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Recent Submissions
Approximation Algorithms for Relative Survivable Network Design Problems
(University of Waterloo, 2025-09-19) Nan, JJ
The Survivable Network Design (SND) problem is a classical and well-studied graph
connectivity problem. Given a set of source-sink pairs and demands between them, SND
asks one to compute a subgraph such that the number of paths between each pair meets
their demand. SND is primarily interesting in modeling fault-tolerance; we can see the
problem as requiring certain nodes to be connected even if some edges ”fail”. It is well
known that a 2-approximation algorithm for SND exists, using the method of iterative
rounding.
In 2022, Dinitz et al. introduced a problem that we refer to as Path-Relative Survivable
Network Design (PRSND), a natural extension of SND that addresses cases where the
underlying graph does not have the required connectivity; in this problem, we require
that the connectivity of our subgraph is ”as good as” it is in the original graph. Perhaps
surprisingly, this variation makes PRSND much harder to approximate than standard SND,
and outside of certain special cases no constant-factor approximations have been found.
In this thesis we introduce the Cut-Relative Survivable Network Design (CRSND) prob-
lem, another variant of SND that similarly aims to capture relative fault-tolerance. We
show that this problem admits a 2-approximation algorithm, matching the best known ap-
proximation factor for SND, via a decomposition technique. We explore some properties of
said approximation, as well as hardness and modeling properties of Cut-Relative Network
Design problems.
Variable Selection and Prediction for Multistate Processes under Complex Observation Schemes
(University of Waterloo, 2025-09-19) Li, Xianwei
This thesis addresses variable selection and prediction in time-to-event analysis under complex observation schemes that commonly arise in biomedical studies. Such schemes may lead to right-censored data, interval-censored event times, or dual-censoring scenarios. Across three main chapters, we develop variable selection methods for multistate processes, address challenges arising from incomplete data under complex observation schemes, and investigate the implications of model misspecification, such as using simpler models in place of multistate models, and the potential risks of violating assumptions on covariate effects estimation and predictive performance.
We begin with considering the problem of variable selection for progressive multistate processes under intermittent observation in Chapter 2. This study is motivated by the need to identify which among a large list of candidate markers play a role in the progression of joint damage in psoriatic arthritis (PsA) patients. We adopted a penalized log-likelihood approach and developed an innovative Expectation-Maximization (EM) algorithm such that the maximization step can exploit existing software for penalized Poisson regression thereby enabling flexible use of common penalty functions. Simulation studies show good performance in identifying important markers with different penalty functions. We applied the algorithm in the motivating application involving a cohort of patients with psoriatic arthritis with repeated assessments of joint damage, and identified human leukocyte antigen (HLA) markers which are associated with disease progression, among a large group of candidate markers.
Chapter 3 extends this algorithm to more general multistate processes, and to more complex observation schemes. We consider the classical illness-death model which offers a useful framework for studying the progression of chronic disease while jointly modeling death. The exact time of disease progression is not observed directly but progression status is recorded at intermittent assessment times; the time to death is subject to right-censoring. This creates a dual observation scheme where progression times are interval-censored and survival times are subject to right censoring.
A penalized observed data likelihood approach is proposed which allows for separate penalties across different intensity functions. An EM algorithm is again developed to facilitate use of different penalties for variable selection on disease progression and death through penalized Poisson regression. This adaptation retains the flexibility to exploit existing software with commonly used penalty functions. Simulation studies show good finite-sample performance in variable selection with different combination of penalty functions. We also explored how various aspects of the variable selection algorithm affect performance such as use of nonparametric baseline intensities and different ways to select the optimal tuning parameter(s). An application to data from the National Alzheimer’s Coordinating Center (NACC) demonstrates the use of our method in jointly modeling dementia progression and mortality.
Chapter 4 builds on insights from Chapters 2 and 3 by investigating how simpler marginal methods targeting entry time to the absorbing state (e.g., a Cox proportional hazards model) compared to full multistate models. Here we retain use of the illness-death process as the basis of the investigation, but consider settings where transition times are only right-censored.
