by infection and vaccination. I will discuss some of the progress we have made in fitting a general stochastic model of pertussis, and the ideas behind the likelihood-based statistical inference methods (trajectory matching and iterated filtering) used to estimate the vaccine parameters.

Date: March 28, 2016
Time: 2:00 pm to 3:00 pm
Location: CB 126

Self-Excited Vibrations in Damped Wave Equations

Nemanja Kosovalic
Assistant Professor
University of South Alabama
Alabama, United States

Abstract: Over the last fifty years much work has been devoted to the study of forced vibrations in damped wave equations. From the mechanical point of view, external forcing is the simplest way of putting energy back into the system to balance out the friction, which results in a global time periodic solution whose amplitude does not decay. Another way of putting energy back into the system results from the presence of a restoring force with time delay. This leads to 'self-excited' vibrations. We discuss some aspects of self-excited vibrations for damped wave equations.

Date: March 18, 2016
Time: 11:30 am to 12:30 pm
Location: CB 126

The Evolution of Antimicrobial De-escalation: Part II

Lindsey Falk
Masters of Public Health (Epidemiology) Candidate
University of Toronto
Toronto, Canada

Abstract: Antibiotic resistance is a prominent issue in healthcare and there is a need to identify strategies that reduce resistance to broad spectrum antibiotics without compromising patient outcomes. This talk builds on a previous seminar given by Xi Huo and Josie Hughes, where a transmission model of P. aeruginosa in an intensive care unit (ICU) was presented to explore the evolutionary and ecologic impacts of antimicrobial de-escalation. De-escalation is a treatment strategy that aims to preserve the efficacy of broad spectrum antibiotics by switching patients to narrower agents. Although it has been applied widely in ICUs, the impacts are poorly understood. I will present the results from the model, which compares the de-escalation strategy to usual care under two different de-escalation approaches. As the mathematical model has previously been discussed, the focus will be on the clinical and public health implications of the model.

Date: March 11, 2016
Time: 11:30 am to 12:30 pm
Location: CB 126

Mathematical Modeling of With-In Host Dynamics of Listeria monocytogenes

Ashrafur Rahman
Postdoctoral Fellow
York University
Toronto, Canada

Abstract: Listeriosis is a potential food-borne disease caused by L. monocytogenes. The disease is an important public health problem as it poses a severe risk to certain populations including pregnant women, older adults, and individuals with a weakened immune systems. An individual can be infected with L. monocytogenes after consuming contaminated food. The bacteria can colonize in the intestines and reach the liver, spleen and placenta via the blood and lymphatic vessels. In this talk, I will outline modeling approaches of the Listeria invasion into the gut and its translocation at different organs. I will highlight the role of the immune responses and the inoculation doses that determine the difference of infection, and characterize the critical transition of listeriosis from mild to severe. This is based on joint work with D. Munther, J. Wu and a team of scientists from the Public Health Agency of Canada.

Date: March 4, 2016
Time: 11:30 am to 12:30 pm
Location: CB 126

Existence and Uniqueness of Mild and Strict Solutions for Abstract Differential Equations with State Dependent Delay

Michelle Pierri
Professor
São Paulo University
São Paulo, Brazil

Abstract: The theory of differential equations with delay is one of many important branches of the theory of differential questions. Recently, a new class of delay equations with a state-dependent delay (SDD) has attracted much attention of researchers. The study of ordinary and partial differential equations with state dependent delay differ from the case of ordinary and partial differential equations with constant or time-dependent delays. In this talk, we present some results related the existence of mild and strict solutions for abstract differential equations with state dependent delay. Our main result is concerning the existence of alpha-Holder strict solutions for this class of problems.

