Schedule for: 24w5286 - SocioEconomic Mathematical Epidemiology: Developing Mathematical Modelling Theory

A buffet dinner is served daily between 5:30pm and 7:30pm in Vistas Dining Room, top floor of the Sally Borden Building.

Breakfast is served daily between 7 and 9am in the Vistas Dining Room, the top floor of the Sally Borden Building.

A brief introduction to BIRS with important logistical information, technology instruction, and opportunity for participants to ask questions.

In cases where existing knowledge is limited and there are very significant consequences to the policy and/or personal decisions that must be made based on the current state of rapidly changing knowledge, we often hear appeals to “trust the science,” “listen to the evidence,”, or “let the data speak for themselves.” These appeals implicitly rely on a view of science and of scientific objectivity according to which science is free from political, social, and moral values and controversies (the Value Free Ideal). This view, however, has been the subject of considerable criticism from philosophers of science over the past 20 years and is almost universally rejected in favour of an understanding of science that emphasizes the centrality of social processes.. Data do not speak for themselves but require interpretation. They do not serve as evidence outside of particular frameworks, about which scientists may disagree. And debate about those frameworks brings in considerations of values, directly or indirectly. Trusting the science, therefore, means placing epistemic trust in specific social processes, institutions, and scientists. In this talk, I will examine some of the complexities of building and maintaining the epistemic trust of various publics in science and medicine.

This talk will examine the identities and ideologies that drive attitudes towards controversial scientific topics and practices. Using examples from COVID-19 and climate change, we will examine how these attitudes are shaped by religious and political beliefs, trust, media, personal experience, emotions etc. We will further examine how, for some, certain attitudes develop into core identities, generating the “us vs. them” dynamic of behavior. While it is difficult to change core identities, we will uncover some areas of common interest which can be capitalized on to develop public policy with wider support.

Lunch is served daily between 11:30am and 1:30pm in the Vistas Dining Room, the top floor of the Sally Borden Building.

We will provide a brief overview of how behaviour is used in disease modelling and distinguish it from modelling behaviour explicitly. We will emphasize the value and importance of mechanistic modelling in general and as it relates to behaviour. The unique complexities of behaviour to other fields will be explored and discussed.

Distilling sociological, economic, and psychological theories into mathematical models requires simplifying assumptions that can change the original meaning of the theory. On the other hand, this process can also highlight similarities and create a common framework for thinking about human behaviour modelling. I will summarize how several representations of social dynamics in coupled behaviour-disease dynamics—the rational actor model, social learning, and social contagion—are limiting cases of the same framework. For this and other reasons, I will also argue a role for phenomenological models in behavioural epidemiology.

We study a simple SIS epidemic model accounting for human behaviour. Individuals can decide at each instant of time whether or not they adopt prophylactic (hereafter protection) measures such as mask wearing or social distancing. These measures decrease susceptibility and/or transmission. We consider a situation in which individuals are unaware of their current health status (infected or not), but can perceive disease prevalence at the population level. This assumption fits situations in which tests are not widely available. Thus, personnal decisions depend first on disease prevalence, as a proxy for the risk of being infected or infecting others, and second on the fraction of the population complying to the protection measure, which people can observe in their every day life. Human behaviour is assumed to be driven by imitation dynamics (Bauch, 2005; Poletti et al., 2009). When the disease does not naturally die out, the model has three types of endemic equilibria: no-protection, mixed-protection, and full-protection. Which endemic equilibrium is stable depends on the parameter values. We assume that the efficiency of the protection measure is positively correlated to its individual cost. Increasing the efficiency of the protection measure and therefore its individual cost can make the system switch from full protection to mixed-protection. This way, increasing the efficiency of a protection measure may increase disease prevalence at equilibrium. In other words, disease prevalence is minimized for intermediate efficiency, and individual cost, of the prophylactic measure. The rational is that when the prophylactic measure is too effective and therefore costly, part of the population free-rides on the effort of others and drops protection, resulting in increased prevalence. Altogether, our results show that the interplay between epidemiology and human behaviour may lead to counterintuitive but nevertheless intelligible outcomes, that should be anticipated when designing public health policies. This work is shared with François Castella and Frédéric Hamelin. References: Bauch, C. T. (2005). Imitation dynamics predict vaccinating behaviour. Proceedings of the Royal Society B: Biological Sciences, 272 (1573), 1669-1675. Poletti , P. et al. (2009) Spontaneous behavioural changes in response to epidemics. Journal of Theoretical Biology, 260

