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.
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.
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.