Wednesday Morning Session (August 10, 8:30-12:00)
Moderator: Bill Nelson
Beyond the reproductive system and reproductive behaviors, men and women exhibit major differences in many organ systems, including the anatomy of the brain, the activities of the stress and immune systems, and the metabolic and cardiovascular functions. A comprehensive understanding of the impact of these sex differences on health and disease is crucial to the development of effective sex-based therapies. Mathematical modeling has the potential of facilitating and contributing to advancing the understanding of sex differences in health and disease. Indeed, explosion of mathematical models have been developed in recent decades for different aspects of human physiology and pathophysiology. I will describe sex-specific mathematical models of physiological systems that our group has developed, and describe insights that have been revealed in those modeling studies, and discuss future opportunities.
Historical records allow us to reconstruct patterns of disease spread in the past, in some cases going back hundreds of years. The questions we can address depend on the available data, which has varied enormously over time. I will present data, going back as far as 1348, which we have acquired and studied at McMaster in the last few years. I will discuss the strengths and limitations of the various types of data for mechanistic modelling, and how these data have so far contributed to improving our understanding of pandemics of plague, cholera, influenza, and COVID-19.
In order to limit the disease burden and economic costs associated with the COVID-19 pandemic, it is important to understand how effective and widely distributed a vaccine must be in order to have a beneficial impact on public health. To evaluate the potential effect of a vaccine, we developed risk equations for the daily risk of COVID-19 infection both currently and after a vaccine becomes available. Our risk equations account for the basic transmission probability of COVID-19 (β) and the lowered risk due to various protection options: physical distancing; face coverings such as masks, goggles, face shields or other medical equipment; handwashing; and vaccination. We found that the outcome depends significantly on the degree of vaccine uptake: if uptake is higher than 80%, then the daily risk can be cut by 50% or more. However, if less than 40% of people get vaccinated and other protection options are abandoned— as may well happen in the wake of a COVID-19 vaccine—then introducing even an excellent vaccine will produce a worse outcome than our current situation. It is thus critical that effective education strategies are employed in tandem with vaccine rollout.
Authors: Calvin P Sjaarda, Kyla Tozer, Prameet M Sheth, Robert I Colautti
Affiliation: Queen's University
The COVID-19 pandemic is the worst global pandemic in 100 years, mobilizing an unprecedented public health response that has accelerated development of real-time genome sequencing. This approach may become the new model for tracking future global pandemics with potential for early detection and tracking of emerging pathogens. In this talk, we review our recent work applying simple machine learning and evolutionary phylogenetic models to investigate the COVID-19 epidemic in Eastern and Northern Ontario and discuss emerging datasets that are ripe for Ecological Modelling.
Wednesday Afternoon Session (August 10, 13:00-15:00)
Moderator: Felicia Magpantay
Phylodynamics is the project of inferring determinants of disease transmission, progression, and immunity from genomic data, in particular, from the genealogical or phylogenetic relationships among pathogen samples. I describe an approach to phylodynamics that unifies and extends existing full-information methods for parameterizing pathogen transmission models. While existing methods rely on approximations that are often violated in practice, our approach yields exact expressions for the likelihood. The key ingredient is to view a genealogy not as a static, retrospective account of ancestry but as a dynamically evolving object. Specifically, we show how a given population-level process induces a genealogy-valued Markov process, and derive a nonlinear filtering equation that can be used as the basis for inference from genomic data.
King AA, Lin Q, Ionides EL (2022). "Markov genealogy processes." Theoretical Population Biology, 143, 77–91. doi:10.1016/j.tpb.2021.11.003.
Disease outbreaks are increasing both in terms of severity and frequency, in part due to accelerating human encroachment into natural landscapes, urbanization, globalization, and climate change. These are exacerbating and worsening already existing health and social inequities by straining health systems and increasing the vulnerability of climate "hotspots" to the emergence and re-emergence of many infectious diseases. The One Health concept recognizes and responds to the reality that human health is interdependent with the health of animals and the environment. Addressing disease outbreaks that span the human-animal-environment interface requires intersectoral, multidisciplinary, and systems-oriented (process-, pattern-based, and participatory) approaches that can examine and manipulate large data sets to identify risks, conduct integrative, predictive modeling, and provide proactive, evidence-based recommendations for public health policy and action. My resea rch program represents a step in this direction.
In this talk, I will share some of the work I have been doing: helping governments and communities manage and contain the spread of COVId-19. In the first part, I will show some of the models that I designed and analyzed for early response and community-based risk mitigation and control of developing epidemics using COVID-19 as a case study. I will present the results I obtained from the models. In the second part, I will talk about some of my ongoing work on designing an early warning framework for emerging and re-emerging infectious diseases.
