Nature’s ‘sixth’ sense? – sensing electrical fields in the natural world 

Recently, bees and spiders (and other such arthropods) have been shown to detect electrical fields (a sense known as electroreception). Whilst this novel discovery expands our view of how such organisms explore their environment, exactly how this curious sense works remains unclear. With such new discoveries, many questions arise that require both theoretical and empirical examination. In this talk, I will introduce the fundamentals of this sensory phenomena, and uncover some of the mechanical and sensory complexities that have been discovered through mathematical studies.

I will explain the mechanics of electroreception via sensory hairs and show the physical and biological feasibility of this sense. From this basis, I will delve deeper into the interactions between hairs and electrical fields introducing the concept of a sensitivity contour (regions of the solution space where hairs deflect to a given sensory threshold), and examine how a hair’s sensory capability changes owing to electrical interactions between hairs. Finally, I will discuss some of the sensory possibilities of electroreception (e.g. object identification and location detection) and the biological implications of this (e.g. foraging decisions, predator-prey behaviour).

Wednesday 07 June 2023: Dr Lucia Marucci (Bristol)

Programming cells using feedback control and whole-cell modelling

The ability to program and design ad hoc cellular and biological processes offers exciting opportunities in basic research, in the biotechnology industry and in the clinic.

Difficulties in engineering cellular phenotypes robust to changes and perturbations, as well as the lack of established tools to design biological functions across scales, still represent major roadblocks.  

In this talk I will start discussing our recent research that leverages feedback control to engineer robust cellular phenotypes. I will show results obtained using intracellular, external or multicellular controllers in both bacterial and mammalian cells, and new applications of Cybergenetics methodologies we are currently exploring (e.g., control-based analysis of gene networks’ dynamics, combination therapy design).

I will also mention a complementary approach aimed at rational and computer-aided cell design via whole-cell models (WCMs), which are mathematical models designed to capture the function of all genes and multiscale processes within a cell. The design of minimal bacterial genomes will be used as a proof-of-concept; I will also show how machine learning can support WCMs’ output interpretation and solve their computational burden challenge.

Our tools and results should make the design and control of complex cellular phenotypes and laboratory engineering a step closer.

Title: Using data science to understand society and support policy makers

Abstract: The rapid increase in data availability has opened up novel opportunities for policy makers to design policies based on better, more up-to-date data about the state of society. In this talk, I will first discuss how data and data science can be used to better understand our society, such as measuring travel flows or estimating the English index of multiple deprivation. I will then share my experience of collaborating with a range of UK policy makers, such as the data science team in 10 Downing Street, and discuss how research can be co-designed to deliver impact.

Data and analytics in public health: the world beyond Covid-19

This talk will describe the UK’s experience of Covid-19 by examining the analytical and policy developments over the last few years. The vast amounts of data available during the pandemic posed challenges as well as opportunities for those at the heart of government. Harnessing those data, and presenting key insights in the right way, was key to many of the public health interventions the UK successfully deployed. Many of the lessons from Covid-19 have also left a lasting impact on the UK’s approach to public health, including the response to Mpox in 2022 and Avian Flu in 2023. It’s an exciting time for anyone interested in health analytics and the talk will conclude with some of the latest work UKHSA is doing to build capabilities for everyday and emergency public health threats.

Gonorrhoea modelling – AMR and vaccination

Katy will present a range of approaches to modelling gonorrhoea from simple to complex and discuss how modelling can inform public health policy decisions.

Gonorrhoea is the only STI on the WHO list of high priority pathogens where AMR is a concern. STIs are still stigmatised and management is often in specialist settings (GUM) clinics. Gonorrhoea is rare at the population level and concentrated in key populations and infections are often asymptomatic. Katy will discuss how we can incorporate these clinical, behavioural and management aspects in models and how these also play into to public policy and decision making.