Date: February 26, 2016
Time: 11:30 am to 12:30 pm
Location: CB 126

Population Dynamics of Borrelia burgdorferi in Lyme disease

Jemisa Sadiku
PhD Candidate
Department of Mathematics and Statistics, York University
Toronto, Canada

Abstract: Many chronic inflammatory diseases are known to be caused by persistent bacterial or viral infections. A well-studied example is the tick-borne infection by the gram-negative spirochaetes of the genus Borrelia in humans and other mammals. It causes severe symptoms of chronic inflammation and subsequent tissue damage (Lyme Disease), particularly in large joints and the central nervous system, but also in the heart and other tissues of untreated patients. Although killed efficiently by human phagocytic cells in vitro, Borrelia exhibits a remarkably high infectivity in mice and men. In experimentally infected mice, the first immune response almost clears the infection. However, approximately 1 week post infection, the bacterial population recovers and reaches an even larger size before entering the chronic phase. We discussed a mathematical model (Binder et al., Frontiers in Microbiology, 2012) that describes the bacterial growth and the immune response against Borrelia burgdorferi in the C3H mouse strain that has been established as an experimental model for Lyme disease. The peculiar dynamics of the infection excludes two possible mechanistic explanations for the regrowth of the almost cleared bacteria. Neither the hypothesis of bacterial dissemination to different tissues nor a limitation of phagocytic capacity were compatible with experiment. The mathematical model predicts that Borrelia recovers from the strong initial immune response by the regrowth of an immune-resistant sub-population of the bacteria. The chronic phase appears as an equilibration of bacterial growth and adaptive immunity. This result has major implications for the development of the chronic phase of Borrelia infections as well as on potential protective clinical interventions.

Date: February 19, 2016
Time: 11:30 am to 12:30 pm
Location: CB 126

Research on Key Technologues to Quantify, Simulate, and Standardize Acupuncture Manipulations

Yine He
Faculty
Shanghai University of Traditional Chinese Medicine
Shanghai, China

Abstract: In this talk, I will introduce the basic concepts of acupuncture and how it can be applied to heal some diseases in China. I will talk about our project and the problems we encountered during clinical activities and research work. The project was based on previous studies on acupuncture techniques and we wanted to improve the hardware devices for parameter acquisition. We improved the software by quantifying parameters, taking videos, and collecting commentary of expert acupuncturists. Afterwards, we established an integrated database of acupuncture specialists and their manipulations. To this database, we applied data mining technologies to analyze the data, extracted common characteristics, and established a mathematical model which describes the features of good acupuncture manipulations. Finally, we applied simulation technologies to have established an experimental research platform based on the mathematical model. The platform can be used to collect and analyze data, and to simulate excellent acupuncture manipulations.

Date: February 5, 2016
Time: 11:30 am to 12:30 pm
Location: CB 126

Modelling the Post-Treatment Control of HIV Infection Using a Within-Host Model with Latent Reservoir and Immune Impairment

Shaoli Wang
Lecturer
School of Mathematics and Statistics, Henan University
Henan, China

Abstract: I will give a simplified within-host model with latent reservoir and immune impairment to explain the post-treatment immune control by exploring the bistability of the model. Mathematically, we show if the basic infection reproductive number, R_{0}, is less than one, the uninfected equilibrium of the proposed model is globally asymptotically stable, which means that the virus will die out. If R_{0} is greater than one, we can obtain two additional thresholds: the post-treatment immune control threshold and the elite control threshold. If the proliferation rate of CTLs is less than the post-treatment immune control threshold, the positive equilibria does not exist, the immune free equilibrium is stable, and the system will have virus rebound. If the proliferation rate of CTLs is within the bistable interval (between the two additional thresholds) and the initial virus population is low, then the system will be under post-treatment immune control. While the proliferation rate of CTLs is greater than the elite control threshold, the positive immune equilibrium is stable, the immune free equilibrium is unstable, and the system will be under elite control.