One of the many difficulties in modelling epidemic spread is that caused by behavioural change in the underlying population. This can be a major issue in public health since, as we have seen during the COVID-19 pandemic, behaviour in the population can change drastically as infection levels vary, both due to government mandates and personal decisions. Such changes in the underlying population result in major changes in transmission dynamics of the disease, making the modelling challenging. However, these issues also arise in agriculture and public health, as changes in farming practice are also often observed as disease prevalence changes. We propose a model formulation wherein time-varying transmission is captured by the level of alarm in the population and specified as a function of recent epidemic history. The alarm function itself can also vary dynamically, allowing for phenomena such as “lockdown fatigue”. The model is set in a data-augmented Bayesian framework as epidemic data are often only partially observed, and we can utilize prior information to help with parameter identifiability. We investigate the identifiability of the population alarm across a wide range of scenarios, using both parametric functions and non-parametric Gaussian process and splines. The benefit and utility of the proposed approach is illustrated through applications of COVID-19 and Ebola disease.

Community resilience is a dynamic process that describes a network of adaptive capacities that impact human society and allow it to adapt after adversity and take advantage of opportunities (Garstka & Kennedy, 2023; Norris et al, 2007). Better methods for quantifying or modeling resilience in an ecosystem allow for a more precise understanding of what influences outcomes at the population-level. Results from a study of multiple community ecosystems and health outcomes using network analysis are presented. How might measurement of complex adaptative systems being improved to better predict dynamic change or incorporate more signals of resilience or vulnerability?

Meet in foyer of TCPL to participate in the BIRS group photo. The photograph will be taken outdoors, so dress appropriately for the weather. Please don't be late, or you might not be in the official group photo!

This presentation will explore what information we have from both quantitative and qualitative research on the issue of opiates and mental health. As people living in Canada, we are aware that Canada is in the grips of an opiate crisis. The Algoma District (where I live) has seen alarming rates of opioid deaths at 50 deaths per 100,000 - well above the Ontario provincial average of 19.7/100,000. Bigras et al. (2021) have demonstrated that front-line workers in social services are uniquely positioned to reflect on the stresses of their work and how best to support people who are struggling with substance use. This presentation reviews both the publicly available quantitative information about the situation and preliminary findings of qualitative research undertaken with local front-line social service workers and volunteers about factors driving the crisis. By exploring both what the numbers make clear and what we hear from frontline workers we will be able to see what is missing from the picture. For instance, we will explore what the barriers are that prevent people from accessing services, and how we often leave this out of our data collection. The goal of this discussion is to give us ideas about what to research and how to research. I aim to give researchers, policy makers and stakeholders a fuller picture so that we can create new solutions for issues that are harming many in our society.

In 2021, the Extension Collaborative on Immunization Teaching and Engagement was created in response to the COVID-19 pandemic. To date, the work of nationwide teams has reached over 21 million people, delivered 1,198 vaccine clinics, and administered 26,497 immunizations. University of Idaho Extension has participated in five rounds of funded EXCITE projects addressing immunization education and access for rural and/or medically underserved populations. This presentation will offer a summary of behavioral determinants that were considered influential on vaccine attitudes and uptake in rural, low-resourced, Hispanic, and/or agricultural families.

We wanted to estimate the likely impact of a program to increase HIV testing rates among members of high-risk groups. To do this, we constructed an epidemic model, and then sought to estimate its parameters from data. The available data consisted of potentially unreliable sexual health survey data along with firm clinical diagnosis data, both drawn from a population of gay, bisexual and other men who have sex with men in Vancouver, BC. I will briefly summarize how a Bayesian analysis allowed us to achieve our modelling goals. This was all joint work with Michael Irvine, Warren Michelow, Bernhard Konrad and Mark Gilbert.

The HIV/AIDS epidemic is still active worldwide with no existing definitive cure. Based on the WHO recommendations stated in 2014, a treatment, called Pre-Exposure Prophylaxis (PrEP), has been used in the world, and more particularly in France since 2016, to prevent HIV infections. We propose a new compartmental epidemiological model with a limited protection time offered by this new treatment. We describe the PrEP compartment with an age-structure hyperbolic equation and introduce a differential equation on the parameter that governs the PrEP starting process. This leads us to a nonlinear differential–difference system with discrete delay. After a local stability analysis, we prove the global behavior of the system. Finally, we illustrate the solutions with numerical simulations based on the data of the French Men who have Sex with Men (MSM) population. We show that the choice of a logistic time dynamics combined with our Hill-function-like model leads to a perfect data fit. These results enable us to forecast the evolution of the HIV epidemics in France if the populations keep using PrEP.