Dry inertial active matter

Active particles which are self-propelled by converting energy into mechanical motion represent an expanding research realm in physics and chemistry. For micron-sized particles moving in a liquid (``microswimmers''), most of the basic features have been described by using the model of overdamped active Brownian motion [1]. However, for macroscopic particles or microparticles moving in a gas, inertial effects become relevant such that the dynamics is underdamped. This introduces the new active matter class of dry inertial active matter. Recently, active particles with inertia have been described by extending the active Brownian motion model to active Langevin dynamics which includes inertia [2]. In this talk, recent developments of active particles with inertia are summarized including: inertial delay effects between particle velocity and self-propulsion direction [3], the influence of inertia on motility-induced phase separation [4] leading to a refrigerator []5, Langevin rockets [6] and the formation of active micelles (“rotelles”) by using inertial active surfactants [7].

References

[1] C. Bechinger, R. di Leonardo, H. Löwen, C. Reichhardt, G. Volpe, G. Volpe, Reviews of Modern Physics 88, 045006 (2016).

[2] H. Löwen, Journal of Chemical Physics 152, 040901 (2020).

[3] C. Scholz, S. Jahanshahi, A. Ldov, H. Löwen, Nature Communications 9, 5156 (2018).

[4] S. Mandal, B. Liebchen, H. Löwen, Physical Review Letters 123, 228001 (2019).

[5] L. Hecht, S. Mandal, H. Löwen, B. Liebchen, Physical Review Letters 129, 178001 (2022).

[6] A. R. Sprenger, S. Jahanshahi, A. V. Ivlev, H. Löwen, Physical Review E 103, 042601 (2021).

[7] C. Scholz, A. Ldov, T. Pöschel, M. Engel, H. Löwen, Science Advances 7, eabf8998 (2021).

Wednesday 23 November 2022: Dr Stephen Parnell (Warwick)
Detection and control of invasive plant pests and diseases; an epidemiological modelling approach

Plants absorb cardon dioxide, provide us with food, and deliver a wide range of other ecosystem services crucial for life. Introductions of exotic plant pests and diseases have increased sharply in recent times threatening this. Prominent recent examples include Ash Dieback Disease, which has been estimated to destroy 90% of ash trees in UK following its first discovery in 2012. Detection and management of pests and diseases across vast and complex host landscapes is challenging. Models allow us to capture current epidemiological understanding and identify strategies to detect and control epidemics. In this seminar, drawing on a range of examples, I will demonstrate how epidemiological models have been used to address a wide range of practical questions from: where and when should surveillance be conducted? To, how should stakeholders be engaged with to maximise the success of a disease control policy?

Dr Kat Rock (Warwick) - in person
Epidemiological modelling for elimination

In this presentation I will highlight how mechanistic modelling and health economic analyses are being used to support the elimination of gambiense human African trypanosomiasis (gHAT, African sleeping sickness) – targeted for elimination of transmission by 2030. gHAT is a vector-borne infection, transmitted by tsetse in Central and West Africa. I shall present work of the HAT modelling and economic predictions for policy (HAT MEPP) project, which focuses on using regional, longitudinal human case data to parameterise location-specific models and provide tailored recommendations for strategies aimed at elimination. I will overview how data from other sources – including tsetse monitoring and cost collection – has contributed to these analyses and impacted our recommendations for policy makers. During the presentation I will demonstrate the HAT MEPP graphical user interface that was developed in conjunction with national programmes to support dissemination of modelling results in an accessible way for non-modellers. Finally I will discuss the challenges of attempting to select cost-effective strategies for infections targeted for elimination.

Wednesday 09 November 2022: Dr Edson Utazi (WorldPop, School of Geography and Environmental Science, University of Southampton)

Bayesian hierarchical modelling approaches for combining information from multiple sources to produce annual estimates of national immunization coverage

Estimates of national immunization coverage are crucial for guiding policy and decision-making in national immunization programs and setting the global immunization agenda. WHO and UNICEF estimates of national immunization coverage (WUENIC) are produced annually for various vaccine-dose combinations and all WHO Member States using information from multiple data sources and a deterministic computational logic approach. The WUENIC approach employs ad hoc estimation rules to supplement administrative and official coverage data reported to WHO and UNICEF with coverage estimates obtained from nationally representative household surveys. The approach, however, is incapable of characterizing the uncertainties inherent in coverage measurement and estimation. It also provides no statistically principled way of exploiting and accounting for the interdependence in immunization coverage data collected for multiple vaccines, countries and time points. In this talk, I will present novel Bayesian hierarchical modelling approaches for producing accurate estimates of national immunization coverage and associated uncertainties. I will discuss two candidate models that we proposed: a balanced data single likelihood (BDSL) model and an irregular data multiple likelihood (IDML) model, both of which differ in their handling of missing data and characterization of the uncertainties associated with the multiple input data sources. I will present a simulation study that demonstrates a high degree of accuracy of the estimates produced by the proposed models, and which also shows that the IDML model is the better model. I will then show the results obtained from an application of the methodology to produce coverage estimates for select vaccine-dose combinations for the period 2000-2020. The utility and reproducibility of the methodology are enhanced by a contributed R package ‘imcover’ implementing the No-U-Turn Sampler (NUTS) in the Stan programming language.