We first study the limiting values of regression estimators from a Cox proportional hazards model when the data generating process is based on a Markov illness-death model. The potential impact of modeling the multistate processes based on a misspecified model is also investigated by considering cases where a) important covariates are omitted, or b) the Markov assumption is violated. We then examine the implications of model misspecification when the goal is prediction - this is done by evaluating the predictive performance of a misspecified Cox regression model for overall survival and a misspecified Fine-Gray model for disease progression, and comparing their respective predictive performance against that of the true illness-death model. We find that the limiting value of regression coefficients estimators obtained from Cox models and Fine-Gray models depend on several factors, including the baseline hazard ratio of death between the intermediate and initial states, the probability of moving through the intermediate state, and covariate effects on all transitions. However, the corresponding predictive accuracy is not substantially compromised despite biases in the regression coefficient estimators in most scenarios we investigated. The limiting value of regression coefficients obtained from a Markov illness-death model and the corresponding predictive accuracy are sensitive to model misspecification such as omitting important covariates and violation of the Markov assumption. The practical implications are illustrated using a dataset of patients with metastatic breast cancer in the control arm to predict overall survival and fracture risk.
Chapter 5 reviews the contributions of this thesis and discusses problems warranting future research.
Accumulation and Recovery of Prolonged Low-Frequency Force Depression at Different Intensities of Repetitive Isometric Contractions
(University of Waterloo, 2025-09-19) Friedel, Jared
Prolonged Low Frequency Force Depression (PLFFD) may impact performance in the workplace by influencing musculoskeletal disorder (MSD) risk or reducing force stability. PLFFD is a reduction in low-frequency stimulated force with little change in high-frequency stimulated force for long period after contraction. The objective of this research was to measure changes in PLFFD over exposure and recovery from a half-shift of an isometric contraction task at two intensities on separate visits, and to determine whether there is a relationship between PLFFD and force stability.
Participants repetitively supported a weight with their elbow flexors for 4 consecutive 1-hour work-segments. Participants performed a low-force high-duty cycle and a high-force low-duty cycle workload-matched protocol on two different days. PLFFD and force stability were measured in the biceps brachii muscle throughout task exposure and recovery. PLFFD was measured as the ratio of elbow flexion force produced at low (10 Hz) and high (100 Hz) frequency transcutaneous stimulations on both the left (non-intervention) and right (intervention) arms. A repeated measures ANOVA detected a progression of PLFFD in the intervention arm through intervention and recovery, but no protocol-effects were detected. Force stability metrics of variability (normalized standard deviation) and unsteadiness (average rate of change) of force during an isometric elbow flexion force matching task were poorly predicted by PLFFD and better predicted by changes in Maximum Voluntary Force (MVF).
This research expanded on incidental findings of PLFFD from past research, and reinforced relationships between muscle fatigue and force stability. PLFFD did not recover at different rates depending on exertion intensity, nor impact force stability metrics, therefore likely not affecting worker performance. Muscle fatigue was shown to impact force unsteadiness to a greater extent than force variability, leading to the suggestion that force stability changes caused by muscle fatigue should be considered when designing a workplace.
Heterogeneity and homophily in coupled behavior-disease dynamics: from model structure to early warnings
(University of Waterloo, 2025-09-19) He, Zitao
Understanding how human behavior and infectious disease dynamics interact is essential for anticipating and mitigating outbreaks. While coupled behavior-disease models have provided valuable insights into the feedback between disease transmission and vaccination behavior, many assume homogeneous populations and neglect the influence of social structure in shaping individual vaccination strategies. Traditional surveillance systems often lack timely data on vaccination behavior, making it difficult to monitor changes in public vaccine sentiment. Moreover, existing statistical methods for detecting early warning signals of critical transitions rely on assumptions that do not always hold in real-world settings. This thesis addresses these limitations by incorporating population heterogeneity and homophily into a coupled behavior-disease model, and by using the resulting simulations to support the training of data-driven models for forecasting outbreak risks from high-frequency social media data. Specifically, we develop a coupled behavior-disease model that distinguishes social media users from non-users, capturing indirect heterogeneity in how individuals access vaccine-related information. The model demonstrates that homophily slows the spread of pro-vaccine strategies, pushing the population closer to tipping points. It also suggests that early vaccine-related online discussions may offer predictive signals of future outbreaks. Building on these findings, we generate synthetic time series with heavy-tailed noise to mimic real-world social media data. These model-generated data are used to train deep learning classifiers, under CNN-LSTM and ResNet architectures, to detect early warning signals in social media data. These classifiers outperform conventional statistical indicators, such as variance and lag-1 autocorrelation, in both sensitivity and specificity. Finally, we extend the modeling framework to a generalized multi-group vaccination game, considering direct heterogeneity in levels of vaccine support. Simulations reveal that homophily contributes to the persistence of opinion polarization in the population, regardless of the presence of diseases. Together, these studies highlight the need to account for heterogeneity in modeling vaccination behavior and that homophily can have various effects depending on the states of the system. We also show that combining mechanistic models and data-driven techniques can help detect emerging risks of disease outbreaks, informing more proactive public health policies.