Understanding the dynamics of health emergencies is essential for effective public health intervention. Human behavior and coping capacities can influence the health crisis. However, these aspects can also be influenced by socio-economic conditions, community interventions, trust, information sources, etc. The integration of these interconnected variables into modeling remains limited due to lack of data or development of modeling approaches. Therefore, in this presentation, I will give some examples of data we have collected or analyzed over the years, as well as some mathematical and data-driven modeling strategies and their limitations.

In this work, we propose a modeling framework that gives insight to public policy compliance and efficacy for complex One Health issues, and apply it to a case study on non-pharmaceutical interventions (NPIs) compliance in Ontario during the COVID-19 pandemic. Using a stepwise multivariate linear regression analysis, we determine monthly comfort levels with both staying-home and mask-wearing, as well as determine significant indicator variables of each. These comfort values are then used within a decision-making model to determine average estimated compliance to each NPI in Ontario from March 2020 to January 2022. Using $SEILR$ and $SEILRV_{1,2,3}$ compartmental models, we fit our simulations to real incidence data to determine the efficacy of staying-home and mask-wearing policies over time. Finally, we explore the applicability of our model to One Health policies, benefits of using the One Health approach, limitations of our work and discuss possible avenues for future work. Joint work with: Rhiannon Loster (UoG & Public Health), Ed Thommes (Sanofi & UoG), Sarah Smook (Astra-Zeneca, UoG graduate) and David Lyver (Quintalink - UoG graduate)

When modelling opinion-dependent spread of infection, the presence of both multiple opinion states and epidemiological compartments can make direct analysis unwieldy. Conceptual understanding of the dynamics can often be improved by representing each compartment through its total size, opinion distribution, and effective parameters. To illustrate this idea, we discuss an example with multiple opinion states among susceptible individuals, and explore insights that can be gained by first assuming time scale separation, then weakening this assumption.

If vaccines are to be promoted, it is important to understand the factors behind individuals’ attitudes and final behaviour toward vaccination. Motivated by the considerable variation in individuals' reliance on social learning, we developed a mechanistic model that incorporates two decision-making types: myopic rationalists and success-based learners. By fitting the model to COVID-19 vaccine uptake data, we estimated their proportions across US states and Canadian provinces. In this talk, I will address the study's limitations and discuss our proposed solutions.

A buffet dinner is served daily between 5:30pm and 7:30pm in Vistas Dining Room, top floor of the Sally Borden Building.

Breakfast is served daily between 7 and 9am in the Vistas Dining Room, the top floor of the Sally Borden Building.

During the past 25 years, the methodological literature on mixture models has expanded and it is now extensively used in practice to capture unobserved heterogeneity of specific parameters in the population. We use a pedagogical review presenting different mixture models and their relationships to each other. We first present the results of a Latent Transition Analysis of political behaviors. Secondly, we will present the results of a Latent Transition Mixture Model that combines Latent Class Analysis to identify profiles based on an important number of risk factors, Latent Class Growth Analysis to identify longitudinal profiles of a behavior, and their interaction in predicting some distal outcomes. We will present these prototypic mixture models in a unified format, based on familiar probability laws, common assumptions, and interpretation. Each mixture model can be divided into a within class model and a between class model. The within class model accounts for the data-generating mechanism for persons in class K. The between class model helps define the probability that a person will be in a class vs. another. Co-authors: Félix Laliberté, Mathieu Pelletier-Dumas, Jean-Marc Lina and Roxane de la Sablonnière

Missing data is inherent in the study of social phenomena, and can introduce bias and result in diminished statistical power. However, missingness can also be an indication of interesting dynamics. For instance, some individuals might be dissatisfied about the efficacy of a treatment and thus abandon a study. Other individuals could be unreachable because of an unstable social and political context. This communication is aimed to argue that mathematical modelling of social behaviour must take into account missingness in order to deepen the understanding of the phenomenons under study. To this end, we will outline the theory behind missing data, present on some of its causes, and provide pointers on the introduction of missingness into the mathematical modelling of social behaviour. Co-authors: Mathieu Caron-Diotte, Mathieu Pelletier-Dumas, Éric Lacourse, Anna Dorfman, Dietlind Stolle, Jean-Marc Lina, and Roxane de la Sablonnière