Prof Mark van Rossum (Nottingham)

Energy efficient learning with in neural networks

The brain is one of the most energy intense organs. Some of this energy is used for neural information processing, however, fruitfly experiments have shown that also synaptic plasticity is metabolically costly. First we will present estimates of this cost, introduce a general model of this cost, and compare it to costs in computers. Next, we turn to a supervised artificial network setting and explore a number of strategies that can save energy need for plasticity, either by modifying the cost function, by restricting plasticity, or by using less costly transient forms of plasticity. Finally, we will discuss adaptive strategies and possible relevance for computer hardware.

Wednesday 19 October 2022: Dr Adam Noel (School of Engineering, Warwick)
Molecular Communication Engineering: Signal Propagation and Information at the Microscale

Molecular communication is an emerging field that applies communication engineering tools to molecular signalling. My talk will focus on introducing molecular communication from the context of communication engineering. I will then briefly present an on-going project where we seek to understand and control signalling in microscale biological systems. The case studies for this project are competitive dynamics in bacteria and glucose regulation in organ-on-a-chip systems.

Wed 22 June 2022
Merve Alanyali (Head of Data Science Academic Partnerships and Research, LV= General Insurance)
From Warwick to LV= General Insurance: A tale of a data scientist

As a Warwick alum, in this talk I will be covering how my career has changed across years starting from the postgraduate days and how my background is helping me with my current position at LV= General Insurance. I will be covering main pillars of our data science team; “delivery” by showcasing example projects, “technical excellence” by giving example of our team best practices including delivery framework to Python packages we open sourced, and “research and innovation” by talking about our academic partnerships and research projects ending with a discussion around the future of data science as well as academia-industry collaborations.

Wednesday 08 June 2022 - Nikolai Bode (Bristol) (virtual)
Smart swarms or mindless mobs: social behaviour in pedestrian crowds at different spatio-temporal scales

The movement dynamics of pedestrian crowds are a paradigmatic example for emergent behaviour, where the interactions between many individuals give rise to the system’s behaviour. It seems natural to ask if this mode of operation functions well or not. In this talk, I will try to convince you that pedestrian crowds are neither particularly smart, nor entirely mindless by taking you for a walk through our patchy understanding of pedestrian social behaviour. In my research I am especially interested in relating theoretical models to empirical data. So, you can expect to see a range of models being used in attempts to explain different data sets.

Wednesday 25 May 2022 - Shreyas Mandre (Warwick) (in person)
Functional interpretation for transverse arches of the human foot

The fossil record indicates that the emergence of arches in human ancestral feet coincided with a transition from an arboreal to a terrestrial lifestyle. Propulsive forces exerted during walking and running load the foot under bending, which is distinct from those experienced during arboreal locomotion. I will present mathematical models with varying levels of detail to illustrate a simple function of the transverse arch. Just as we curve a dollar bill in the transverse direction to stiffen it while inserting it in a vending machine, the transverse arch of the human foot stiffens it for bending deformations. A fundamental interplay of geometry and mechanics underlies this stiffening -- curvature couples the soft out-of-plane bending mode to the stiff in-plane stretching deformation. This result overturns a century-old theory that the longitudinal arch underlies the bending rigidity of the human foot. In addition to presenting a functional interpretation of the transverse arch of the foot, this study also furnishes an interpretation of the fossil record implying that the evolution of the transverse arch may have preceded the longitudinal arch.

