ANZIAM2021 – Mathematical Biology Special Interest Group Annual Meeting
In 2021, ANZIAM will be holding its annual conference virtually, and as a result the Mathematical Biology Special Interest Group (MBSIG) will also be meeting in a virtual setting.
Decision Making and Mathematical Biology
Monday 8th of February 2021
This years MBSIG annual meeting will be held virtually on VirtualChair, in the Plenary Room.
9:10am-10:10am: Keynote Presentation (Prof. Alex Mogilner, Courant Institute, New York University)
10:10am-10:40am: Invited Talk (Dr Kate Helmstedt, Queensland University of Technology)
10:40am-11:00am: Morning Tea
11:00am-12:30pm: Talks and Panel Discussion on Modelling and Covid Policy (Dr Oliver Maclaren, University of Auckland; Dr Freya Shearer, University of Melbourne; Dr Michael Lydeamore, Department of Health and Human Services, Victorian State Government and Monash University)
12:30pm-1:00pm: Annual General Meeting
2:00pm-2:30pm: Invited Talk (Dr Shannon Algar, University of Western Australia)
2:30pm-3:00pm: MBSIG Best Paper Prize (Yuhuang Wu, Kirby Institute)
3:00pm-3:15pm: Closing remarks
The organisers thank the Special Emphasis Year “Biological Dynamics: Mathematics of Cellular Systems to Epidemics“ hosted by the School of Mathematics and Statistics at the University of Melbourne for acting as a key sponsor of the 2021 MBSIG annual meeting.
- Dr Stuart Johnston (contact: firstname.lastname@example.org)
- Dr Vijay Rajagopal (contact: email@example.com)
Titles and Abstracts
- Prof. Alex Mogilner, Courant Institute, New York University
Modelling cell migration: from 2D to 3D
Cell migration is a fundamentally important phenomenon underlying wound healing, tissue development, immune response and cancer metastasis. Understanding basic physics of the cell migration presented a great challenge until, in the last three decades, a combination of biological, biophysical and mathematical approaches shed light on basic mechanisms of the cell migration. I will first focus on the simplest case of a single 2D cell. I will describe a model based on a nonlinear PDE free boundary problem. The model makes a non-intuitive prediction: cells often move along circular trajectories. I will show how experimental data compares to the model.
Most cells, however, migrate collectively, not individually, and in 3D. I will introduce experimental data on a collective migration of two heart progenitor cells in Ciona embryo. These cells crawl cohesively squeezing between stiff ectoderm and elastic endoderm with persistent leader-trailer polarity. I will present simulations based on the Cellular Potts Model that shed light on design principles of this motile system.
- Dr Kate Helmstedt, Queensland University of Technology
Mathematics for planning better environmental protections
There is never enough money to save all of the environment, all species, or all ecosystems. Difficult choices must be made in a dynamic, complex system where both steady and rapid changes are occurring. Data for specific ecosystems is often rare. Rarer still is concrete evidence of how management actions might change these systems, and how those actions might flow on to humans as utility we gain from the environment. Given that decisions need to be made in this highly uncertain context, this is a rich field for applying decision science, optimisation, and operations research. I will discuss some approaches we have used to tackle these complex problems that require collaboration beyond mathematics with ecologists, economists, and often policy scientists.
- Dr Oliver Maclaren, University of Auckland
Modelling and uncertainty in COVID-19 policy for Aotearoa NZ
Modelling has played a fundamental and increasingly visible role in COVID-19 policymaking. The Aotearoa NZ government has received praise for taking a science-informed approach to their response, and most of the modelling used for decision making has been made publicly available. On the other hand, the nuances of models, their data inputs, assumptions and uncertainties, and their use for counterfactual policy planning, remain challenging to communicate to various stakeholders, ranging from government decision-makers, the population as a whole, to specific local regions or communities. Furthermore, decision making has been based on a range of different model types with different strengths and weaknesses, with relatively simple models used for rapid decision making, and more detailed models being developed and used for more specific policy questions. It is not always obvious how the aims and results from different models relate.
