Dr Louise Dyson
Senate House: Office: 348A
Phone: +44 (0)24 765 24975
SLS: Office: B138
Phone: +44 (0)24 765 74725
Teaching Responsibilities 2017/18:
Term 2: MA4E7 Population Dynamics: Ecology & Epidemiology
I am an Assistant Professor in Epidemiology appointed jointly between the Mathematics Institute and the School of Life Sciences. I am also a member of the Warwick Infectious Disease Epidemiology Research centre (WIDER), a cross-departmental group bringing together researchers from different disciplines to investigate the spread of infectious diseases. My research interests involve using techniques from mathematics and statistical physics to analyse biological and social systems. I am particularly interested in work with strong experimental links and in discovering the simplest possible explanatory mechanisms for observed data.
For publications arising from my research, see here
Targetting 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 targetting 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.
More than 1.5 billion people, or 24% of the world’s population, are infected with soil-transmitted helminth infections worldwide, with the majority of infections concentrated in sub-Saharan Africa, the Americas, China and East Asia. Coinfections with multiple types of organisms is common, but the impact that one infection might have on another is not widely understood. I am formulating models of helminth coinfections in order to better understand the interactions between different parasites within a single host, and the effects of these interactions at the population level.
Postdoc at the University of Manchester
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
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.
PhD Work at the University of Oxford
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.
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.
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.