Wednesday 11 May 2022 - Rebecca Killick (Lancaster)
Tackling Global Issues: Change

Traditional statistical model building assumes that the same model (and fitted parameters) can describe the data at any point in the observed process. To tackle early violations to this statisticians introduced regressors to describe time-varying features such as trend and seasonality. These have enabled model building to become engrained in everyday applications across all fields. But what happens when this neat assumption of a static model is no longer appropriate?

The simplest departure from this static model assumption is to piece together static models, which we understand well. Questions then surface around where and how many of these static models should we use? This field of statistical modelling is called changepoint detection.

This talk will give a potted history of the challenges in changepoint detection that I have been preoccupied with over the last ten or so years. Always motivated by applications of changepoint detection to real-world problems, the tour of statistical challenges will be littered with applications to different areas including environment, health and business. To conclude the talk I will describe some of the existing challenges that must be addressed by the community if changepoint detection is going to remain relevant to the world.

Within the talk, my aim is to convey the intuition behind the field, its challenges and proposed solutions. As such, the talk will be accessible to a general audience.

Wednesday 16 March 2022 - Nicole Nisbett (Leeds)

Unearthing climate action discourses with sentence transformers

In this talk I explain how we use natural language processing and the BERTopic algorithm to uncover topics from text. The algorithm uses a transformers-based topic modeling approach, embedding models, clustering, and vector space representations to provide meaning to unstructured text data. We apply these models to large volumes of Twitter data on the climate crisis discussion around the UN Climate Change Conferences (COP) to understand the core themes in the public debate and how these have changed between 2014 and 2021.

Wednesday 09 March 2022 - Yonatan Berman (KCL)

A Simplified Mortality Multiplier Method: New Estimates of Wealth Concentration

We derive a method for estimating top wealth shares using estate tax data and minimal data on mortality. It is a simplification of the mortality multiplier method. We first investigate the conditions in which the novel method provides accurate wealth concentration estimates and validate it empirically. We then use it to produce novel long-run historical series of the distribution of wealth for Belgium, Japan, and South Africa, where data have not been exploited yet. This is particularly important for historical inequality studies, given that existing series of wealth inequality are very scarce and only cover a few developed countries.

This is joint work with Facundo Alvaredo and Salvatore Morelli

Wednesday 16 February 2022 - Simon Schnyder (Kyoto)

Epidemics of infectious diseases posing a serious risk to human health have occurred throughout history. During the ongoing SARS-CoV-2 epidemic there has been much debate about policy, including how and when to impose restrictions on behavior. Under such circumstances policymakers must balance a complex spectrum of objectives, suggesting a need for quantitative tools. Here we show how costly interventions, such as taxes or subsidies on behaviour, can be used to exactly align individuals' decision making with government preferences even when these are not aligned. We assume that choices made by individuals give rise to Nash equilibrium behavior. We focus on a situation in which the capacity of the healthcare system to treat patients is limited and identify conditions under which the disease dynamics respect the capacity limit. In particular we find an extremely sharp drop in peak infections as the maximum infection cost in the government's objective function is increased. This is in marked contrast to the gradual reduction without government intervention. The infection costs at which this switch occurs depend on how costly the intervention is to the government. We find optimal interventions that are quite different to the case when interventions are cost-free.

Wednesday 09 February 2022 - Tommaso Lorenzi (Politecnico di Torino)
Dissecting the evolutionary and spatial dynamics of cancer through partial differential equations

A range of mathematical models have been used to gain a more in-depth theoretical understanding of different aspects of cancer dynamics. In this talk, deterministic, continuum models formulated as partial differential equations will be considered. The first part of the talk will focus on partial integro-differential equations modelling the eco-evolutionary dynamics of cancer cells in vascularised tumours. In the second part of the talk, attention will turn to models of avascular tumour growth that comprise coupled systems of nonlinear partial differential equations, which reflect the heterogeneous cellular composition of the tumour micro-environment. Analytical and numerical results summarising the behaviour of the solutions to the model equations will be presented and the biological insight generated by these results will be discussed.

