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Mathematics Institute

Louise Dyson

  Louise Dyson

Dr Louise Dyson

Reader

Office: Mathematical Sciences Building, 5.22
 Phone: +44 (0)24 765 24975

 
Email: L dot Dyson at warwick dot ac dot uk

Teaching Responsibilities 2022/23:

Term 2: MA4E7 Population Dynamics: Ecology & Epidemiology

About me

I am a Reader in Epidemiology appointed jointly between the Mathematics Institute and the School of Life Sciences. I am also a member of the Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research (SBIDER), a cross-departmental group bringing together researchers from different disciplines to better understand the biological world. My research interests involve using techniques from mathematics and statistical physics to analyse biological and epidemiological systems. I am particularly interested in work with strong experimental links and in discovering the simplest possible explanatory mechanisms for observed data.

List of publications

A full list of publications can be found here.

Epidemiology

COVID-19

For information regarding my recent work modelling COVID-19 as a member of the Scientific Pandemic Influenza Group on Modelling (SPI-M-O), see the SBIDER COVID-19 webpages. Preprints and publications arising from this work will be added below.

  1. Leng T, Hill EM, Thompson RN, Tildesley MJ, Keeling MJ, Dyson L. Assessing the impact of lateral flow testing strategies on within-school SARS-CoV-2 transmission and absences: a modelling study PLOS Comp Biol. (accepted).
  2. Keeling MJ, Dyson L, Tildesley M, Hill EM, Moore SM. Comparison of the 2021 COVID-19 'Roadmap' Projections against Public Health Data. medRxiv. (2022)
  3. Leng T, Hill EM, Holmes A, Southall E, Thompson RN, Tildesley MJ, Keeling MJ, Dyson L. Quantifying within-school SARS-CoV-2 transmission and the impact of lateral flow testing in secondary schools in England. Nat Comms. (2022) 13, 1106
  4. Dyson L Modelling results on the impact of COVID-19 testing in schools The Lancet Infectious Diseases (2022)
  5. Keeling MJ, Dyson L, Guyver-Fletcher G, Holmes A, Semple MG, Tildesley MJ, Hill EM. Fitting to the UK COVID-19 outbreak, short-term forecasts and estimating the reproductive number. Statistical Methods in Medical Research. (2022)
  6. Moore S., Hill EM, Dyson L, Tildesley MJ, Keeling MJ The impacts of increased global vaccine sharing on the COVID-19 pandemic; a retrospective modelling study. medRxiv (2022)
  7. Marshall GC, Skeva R, Jay C, Silva ME, Fyles M, House TC, Davis E, Pi L, Medley GF, Quilty BJ, Dyson L. Public perceptions and interactions with UK COVID-19 Test, Trace and Isolate policies, and implications for pandemic infectious disease modelling. medRxiv. (2022).
  8. Guzmán-Rincón LM, Hill EM, Dyson L, Tildesley MJ, Keeling MJ. Bayesian Estimation of real-time Epidemic Growth Rates using Gaussian Processes: local dynamics of SARS-CoV-2 in England. medRxiv. (2022)
  9. Keeling MJ, Brooks-Pollock E, Challen RJ, Danon L, Dyson L, Gog JR, Guzman-Rincon L, Hill EM, Pellis LM, Read JM, Tildesley M. Short-term Projections based on Early Omicron Variant Dynamics in England. medRxiv. (2021)
  10. Keeling M.J., Thomas, A., Hill E.M., Thompson, R.N., Dyson L., Tildesley M.J., Moore, S. Waning, Boosting and a Path to Endemicity for SARS-CoV-2. medRxiv. (2021)
  11. Keeling MJ, Guyver-Fletcher G, Holmes A, Dyson L, Tildesley MJ, Hill EM, Medley GF. Precautionary breaks: planned, limited duration circuit breaks to control the prevalence of COVID-19. Epidemics (2021) 27:100526.