Thursday Morning Session (August 11, 8:30-12:00)
Moderator: Troy Day
Developing methods for anticipating the emergence or re-emergence of infectious diseases is both important and timely, however traditional model-based approaches are stymied by uncertainty surrounding the underlying drivers. Here, we demonstrate an operational, mechanism-agnostic detection algorithm for disease (re-)emergence based on early-warning signals (EWS) derived from the theory of critical slowing down. Specifically, we used computer simulations to train a supervised learning algorithm to detect the dynamical footprints of (re-)emergence present in epidemiological data. Our algorithm was then challenged to forecast the slowly manifesting, spatially-replicated reemergence of mumps in England in the mid-2000s and pertussis post-1980 in the US. Our method successfully anticipated mumps re-emergence four years in advance, during which time mitigation efforts could have been implemented. From 1980 onwards, our model identified resurgent states with increasing accuracy, leading to reliable classification starting in 1992. Additionally, we successfully applied the detection algorithm to two vector-transmitted case studies, namely outbreaks of dengue serotypes in Puerto Rico and a rapidly unfolding outbreak of plague in 2017 in Madagascar. Taken together, these findings illustrate the power of theoretically-informed machine learning techniques to develop early warning systems for the (re-)emergence of infectious diseases.
As a result of the COVID-19 pandemic, viral genomic surveillance is playing an increasing important role in public health decision-making around the world. SARS-CoV-2 evolution has been documented and analyzed at unprecedented scale and scrutiny. I will discuss how some classic evolutionary biology concepts have been used to understand variant emergence and the global spread of the virus and are now used in routine assessments of risk that inform the public health response. Models and inference methods and are often pulled into play without a model selection process, and typically with obviously violated assumptions and in the presence of significant uncertainties that they were not designed for. This presents method developers with the opportunity to have immediate impact by putting out improved methods that are more scalable and reproducible, to handle the challenging regimes relevant to real-world settings.
Understanding the immunopathogenesis of malaria requires investigation of mechanisms including parasite invasion and transmission, parasite life cycle (which illustrates the interplay of parasite and host interactions) and host immune defence. One of the most complex features of the Plasmodium parasites is the dynamic interaction of the parasite’s infection and human immunity, and thus a better understanding of this interaction is important which could greatly enhance the development process of novel drugs and a potential malaria vaccine at different stages of the parasite’s life cycle. In this talk, I will present two main models: (1), first, I will talk about a within-human-host malaria parasite model that integrates key variables that influence parasite evolution-progression-advancement, under innate and adaptive immune responses. Our models allow for the possibility of examining different recruitment functions for healthy red blood cells (HRBCs) and innate immune effector cells as well as the phenomenon of the Allee effect. The implicit role of immunity on the steady-state parasite loads and parasitemia reproduction number, Rc a threshold parameter measuring the parasite’s annexing ability of HRBCs, eventually rendering a human infectious to mosquitoes, is investigated. The model steady states and Rc, both obtained as functions of immune system variables, are analyzed at snapshots of immune sizes. Oscillatory dynamics reminiscent of malaria parasitemia are obtained. A dependence exists on the type of recruitment function used to generate HRBCs. Model results indicate that the more the immune cells are innate and adaptive, the more efficient they are at inhibiting parasite development and progression; consequently, the less severe the malaria disease in a patient. (2) Then, I will present a model for the within-mosquito dynamics of the Plasmodium falciparum malaria parasite. This considers the action and effect of blood resident human antibodies, ingested by the mosquito during a blood meal from humans, in inhibiting gamete fertilization. The model also captures subsequent developmental processes of the parasite within the mosquito such as gametogenesis, fertilization and sporogenesis culminating in the formation of sporozoites. Continuous functions are used to model the switching transition from oocyst to sporozoites as well as human antibody density variations within the mosquito. Our analysis for the within vector host dynamics showed that an increase in the efficiency of the antibodies in inhibiting fertilization results in lowering the density of sporozoites that are eventually produced. The total and average load of sporozoites produced during the within vector process are quantified. We also showed that control of sporozoites within the mosquito is possible by boosting the efficiency of antibodies. In the end, I will present a comprehensive model for the entire lifecycle of Plasmodium parasites by joining the two models.
The WHO suggests that vaccinating dogs is the most cost-effective strategy for preventing rabies in people, and reduces both human deaths and the need for post-exposure treatment. In endemic regions, which have large populations of free-roaming dogs, investment in dog vaccination has been judged insufficient. Ecological modeling studies and field experience indicate that the annual vaccination of over 70% of the dog population can stop transmission and eventually lead to elimination if repeated over several years. However, vaccination of feral dogs requirement a large number of personnel to net, restrain and complete the injection, with high associated costs. Oral rabies vaccine baits have been used with great success to control rabies in wildlife populations in North America and Europe. A bait handout strategy in India and Pakistan could be similarly successful. I use simple linear programming to determine the optimal vaccination strategies for these dog populations, and demonstrate that in spite of the currently high per bait cost, this strategy could be the most cost-effective vaccination method.