Wednesday 19 January 2022 - Diana Khoromskaia (Crick Institute)
Active morphogenesis of patterned epithelial shells
Shape transformations of epithelial tissues in three dimensions, which are crucial for embryonic development or in vitro organoid growth, can result from active forces generated within the cytoskeleton of the epithelial cells. How the interplay of local differential tensions with tissue geometry and with external forces results in tissue-scale morphogenesis remains an open question. Here, we describe epithelial sheets as active viscoelastic surfaces and study their deformation under patterned internal tensions and bending moments. In addition to isotropic effects, we take into account nematic alignment in the plane of the tissue, which gives rise to shape-dependent, anisotropic active tensions and bending moments. We present phase diagrams of the mechanical equilibrium shapes of pre-patterned closed shells and explore their dynamical deformations. Our results show that a combination of nematic alignment and gradients in internal tensions and bending moments is sufficient to reproduce basic building blocks of epithelial morphogenesis, including fold formation, budding, neck formation, flattening, and tubulation.

Preprint: D. Khoromskaia and G. Salbreux, arXiv:2111.12820 [physics.bio-ph]

Wednesday 12 January 2022: Sophie Meakin (London School of Hygiene and Tropical Medicine, and MathSys alumna)
Comparative assessment of methods for short-term forecasts of COVID-19 admissions in England at the local level
Forecasting healthcare demand is essential in epidemic settings, both to inform situational awareness and facilitate resource planning. We made weekly forecasts of daily COVID-19 hospital admissions for National Health Service (NHS) Trusts in England between August 2020 and April 2021 using three disease-agnostic forecasting models: a mean ensemble of autoregressive time series models, a linear regression model with 7-day-lagged local cases as a predictor, and a scaled convolution of local cases and a delay distribution. We compared their point and probabilistic accuracy to a mean-ensemble of them all, and to a simple baseline model of no change from the last day of admissions. We measured predictive performance using the Weighted Interval Score (WIS) and considered how this changed in different scenarios (the length of the predictive horizon, the date on which the forecast was made, and by location), as well as how much admissions forecasts improved when future cases were known.

Wednesday 08 December 2021 - Rosanna Barnard (London School of Hygiene and Tropical Medicine)
Modelling SARS-CoV-2 transmission in England: vaccines, variants and variability
In this talk I will present mathematical modelling work considering the dynamics of SARS-CoV-2 transmission in England. I will discuss the challenge of modelling disease transmission within an ever-evolving epidemiological context, with consideration of COVID-19 vaccinations, variants of concern, and various uncertainties such as behavioural changes. I will outline the compartmental modelling framework we use to describe SARS-CoV-2 transmission, as well as the data sources and the model fitting processes used. I will give an overview of various modelling outputs we have produced which have informed the UK response.

Wednesday 01 December 2021 - David SibleyLink opens in a new windowLink opens in a new window (Loughborough)
Coupled dynamics of fluid systems with mass transfer
In this talk we will consider how to develop models for systems of fluids that involve complex behaviour such as mass transfer. We will consider multiphase systems of a single component in solid, liquid and vapour form, and systems that involve both fluids and colloids. In both situations the dynamics should account for conserved behaviour such as surface tension driven flow, and non-conserved behaviour due to evaporation/condensation. We will discuss results for the solid-liquid-vapour system in detail. We consider a regime where a droplet of liquid spreads over a solid and in the presence of a vapour, all of the same material (such as ice-water-water vapour). We consider dynamics where vapour can condense to liquid which in turn can freeze to solid throughout a droplet spreading process, and indeed vice-versa. By developing a thin-film model capturing phase-change, surface tension, density contrast, and interfacial potentials, a rich phase diagram will be explored---and complex dynamics such as layered solid growth driven by forces at the contact lines described [work in collaboration with Pablo Llombart (Universidad Complutense Madrid), Eva G. Noya (CSIC Madrid), Andrew J. Archer, and Luis G. MacDowell (Universidad Complutense Madrid); based on Nat. Commun. 12, 239 (2021)].