  12. Dyson L, Hill EM, Moore S, Curran-Sebastian J, Tildesley MJ, Lythgoe KA, House T, Pellis L, Keeling MJ. Possible future waves of SARS-CoV-2 infection generated by variants of concern with a range of characteristics. Nat Comms. (2021) 12:5730.
  13. Challen R, Dyson L, Overton CE, Guzman-Rincon LM, Hill EM, Stage HB, Brooks-Pollock E, Pellis L, Scarabel F, Pascall DJ, Blomquist P. Early epidemiological signatures of novel SARS-CoV-2 variants: establishment of B. 1.617. 2 in England. medRxiv. (2021).
  14. Sachak-Patwa R, Byrne HM, Dyson L, Thompson RN. The risk of SARS-CoV-2 outbreaks in low prevalence settings following the removal of travel restrictions. Commun. Med. (2021) 1, 39.
  15. Enright, J.*, Hill, E.M.*, Stage, H.B., Bolton, K.J., Nixon, E.J., Fairbanks, E.L., Tang, M.L., Brooks-Pollock, E., Dyson, L., Budd, C.J., Hoyle, R.B. SARS-CoV-2 infection in UK university students: lessons from September-December 2020 and modelling insights for future student return. Royal Society Open Science. (2021).
  16. Southall, E.*, Holmes, A.*, Hill, E.M., Atkins, B.D., Leng, T., Thompson, R.N., Dyson, L., Keeling, M.J. and Tildesley, M.J.. An analysis of school absences in England during the Covid-19 pandemic. BMC medicine. (2021) Dec;19(1):1-4.
  17. Hill, E.M., Atkins, B.D., Keeling, M.J., Dyson, L, and Tildesley, M.J. A network modelling approach to assess non-pharmaceutical disease controls in a worker population: An application to SARS-CoV-2. PLOS Comp Biol. (2021) Jun 16;17(6):e1009058.
  18. Hill, E.M., Atkins, B.D., Keeling, M.J., Tildesley, M.J., and Dyson, L Modelling SARS-CoV-2 transmission in a UK university setting. Epidemics (2021) Sep (36) 100476
  19. Challen R, Brooks-Pollock E, Read JM, Dyson L, Tsaneva-Atanasova K and Danon L. Risk of mortality in patients infected with SARS-CoV-2 variant of concern 202012/1: matched cohort study. BMJ (2021) Mar 10;372.
  20. Moore S, Hill EM, Tildesley MJ, Dyson L, and Keeling MJ. Vaccination and non-pharmaceutical interventions for COVID-19: a mathematical modelling study. The Lancet Infectious Diseases. (2021) Jun 1;21(6):793-802.
  21. Moore S, Hill EM, Dyson L, Tildesley M, and Keeling MJ. Modelling optimal vaccination strategy for SARS-CoV-2 in the UK. PLOS Comp Biol (2021) May 6;17(5):e1008849.
  22. Keeling MJ, Tildesley MJ, Atkins BD, Penman B, Southall E, Guyver-Fletcher G, Holmes A, McKimm H, Gorsich EE, Hill EM, and Dyson L. The impact of school reopening on the spread of COVID-19 in England. Phil Trans B. (2021) Jul 19;376(1829):20200261
  23. Lowe-Power T., Dyson L., Polter A.M. A generation of junior faculty is at risk from the impacts of COVID-19. PLoS Biol (2021) 19(5): e3001266.
  24. Keeling M.J., Hill E.M., Gorsich E.E., Penman B., Guyver-Fletcher G., Holmes A., Leng, T., McKimm, H., Tamborrino, M., Dyson, L., and Tildesley, M.J. Predictions of COVID-19 dynamics in the UK: Short-term forecasting and analysis of potential exit strategies. PLoS Comp Biol 17(1): e1008619. (2021)
  25. Funk S, Abbott S, Atkins BD, Baguelin M, Baillie JK, Birrell P, Blake J, Bosse NI, Burton J, Carruthers J, Davies NG,... Dyson L,... Short-term forecasts to inform the response to the Covid-19 epidemic in the UK. medRxiv. (2020).
Targeting treatment of yaws

Yaws is a bacterial infection that can cause lesions in the skin and bones and is primarily found in tropical areas. We are investigating whether household contacts constitute a major contribution to disease transmission to evaluate whether targeting treatment at diagnosed cases and their contacts is a feasible strategy for controlling or eliminating Yaws. This work is in collaboration with Michael Marks at the London School for Hygiene and Tropical Medicine and Deirdre Hollingsworth here in Warwick.