Date: January 29, 2016
Time: 11:30 am to 12:30 pm
Location: CB 126

Existence and Uniqueness of Solutions for Abstract Neutral Equations with State Dependent Delay

Eduardo Hernández
Professor
São Paulo University
São Paulo, Brazil

Abstract: In this seminar we present some results on the existence and uniqueness of strict solutions for a class of abstract neutral equations with state dependent delay with applications to partial neutral functional differential equations.

Date: January 22, 2016
Time: 11:30 am to 12:30 pm
Location: CB 126

Local Bifurcation Theory for Some Nonreversible Wave Equations

Nemanja Kosovalic
Assistant Professor
University of South Alabama
Alabama, United States

Abstract: Over the last fifty years a huge effort has been devoted to the study of the local bifurcation of periodic and quasi-periodic
solutions for reversible wave equations. Despite this effort, there are gaps in what is currently known about the nonreversible counterpart. Nonreversible wave equations generally include wave equations having either time delay or damping terms. We discuss some results in this direction and some open problems. This work presented is collaborative work with Dr. Brian Pigott and Dr. Chris Lin.

Date: December 21, 2015
Time: 11:30 am to 12:30 pm
Location: CB 126

The Evolutionary Ecology of Antimicrobial Descalation

Josie Hughes
Post-doctoral Fellow
Mount Sinai Hospital

Xi Huo
Post-doctoral Fellow
York University and Ryerson University

Abstract: We model the transmission of P. aeruginosa in intensive care units (ICUs) with deescalation as the major antibiotic treatment strategy. That is, empirical therapy is initiated when a patient is infected with P. aeruginosa, right after the laboratory test results become available, the definitive therapy will be de-escalated - the broad-spectrum antibiotic for empirical therapy is switched to a narrow-spectrum antibiotic if possible. De-escalation is a treatment strategy that have been applied widely in ICUs, with the aim of reducing the risk of super-infection and preserve the efficacy of broad spectrum drugs. It has been considered as a potential way of reducing antibiotic use and antimicrobial resistance in ICUs. This is a project of the Development of an Antimicrobial Resistance Diversity Index (ARDI) led by Prof. Jianhong Wu.

Date: December 11, 2015
Time: 11:30 am to 12:30 pm
Location: CB 126

Flocking, Flocking Bifurcation and Flocking Switches in a Two-Agent Flock with Processing Delay

Xiao Wang
Professor
College of Science, National University for Defense Technology
Changsha, China

Abstract: Necessary and sufficient conditions are established for a two-agent flock model with processing delay to admit a time-asymptotic flocking. The results provide a relation based on which proper initial positions and velocities can be selected to form a flocking with predetermined position displacement distance. It is shown that the processing delay can terminate a flocking, can induce a flocking and can lead to a flocking bifurcation resulting a periodic flocking. It is also shown that the processing delay can induce flocking switches in the sense that as the processing delay varies, the flocking may follow a switching pattern as no flocking-flocking-periodic flocking-flocking-divergence.

Date: November 27, 2015
Time: 11:30 am to 12:30 pm
Location: CB 126

Multiple-Platform Data Integration Method with Applications to Combined Analysis of Microarray and Proteomic Data

Yawen Xu
Postdoctoral Fellow
Department of Mathematics and Statistics, York University
Toronto, Canada

Abstract: It’s desirable in genomic studies to select biomarkers that differentiate between normal and diseased populations based on related data sets from different platforms. Most recently developed integration methods focus on correlation analyses between gene and protein expression profiles. These methods select biomarkers with concordant behavior but do not directly select differentially expressed biomarkers. Other methods combine statistical evidence in terms of ranks and p-values, but they don't account for the dependency relationships among the data across platforms. We propose an integration method to perform hypothesis testing and biomarkers selection based on multi-platform data sets observed from normal and diseased populations.