Wednesday 24 November 2021 - Alejandra Avelos Pacheco (Harvard)
Multi-study Bayesian factor regression analysis for heterogenous high-dimensional biological data
Two key challenges in modern statistical applications are the large amount of information recorded per individual, and that such data are often not collected all at once but in batches. These batch effects can be complex, causing distortions in both mean and variance. We propose a novel sparse latent factor regression model to integrate such heterogeneous data. The model provides a tool for data exploration via dimensionality reduction and sparse low-rank covariance estimation while correcting for a range of batch effects. We study the use of several sparse priors (local and non-local) to learn the dimension of the latent factors. We provide a flexible methodology for sparse factor regression which is not limited to data with batch effects. Our model is fitted in a deterministic fashion by means of an EM algorithm for which we derive closed-form updates, contributing a novel scalable algorithm for non-local priors of interest beyond the immediate scope of this paper. We present several examples, with a focus on bioinformatics applications. Our results show an increase in the accuracy of the dimensionality reduction, with non-local priors substantially improving the reconstruction of factor cardinality. The results of our analyses illustrate how failing to properly account for batch effects can result in unreliable inference. Our model provides a novel approach to latent factor regression that balances sparsity with sensitivity in scenarios both with and without batch effects and is highly computationally efficient; and opens new avenues for future research on dimension-reduction-based data integration.

Wednesday 17 November 2021 - Katerina Kaouri (Cardiff)
Modelling across scales: from fertilization and embryogenesis to COVID-19 modelling
I will present an overview of some interdisciplinary challenges I have been working on, in collaboration with experimentalists and industry. I will start with the important societal challenge of improving In-Vitro Fertilization (IVF) therapies, proceed with models of calcium signalling in IVF and in embryogenesis. I will also relay our work on modelling airborne transmission of COVID-19 in indoor spaces which has been developing over the last year in collaboration with Oxford University and the Welsh government.

Dr Katerina KaouriLink opens in a new windowLink opens in a new window is a Senior Lecturer in Applied Mathematics at Cardiff University. She holds a DPhil in Applied Mathematics from Oxford University. She has been working on a range of interdisciplinary challenges mainly in biology and medicine, in collaboration with industry and society. She has been involved in several public engagement activities such as the SciShops.eu Link opens in a new windowLink opens in a new windowproject where 18 organisations in 12 EU countries tackled pressing societal challenges. She is also the founder and coordinator of the CY Study Groups with IndustryLink opens in a new windowLink opens in a new window, modelling workshops in Cyprus which are part of the European Study Groups with Industry series. She co-founded the non-profit SciCo Cyprus and the Mediterranean Science Festival to bring science closer to people. She is a TEDx speaker and a TED-Ed educatorLink opens in a new windowLink opens in a new window (4.2 million views) and has given numerous talks to schools and other public venues. In the last year she has been working on COVID-19 modelling and participates in the Technical Advisory Group of the Welsh government.

Wednesday 03 November 2021 - Ruth Baker, University of Oxford
Teasing apart the impact of electric fields on single-cell motility
Cell motility in response to environmental cues forms the basis of many developmental processes in multicellular organisms. One such environmental cue is an electric field, which induces a form of motility known as electrotaxis. Electrotaxis has evolved in a number of cell types to guide wound healing, and has been associated with different cellular processes, suggesting that observed electrotactic behaviour is likely a combination of multiple distinct effects arising from the presence of an electric field. In order to determine the different mechanisms by which observed electrotactic behaviours emerge, and thus to design electric fields that can be applied to direct and control electrotaxis, we require accurate quantitative predictions of cellular responses to externally-applied fields. Here, we use mathematical modelling to formulate and parametrise a variety of hypothetical descriptions of how cell motility may change in response to an electric field. We calibrate our model to observed data using synthetic likelihoods and Bayesian sequential learning techniques, and demonstrate that electric fields impact cellular motility in three distinct ways.

Wednesday 20 October 2021 - Gabriela Gomes, University of Strathclyde
Frailty variation in population dynamics: Adventures and misadventures of an elusive concept
Selection acting on unmeasured individual variation is a common source of bias in the analysis of populations. It has been shown to affect measured rates of mortality1-3, the survival of endangered species4,5, the scope of neutral theories of biodiversity and molecular evolution6,7, the measured risk of diseases whether non-communicable8,9 or infections10-15, and the efficacy of interventions such as vaccines16-20 or symbionts21,22. Building on this knowledge, we have addressed how selection on individual variation might have affected the course of the coronavirus disease (COVID-19) pandemic23.

This form of variation that responds to selection and impacts within-cohort population dynamics, termed frailty variation by Vaupel et al. (1979)2, constitutes a most genuine phenomenon that scientific disciplines have been dismissing for decades. I will present some examples and discuss the mixed attitudes towards what is arguably the most elusive concept in population dynamics.