The United Nations anticipates that over 500 dramatic social changes (DSC) like the COVID-19 pandemic will occur by 2030 (i.e., climate catastrophes). DSC requires the implementation of public policies aimed at shaping collective behavior, and in particular compliance. Understanding the adaptation process to DSCs entails complex methodologies and analyses that consider the dynamic nature of DSC. However, to date, most research has focused on cross-sectional “static” designs. We aim at answering this limit by exploring compliance with public policies during the COVID-19 pandemic. Specifically, we use a comprehensive investigation spanning three studies that involves a representative sample of Canadian citizens (N=3617) who participated in a 12-wave longitudinal study (April 2020 to April 2022). All studies use LGCA (i.e., trajectory analysis) to account for the dynamic aspect of the pandemic. Study 1 seeks to identify longitudinal patterns of compliance behaviour with preventive measures during the first year of the pandemic and their relation with factors like used sources of information and level of understanding. Study 2 uses different indicators (e.g., trust in the Prime Minister) to predict transitions in memberships to varying patterns of compliance across time. Finally, Study 3 furthers our understanding of transition patterns during a different moment of the COVID-19 pandemic (July 2020 to March 2021). By delving into dynamic processes, this investigation offers valuable insights for understanding how individuals react to the public policies put in place to face the DSCs of today and of those anticipated in the coming decades. Co-authors: Sahar Ramazan Ali, Éric Lacourse, Jean-Marc Lina, Jacques Bélair, Dietlind Stolle, and Roxane de la Sablonnière

There are now a number of ways in which behaviour and behaviour change can be modelled. One of the most popular is the Capability-Opportunity-Motivation: Behaviour (COM-B) model. This provides an excellent structure to be able to understand behaviours. COVID-19 was, and still is, a disease which requires a specific set of behaviours to occur to reduce its spread and impact. As an example of the kind of work we do at the Montreal Behavioural Medicine Centre (www.mbmc-cmcm.ca), we applied the COM-B to an ongoing COVID-19 study, the iCARE study (www.iCAREStudy.com), which has collected over 170,000 responses since the start of the pandemic, to explore the drivers of various pandemic behaviours, e.g., mask wearing, vaccine uptake, and physical distancing. Mapping the data collected onto the COM-B provides concrete information on how to better structure public health interventions to fight future pandemics. However, there are notable challenges in analysing this kind of data, e.g., changes in trends overtime, accounting for impacts of multiple concurrent behaviours, etc., which would benefit from more advanced statistical techniques.

Lunch is served daily between 11:30am and 1:30pm in the Vistas Dining Room, the top floor of the Sally Borden Building.

Throughout the last two centuries, vaccines have been helpful in mitigating numerous epidemic diseases. However, vaccine hesitancy has been identified as a substantial obstacle in healthcare management. We examined the epidemiological dynamics of an emerging infection under vaccination using an SVEIR model with differential morbidity. We mathematically analyzed the model, derived R0, and provided a complete analysis of the bifurcation at R0 = 1. Sensitivity analysis and numerical simulations were used to quantify the tradeoffs between vaccine efficacy and vaccine hesitancy on reducing the disease burden. Our results indicated that if the percentage of the population hesitant about taking the vaccine is 10%, then a vaccine with 94% efficacy is required to reduce the peak of infections by 40%. If 60% of the population is reluctant about being vaccinated, then even a perfect vaccine will not be able to reduce the peak of infections by 40%.

It is well-established that human activity is driving extreme weather patterns, and that these extreme events influence human behaviour. However, few models allow for human behaviours and the climate to dynamically interact. The models presented in this talk expand on previous work and serve as an initial framework to extend current models by using a dynamic social-climate feedback loop. First, we introduce a social model to determine the conditions under which a committed minority can overturn a pre-established social convention. Second, we modify an existing climate model to include climatic variability. Lastly, we formulate a social-climate feedback loop to study the interplay between human behaviour and the climate. Our results demonstrate that the social-climate feedback loop may be important in accurately predicting future temperatures, in contrast to the standard approach where human behaviour is considered fixed. Additionally, we find that a committed minority plays a vital role in shifting public opinion towards climate action and that the time at which the social convention of climate inaction is overturned has a large impact on future temperatures.