  1. Holmes, A, Tildesley, MJ, Dyson, L Approximating steady state distributions for household structured epidemic models. J. Theor. Biol (2022) 7;534:110974
  2. Holmes, A, Tildesley, M.J., Solomon, A.W., Mabey, D.C.W, Sokana, O., Marks, M. and Dyson, L. Modeling Treatment Strategies to Inform Yaws Eradication Emerging infectious diseases 26.11: 2685 (2020)
  3. Dyson, L., Crook, O., Bishop, A., Marks, M. and Hollingsworth, T.D. Targeted treatment of yaws with household contact tracing: How much do we miss? Am. J. Epidemiol. 187:4 837-844 (2018)
Systematic non-adherence to mass drug administration

For many diseases that can be safely and effectively treated with appropriate drugs we attempt to reduce disease impact through a mass drug administration campaign, in which we repeatedly (for example, annually) treat as many people in the community as possible, regardless of their disease status. These campaigns are common in many diseases, however their success depends upon a good implementation of the campaign. It is generally accepted that high coverage levels (the percentage of of people treated in each round) is essential good efficacy of the campaign. However, the quality of that coverage is also highly important, particularly the amount of overlap between different rounds of treatment. If we continually treat the same people, that is less good than gradually treating everyone over multiple years. I am investigating ways of modelling this effect, and the extent to which it is a problem in disease elimination campaigns.

  1. Dyson, L., Stolk, W., Farrell, S.H. and Hollingsworth, T.D. Measuring and modelling the effects of systematic non-adherence to mass drug administration. Epidemics. 18:56-66 (2017)
Modelling to support the eradication of NTDs

The World Health Organisation (WHO) is targeting control efforts at a set of neglected tropical diseases (NTDs), a group of communicable diseases that affect more than one billion people worldwide. I use mathematical modelling to support these efforts in a variety of ways, some targeted at particular diseases, others that aim to be more widely applicable across diseases.

  1. Southall, E. Tildesley MJ, Dyson L How early can an upcoming critical transition be detected? medRxiv (2022)
  2. Southall, E., Brett, T.S., Tildesley, M.J. and Dyson, L. Early-warning signals of infectious disease transitions: a review. Journal of the Royal Society Interface (2021) 18: 20210555
  3. Southall, E., Tildesley, M.J. and Dyson, L. Prospects for detecting early warning signals in discrete event sequence data: Application to epidemiological incidence data PLOS Comp. Biol. 16(9): e1007836, (2020)
  4. Gama Dessavre, A., Southall, E., Tildesley, M.J. and Dyson, L. The problem of detrending when analysing potential indicators of disease elimination J. Theor. Biol. 481, 183-193, (2019)
  5. Dyson, L. and Hollingsworth, T.D. Diagnosing risk factors alongside mass drug administration using serial diagnostic tests—which test first? Trans. R. Soc. Trop. Med. Hyg. 112(7), 342348 (2018)
  6. Hollingsworth, T.D., ... , Dyson, L., ... , NTD Modelling Consortium Quantitative analyses and modelling to support achievement of the 2020 goals for nine neglected tropical diseases. Parasites & Vectors 8:630 (2015)
  7. Chapman, L.A.C., Dyson, L., Courtenay, O., Chowdhury, R., Bern, C., Medley, G.F. and Hollingsworth, T.D. Quantification of the natural history of visceral leishmaniasis and consequences for control. Parasites & Vectors 8:521 (2015)

Stochastic dynamics

Noise-induced bistable states

Noise-induced bistability is often found in systems with two deterministic stable steady states, where the addition of noise simply moves the system between the two states. We instead considered a situation where these steady states are not present at all in the deterministic system, but are instead a consequence of multiplicative noise induced by low population numbers in a system with an autocatalytic reaction. This type of noise-induced bistability may be distinguished in experimental systems from the more usually described type, by the presence of a critical population size, above which bistability ceases to occur.