References:

1. Keyfitz, N. & Littman, G. (1979) Popul. Stud. 33, 333-342.
2. Vaupel, J., Manton, K. & Stallard, E. (1979) Demography 16, 439-454.
3. Vaupel, J., & Yashin, A. (1985) Am. Stat. 39, 176-185.
4. Kendall, B. E. & Fox, G. A. (2002) Conserv. Biol. 16, 109-116.
5. Jenouvrier, S, Aubry, L. M., Barbraud, C, Weimerskirch, H & Caswell, H. (2018) J. Anim. Ecol. 87, 212-222.
6. Steiner, U. K. & Tuljapurkar, S. (2012) Proc. Natl. Acad. Sci U. S. A. 109, 4684-4689.
7. Gomes, M. G. M., King, J. G., Nunes, A., Colegrave, N. & Hoffmann, A. (2019) Ecol. Evol. 16, 8995-9004.
8. Aalen, O. O., Valberg, M., Grotmol, T. & Tretli, S. (2015) Int. J. Epidemiol. 4, 1408-1421
9. Stensrud, M. J. & Valberg, M. (2017) Nat. Commun. 8, 1165.
10. Anderson, R. M., Medley, G. F., May, R. M. & Johnson, A. M. (1986) IMA J. Math. Appl. Med. Biol. 3, 229-263.
11. Dwyer, G., Elkinton, J. S. & Buonaccorsi, J. P. (1997) Am. Nat. 150, 685-707.
12. Smith, D. L., Dushoff, J., Snow, R. W. & Hay, S. I. (2005) Nature 438, 492-495.
13. Bellan, S. E., Dushoff, J., Galvani, A. P. & Meyers, L. A. (2015) PLOS Med. 12, e1001801.
14. Gomes, M. G. M., et al. (2019) Nat. Commun. 10, 2480.
15. Corder, R. M., Ferreira, M. U. & Gomes, M. G. M. (2020) PLOS Comput. Biol. 16, e1007377.
16. Halloran, M. E., Longini, I. M. Jr. & Struchiner, C. J. (1996) Am. J. Epidemiol. 144, 83-97.
17. O’Hagan, J. J., Hernán, M. A., Walensky, R. P. & Lipsitch, M. (2012) AIDS 26, 123.
18. Gomes, M. G. M., et al. (2014) PLOS Pathog. 10, e1003849.
19. Gomes, M. G. M., Gordon, S. B. & Lalloo, D. G. (2016) Vaccine 34, 3007.
20. Langwig, K. E., et al. (2017) mBio 8, e00796-17.
21. Pessoa, D., et al. (2016) PLOS Comput. Biol. 10, e1003773.
22. King, J. G., Souto-Maior, C., Sartori, L. M., Maciel-de-Freitas, R. & Gomes, M. G. M. (2018) Nat. Commun. 9, 1-8.
23. Gomes, M. G. M., et al. (2020) medRvix 10.1101/2020.04.27.20081893.

Wednesday 13 October 2021 - Bruce Edmonds (Manchester Met)
Staging abstraction from a complex model of voter turnout
Simpler models (e.g. mathematical models that are analytically tractable) can rigorously determine the overall behaviour of a model, but the approximations and assumptions necessary to make these solvable can make the connection to what is being modelled weak. Complicated simulation models can more directly represent observed processes (as they are understood), i.e. are relevant, but are difficult to rigorously understand. Thus, it is increasingly common to use a combination of simulation and analytical models when trying to understand complex systems. This talk discusses some work which extends and formalises this approach, staging the abstraction into a sequence of models, starting with a complicated, descriptive model (that represents) but then progressively simplifying this into 3 more stages with an analytical model at the 'top'. This attempts to obtain both rigour and relevance of the modelling but at the cost of a more complicated 'pile' of models and a lot more work. The particular target that this work focussed on was "why people bother to go out and vote", which was part of a collaborative grant between the department of Theoretical Physics and the Cathie Marsh Centre (for quantitative social science) at the University of Manchester, with us modellers, at the Centre for Policy Modelling, Manchester Met. University being the bridge between the two.