Problems in environmental sustainability, climate change, and spread of contagious disease involve biophysical, economic, and human social and behavioral systems that dynamically interact. We present a framework for modeling human social and behavioral systems through the processes of contagion and cognition with perceived risk as the state variable determining the human response. We briefly present examples from epidemiology, solar geoengineering, and anthropogenic climate change. Co-authors: Katherine LaCasse, Rhode Island College Louis J. Gross, University of Tennessee, Knoxville Karim Chichakly, ISEE Systems Sarah Constantino, Stanford University Travis Franck, Tufts University Sara Metcalf, University at Buffalo Fran Moore, UC Davis Kaitlin Raimi, University of Michigan Dale Rothman, George Mason University Daniele Visioni, Cornell University

The vast majority of climate models designed to project future global temperature trajectories ignore feedbacks between human behavioral and social system responses and the climate system. Prior research on models linking climate models to human behavior provide evidence that these linkages can significantly modify future trajectories compared to climate models based only on natural system processes. We will describe our efforts to model the interactions of climate systems and human social systems, focusing particularly on risk perception. We will describe how we model human risk perception and associated changes in attitudes as driven by the experience of climate change, for example, from extreme climate events and economic damages, and how this perceived risk motivates willingness to pay for abatement of greenhouse gas emissions and support for ‘green’ policies. This presentation will focus on methods to model cognition, personal experience and memory processing. This is modeled as a balance between sensing and forgetting extreme climate events that allows for habituation, salience, biased assimilation and recency and considers how these factors might vary across a heterogeneous population. Co-authors: Brian Beckage (University of Vermont), Karim Chichakly (isee systems), Sarah Constantino (Northeastern University), Travis Franck (Tufts University), Katherine LaCasse (Rhode Island College), Sara Metcalf (University at Buffalo), Dale Rothman (George Mason University)

COVID-19, wars, and natural disasters exemplify dramatic social changes (DSC), characterized by rapid shifts, disruption in social structures and behaviors, and threats to social identity. While existing models like ViEWS and MASON RebeLand attempt to forecast such events, they fall short by focusing narrowly on specific contexts and types of social change without taking a proper look on individual experiences such as identity threats. To address these gaps, we developed a new typology of social change from an extensive review of over 300 selected papers. This typology identifies four social states: stability, incremental social change, DSC, and collective inertia. Utilizing this framework, we created the Social Change Algorithm (SCA), a predictive tool now undergoing validation and simulation. The SCA forecasts shifts between social states by analyzing patterns and probabilities using a Bayesian model, aiming to improve decision-making in response to events like pandemics. Initial studies have modeled transitions due to coups d’état and elections and have first applied the algorithm to assess the impact of COVID-19 in North America. The SCA, however, requires further theoretical refinement, including the integration of collective memory, to enhance its utility for decision-makers. Co-authors: Jean-Marc Lina, Jacques Bélair, and Roxane de la Sablonnière

The public health measures imposed to curb the spread of the COVID-19 virus created strong opposition among part of the population. The literature points to psychological reactance as a main factor explaining non-compliance with the public health measures, as it prompts individuals to want to restore the freedom that they felt they have lost. While demographic, personal, and contextual factors have been identified as influencing the level of reactance, previous studies have not explored a comprehensive model that includes all these factors together to develop a broad profile of reactant individuals. Using a representative sample of Canadians (N=3617) and machine learning, the present study employs a regression with Lasso regularization to develop a predictive model assessing the most important factors (old and new) related to psychological reactance during the COVID-19 pandemic. Out of the 158 factors considered, the Lasso regularization retained the 34 most important ones. The results reveal that confidence towards scientists, political orientation, and loneliness are among the strongest factors related to psychological reactance. These findings contribute to a better understanding of the complex interplay of factors influencing psychological reactance and can inform public health strategies to enhance compliance with health measures. Co-authors: Matthew Fernandez, Sophie Sydoriak, Mathieu Pelletier-Dumas, Eric Lacourse & Roxane de la Sablonnière