In this presentation, I will discuss these issues in the context of some of the NZ-centric COVID-19 modelling work I have been a part of, carried out in collaboration with researchers from Te Pūnaha Matatini.
- Dr Freya Shearer, University of Melbourne
Supporting government response to COVID-19 through model-based situational assessment
A key element of epidemic decision-making is situational awareness — that is, knowing the current and potential future impact of the epidemic. Outputs from mathematical and statistical models have provided enhanced situational awareness to governments throughout the course of the COVID-19 pandemic. Key analyses include estimation of the effective reproduction number (Reff) and forecasting of epidemic activity. Accurate and timely estimation of Reff enables the tracking and planning of progress towards the control of outbreaks. Short-term forecasts of daily case incidence and hospital bed occupancy provide information on future health system requirements, which supports both clinical and public health planning.
In this talk, I will describe Australia’s situational awareness modelling program for COVID-19. I will provide an overview of the modelling outputs reported to key government decision-making committees on a weekly basis (at least) since April 2020. I will also discuss how the structure, content and communication style of our reports has evolved over the past 12 months, in response to changing epidemiology, response priorities and committee feedback.
- Dr Michael Lydeamore, Department of Health and Human Services, Victorian State Government and Monash University
Mathematical modelling for COVID-19 policy as an academic in the public service
The COVID-19 pandemic has brought mathematical modelling from a relatively niche area of science into the public spotlight. The Victorian Department of Health and Human Services (DHHS) has utilised mathematical models to inform policy on a range of issues including the introduction and relaxation of restrictions and health service capacity. I will discuss two sets of policy-relevant modelling created by my team that contributed to key policy decisions in Victoria. The first, developed in conjunction with the University of Melbourne and the Peter Doherty Institute for Infection and Immunity, is a transmission model that includes disease severity, allowing for estimates of required health service capacity (i.e. ward beds, ICU beds and ventilators) in the event of a large wave. The second is an age-structured model, developed in conjunction with Monash University, that explores potential relaxation of workplace, social and school restrictions. I will explain how these models were transformed into advice for policy, as well as my experience as a (relatively) junior academic working in the public service during an emergency response.
- Dr Shannon Algar, University of Western Australia
Using swarms as a reservoir computer
This work studies swarms as dynamical systems for reservoir computing. By example of a modified Reynolds boids model, the specific symmetries and dynamical properties of a swarm are explored with respect to a nonlinear time-series prediction task. Specifically, we seek to extract meaningful information about a predator-like driving signal from the swarm’s response to that signal. By distinguishing between the computational substrate of the swarm and a separate observation layer in which the swarm’s response is measured for use in the task we demonstrate how swarms can be used within this framework. The relationship between the reservoir computer’s performance and the swarm’s behaviour demonstrates that optimal computational properties are obtained near a phase transition regime.
- Yuhuang Wu, Kirby Institute
Impact of fluctuation in frequency of HIV / SIV reactivation during antiretroviral therapy interruption.
One of the current goals in HIV research is to develop drugs and interventions that can sufficiently delay HIV reactivation from latency, and thereby allow patients to achieve a meaningful antiretroviral therapy free HIV remission. Predicting the duration of remission expected from a given “anti-latency” intervention requires a comprehensive understanding of the dynamics of HIV reactivation from latency, which to date still remains unclear.
In this study we consider estimates of the time until viral reactivation following cessation of antiretroviral therapy in both macaques and humans. We observe that the average time until the first successful reactivation event is generally longer than the time between subsequent successful reactivations. We show that this result is consistent with time-dependent fluctuations in the frequency of HIV / SIV reactivation, rather than the constant frequency that has previously been assumed. Furthermore, we show that the effect of such a fluctuation in reactivation frequency would be to significantly reduce the efficacy of “anti-latency” interventions for HIV that aim to reduce the reactivation frequency. Both mathematical (agent-based) modelling and statistical analysis are used in this work.
This is the first analysis that considers the fluctuation of HIV/ SIV reactivation frequency and quantifies the impact of such fluctuations. It is essential to consider the impact of a fluctuating reactivation frequency, since, as we have shown, it has significant implications on treatment strategies for HIV, and may affect their future design.