Date: November 13, 2015
Time: 11:30 am to 12:30 pm
Location: CB 126

Stability or Instability of Switched Systems with Time-Delay Using Fast and Random Switches

Yao Guo
Postdoctoral Fellow
Department of Mathematics and Statistics
Toronto, Canada

Abstract: This talk first presents examples that systems even with time delays switching among stable subsystems can be destabilized by fast switches. To illustrate this phenomenon, we introduce a prototype model which includes a linear random switched system with time-delay, and theoretically establish conditions under which the switched system can still be either unstable or stable by using certain sets of switches. Standard tools of stochastic theory are utilized in theoretical arguments, including Doob’s Optimal Stopping Theorem. In addition, we explain intuitively how this phenomenon happens and provide numerical simulations to reinforce our theoretical results.

Date: October 9, 2015
Time: 11:30 am to 12:30 pm
Location: Ross N638

Bistability in Ideological Conflict

Shaoli Wang
Henan University
Henan, China

Abstract: In this talk, we analyze the dynamics of the ideological model provided by Marvel et al. [1]. We show that bistability appears when the constant fraction of zealots, p, is less than a critical value p=p_c. In this case, when a subpopulation crosses certain boundaries, its stable equilibrium switches accordingly. We also prove that in the case of p=p_c, a saddle-node bifurcation replaces the bistability, and leaves a unique stable equilibrium, which means the entire population reaches a consensus. Simulations show that a system with two zealots, p₁ and p₂, is bistable with strictly positive equilibrium.

Date: September 30, 2015
Time: 2:30 pm to 3:30 pm
Location: TEL 5021A

Traveling Waves in Isothermal Diffusion Systems: Existence, Stability and Oscillations

Yuanwei Qi
University of Central Florida
Florida, USA

Abstract: In this talk I shall present some of the most recent results my and collaborators and I have proved in the last a few years. In particular, we show a promising model proposed by a leading world authority in chemical engineering, Prof. Gary of U. Cambridge, FRS, has very rich structures and the analytic study proves to be far more challenge than the old model.

Date: September 30, 2015
Time: 4:00 pm to 4:30 pm
Location: TEL 5021A

Some Points on Traveling Wave Front of Reaction-Diffusion Systems with Delay: The Threshold Dynamics

Ruili Feng
PhD candidate, University of Science and Technology of China

Abstract:

Date: September 18, 2015
Time: 11:30 am to 12:00 pm
Location: Ross N638

Building an Assessment Model for a Movie Quality Rating

Yong Yang

Abstract: SINA weibo (新浪微博)and Film and state administration of press, publication, radio, film and Television of The people's republic of china(国家广电总局) propose a project on movie quality rating assessment. SINA Weibo is one of the most popular Chinese microblogging website in china. SINA team wants to build a model for the movie quality rating .Based on this project, I will introduce a dataset and propose a method. Any suggestions or methods are welcome.

Date: September 18, 2015
Time: 12:00 pm to 12:30 pm
Location: Ross N638

Consensus and Clustering of Linear Leader-Following System with Delay

Yicheng Liu
Associate Professor
College of Science, National University of Defense Technology
Changsha, China

Abstract: Two or more groups with different initial opinion values would develop into a common consensus if there are special communications and interactions between groups. What adjacent structures within groups make them reaching a consensus is an important issue. In this talk, we present an opinion consensus and clustering problem between two groups, say leader group and following group. The evolution of opinions is described as a normalized continuous dynamical system involving a distributed delay. With hypothesis that the normal Laplacian matrix has a semisimple zero eigenvalue, we find that the coupled system reaches an unconditional consensus if and only if the multiplicity of zero is 1, and reaches a conditional consensus if the coupled structure is multipartite. Also, we will mention the relationship between consensus value of coupled group and consensus values of each group. An analytic consensus value formula is deduced by using the eigenvector analysis method. In results, we find the consensus value falls into the interval of leader’s and following’s consensus values. Meanwhile, the influence strength between groups has sensitively affected consensus value of the coupled system.

Date: September 11, 2015
Time: 11:30 am to 12:30 pm
Location: Ross N638