  1. Biancalani, T., Dyson, L. and McKane, A.J. Noise-Induced Bistable States and Their Mean Switching Time in Foraging Colonies. Phys. Rev. Lett. 112(3) (2014)
  2. Biancalani, T.*, Dyson, L.* and McKane, A.J. The statistics of fixation times for systems with recruitment. J. Stat. Mech. P01013 (2015)
  3. Dyson, L.*, Yates, C.*, Buhl, J. and McKane, A.J. Onset of collective motion in locusts is captured by a minimal model. Phys. Rev. E. 052708 (2015)
Stochastic modelling of cellular migration with volume exclusion

Partial differential equations (PDE) are widely used in the modelling of cellular migration, enabling the use of both analytical and numerical techniques for studying such systems. However, these equations are rarely explicitly derived from the underlying behaviours of individual cells and thus it is difficult to parameterise and perturb systems on an individual level. Moreover, in many biological systems there is not a large enough number of individuals to justify the continuum approximation. I am interested deriving PDE approximations to off-lattice individual-based models (IBMs), particularly those with volume exclusion.

  1. Dyson, L., Maini, P.K. and Baker, R.E. Macroscopic limits of individual-based models for motile cell populations with volume exclusion. Phys. Rev. E 86, 031903 (2013)
  2. Dyson, L., and Baker, R.E. The importance of volume exclusion in modelling cellular migration. J. Math. Biol. 71 (3), 691-711, (2015)

Mathematical biology

Individual-based modelling the migration of cranial neural crest cells during embryo development

Cell migration and differentiation during embryo development is instrumental in transforming a clump of cells into a functioning organism. One such migration is that of cranial neural crest cells (CNCCs), which give rise to bone, cartilage, nerves and connective tissue in the face. Elucidating the mechanisms underlying the migration requires close collaborations between experimentalists and mathematical modellers. I have formulated an individual-based model of this system which is then used to predict experimental outcomes, thus testing our modelling assumptions and hypotheses. This work is in close collaboration with Prof. Paul Kulesa at the Stowers Institute for Medical Research, and has lead to a further collaboration studying the migration of the trunk neural crest.

  1. McLennan, R.*, Dyson, L.* Prather, K.W., Morrison, J.A., Baker, R.E., Maini, P.K. and Kulesa, P.M. Multiscale Mechanisms of Cell Migration During Development: Theory and Experiment. Development. 139, 2935-2944, 2012 (Faculty of 1000 Recommended).
  2. Dyson, L., Holmes, A., Li, A. and Kulesa P.M. A Chemotactic Model of Trunk Neural Crest Cell Migration Genesis 56:e23239 (2018)

Model simplification

Social complexity of immigration and diversity

When investigating complex inter-dependent systems one is often left trying to reconcile two modelling paradigms: whether to use detailed, highly complex models that have direct parametric links to reality; or to consider simpler models that may be more easily analysed, but have a looser, more descriptive link with the experimental system? We investigated methods to link these two frameworks, by taking a highly complicated computational model of voting patterns in a population, and deriving a series of smaller models which may then be analysed and compared with the original model. This work was carried out in collaboration with social scientists within the University of Manchester

  1. Lafuerza, L.F., Dyson, L, Edmonds, B. and McKane A.J. Simplification and analysis of a model of social interaction in voting. Eur. Phys. J. B 89:159 (2016)
  2. Lafuerza, L.F., Dyson, L, Edmonds, B. and McKane A.J. Staged Models for Interdisciplinary Research. PLoS ONE, 11:6 (2016)

      Public Engagement

      Our recent paper on swarming locusts has received some attention in the press, with interviews on Radio 4's Today Programme (50mins in) and BBC World's Newsday (15.55mins in) and an article in the conversation. I have previously given a talk for the general public on this and other research at the Institute for Mathematics and its Applications.