Punctuated Ethos: Addressing Trust, Credibility and Expertise in Times of Crisis
(University of Waterloo, 2025-09-19) Eckert, Carolyn
Trust, Communication, and Crisis: Rhetorical Lessons from COVID-19
Trust, the earning, sustaining, and loss of it, is at the center of public responses during a health crisis like the COVID-19 pandemic. This dissertation explores how trust functions not merely as a social or institutional ideal, but as a rhetorical construct negotiated through language, ethos, and public discourse. Drawing on rhetorical theories of ethos, from Aristotle’s character-based model to Hyde’s concept of ethos as dwelling, the project introduces the concept of “punctuated ethos” to analyze how rhetorical credibility is constructed, fractured, and recalibrated at key moments of crisis. Through a rhetorical analysis of Canadian responses to COVID-19, grounded in a corpus of local news media coverage, this study investigates how political and public health authorities communicated protective measures such as lockdowns and vaccination campaigns, and how acts of resistance, such as the Trinity Bible Chapel (2020-2021) defiance and the “Freedom Convoy” (2022) protest, contested institutional credibility and reshaped public narratives of trust.
In early 2020, Canadian acceptance of public health measures was initially high. However, prolonged lockdowns, pandemic fatigue, and vaccine controversies fractured public trust, leading to increased polarization and protest. Emerging communication technologies further complicated trust-building by amplifying mis/disinformation and undermining traditional media authority. This dissertation applies a rhetorical approach to health risk communication frameworks (Leiss, 2004; Witte, 1992), alongside theoretical tools such as Huiling Ding’s epidemic rhetoric (2014), Stephen Katz and Carolyn Miller’s rhetorical model of risk communication (1996), and rhetorical analyses of appeals, topoi, and public argumentation (Fahnestock, 1998; Miller, 1989; Perelman, 1982; Sontag, 1978, Bitzer, 1968; Goodnight, 1982; Burke, 1969). These frameworks support an examination of how the public validates expertise (Mehlenbacher, 2022) and how trust becomes rhetorically shaped, disrupted, or re-established in moments of crisis.
Chapter 2 offers a historical context for Canada’s public health communication, from the 1918 Spanish influenza pandemic through SARS (2003) and H1N1 (2009), showing how trust was constructed, destabilized, and unevenly distributed across racialized and marginalized communities. Chapter 3 surveys relevant rhetorical, medical, and communication literatures, framing trust as a contingent rhetorical achievement rather than a stable condition. Chapters 4, 5, and 6 form the core case studies, analyzing pandemic rhetoric, vaccine rhetoric, and protest rhetoric, respectively, each applying grounded theory and rhetorical analysis to trace how communicators used strategies like fear, hope, and ethos to shape audience responses. These chapters also identify the shifting roles of local media as amplifier, skeptic, or translator of public health messages. The final chapter proposes a symbolic formulaic framework to model how emotional appeals, perceived efficacy, and media functions interact rhetorically to either sustain or fracture public trust.
Key findings highlight the importance of localized, community-centered messaging, the strategic use of emotional appeals, and the need for credible, transparent communication. Public health communicators must anticipate rhetorical outcomes by aligning emotional resonance with timing (kairos), community values (topos), and credible ethos. Authorities, professionals and communicators must develop critical literacy practices to prepare for future crises, including audience analysis, myth debunking, and media testing. Policy considerations, such as regulating mis/disinformation and enhancing journalistic integrity, are essential to supporting effective communication frameworks.
This research underscores that rhetoric is not an afterthought in crisis communication, it is the mechanism through which trust is built, challenged, or lost. Grounded in rhetorical theory and applied to contemporary media and health contexts, this dissertation offers actionable strategies for health professionals, communicators, educators, journalists, and policymakers to design resilient, trustworthy communication in times of crisis.