All the major challenges humanity faces - climate adaptation, social cohesion, technology adoption, healthy lifestyles depend on behaviour change. A silver lining of COVID-19 may be an awareness of the importance of behaviour change and the contribution of psychology. Different countries engaged with psychology and social psychology in different ways and adopted different ‘theories’ of the human subject and behaviour change (e.g., with an individual approach there was an emphasis on attitudes, risk perception, behavioural fatigue, loneliness and mental health, crowd fear and panic, incentivisation). In some areas there was an increased and novel openness towards group-based processes (social identity and group norms, collective responsibility, aid and solidarity, leadership and social influence). Internally within the field debates emerged about whether psychology was ready for “prime time” and the crisis of generalisability. In this presentation the focus will be on how COVID-19 offers a disciplinary opportunity to advance social psychology. Post-crisis it is important to consider questions such as What have we learned? What are the implications for other imminent crises and human challenges? and where are the gaps in knowledge and advances that are needed in theory and research?

A standard of evidence is a transition from collecting information to acting on that information. In this brief talk I argue for two claims. First, evidentiary standards are heterogenous because they depend on context, domain, and social conventions. Here I will present some results from empirical work about people's evidence gathering behavior when they are asked to assess a causal claim, such as the effectiveness of an anti-viral nasal spray in preventing contraction of COVID. Second, deference to experts is itself subject to standards of evidence, but little is known about how people update their assessments of the reliability or trustworthiness of sources. Consequently, models should capture this uncertainty.

Coupling social influence-based opinion model with disease dynamics knows a growing interest to understanding feedback loops between the distribution of opinions on costly prophylactic behaviors and disease evolution. Recent research has examined how the occurrence of multiple epidemic waves depends on the influence cost of prophylaxis and the rate of opinion changes relative to disease propagation. Building on these results, we combine an attitude spectrum based on a double-prophylaxis with an SIR disease dynamic model that accounts for important disease states from the COVID-19 pandemic context. In this model, attitude changes are governed by the effective rate of mutual influence which is determined by the perceived risk of becoming infected. Under this framework, the distribution of opinions in disease-free conditions depends on the influence cost of prophylaxis. We explore how epidemic severity measures such as epidemic peak and final size, and the occurrence of multiple epidemic waves are related to the disease-free influence cost of prophylaxis and the influence benefit of adherence to prophylactic behaviors during an epidemic.

In the deployment of non-pharmaceutical interventions (NPIs) for the control of infectious disease propagation, the level of compliance in the target population is crucial for their success. We present deterministic compartmental models in which subgroups of the population have variable degrees of i. information about the NPIs and ii. compliance with the NPIs. We explore the dynamical consequences of movements between the corresponding subgroups.

A buffet dinner is served daily between 5:30pm and 7:30pm in Vistas Dining Room, top floor of the Sally Borden Building.

Posters will be presented by (in no particular order): • Laura French Bourgeois • Teri Garstka • Betsy Varughese • Azadeh Aghaeeyan • Monica Cojocaru • Lindsey McConnell-Soong • Priscilla (Cindy) Greenwood • Lou Gross • Md Mijanur Rahman • Grégoire Ranson • Chadi Saad-Roy • Elissa Schwartz • Rebecca Tyson • Madeline Ward

Breakfast is served daily between 7 and 9am in the Vistas Dining Room, the top floor of the Sally Borden Building.

In this session we will identify 6 subtopics for further discussion in small groups.

Lunch is served daily between 11:30am and 1:30pm in the Vistas Dining Room, the top floor of the Sally Borden Building.

During this time each of the subgroups will present a summary of their discussion to the full workshop group.

A buffet dinner is served daily between 5:30pm and 7:30pm in Vistas Dining Room, top floor of the Sally Borden Building.

Breakfast is served daily between 7 and 9am in the Vistas Dining Room, the top floor of the Sally Borden Building.

During this session we will identify subtopics for discussion. These may be the same as those discussed the previous day, or new.

Lunch is served daily between 11:30am and 1:30pm in the Vistas Dining Room, the top floor of the Sally Borden Building.

During this session, subgroups will report back to the full group with presentations summarizing the subgroup discussions.

During this session, we will identify avenues forward for future research, and identify possible publications arising from this meeting.

During this session, participants will provide feedback to the organisers regarding the entire workshop, including material that will go in the workshop report. We will also discuss strategy for moving the workshop conversation forward.

A buffet dinner is served daily between 5:30pm and 7:30pm in Vistas Dining Room, top floor of the Sally Borden Building.

Breakfast is served daily between 7 and 9am in the Vistas Dining Room, the top floor of the Sally Borden Building.