I am a mathematical modeller in infectious disease epidemiology working with Prof Matt Keeling and Dr Thomas House in the Mathematics Institute at the University of Warwick, as part of the WIDER (Warwick Infectious Disease Epidemiology Research) group.
I am also an Honorary Research Associate in the Medical Research Council (MRC) Centre for Outbreak Analysis and Modelling, within the Department of Infectious Disease Epidemiology, Imperial College London and where I have active collaborations in the Evolutionary Epidemiology Research Group led by Prof Christophe Fraser.
My research focuses on the development of novel deterministic and stochastic techniques to follow, approximate and summarise the dynamics of infection spread. I mostly focus on directly transmissible human infections, and on the heterogeneity imposed on the spread by the complexity of the human social structure.
I am interested in any modelling approach that can lead to better insight and practically useful applications, including branching processes, network models, moment-closure techniques, MCMC methods for parameter estimation and individual-based stochastic simulations. I am trying to bridge the gap between “unrealistic but tractable” and “complex and intractable” approaches.
Stemming from previous work on understanding the evolution of HIV virulence (Lythgoe et al, 2013), I am currently developing methods to study multi-strain and multi-pathogen systems in the presence of complex within-host dynamics and superinfection. Other work in progress focuses on human respiratory syncytial virus (RSV) transmission in Kenya, as part of a project coordinated by Prof James Nokes, as well as on improving methods for approximating epidemic dynamics on networks. Previous research has focused on determining the importance of school closure in mitigating influenza pandemics and quantifying the relative contribution of household and age stratification on epidemic spread. I have a strong interest in the problem of models comparison, with the purpose of investigating when simple models, in addition to being key tools to gain understanding of the determinants of system dynamics, can inform health care decision-making processes, and when instead they are over-simplistic, fail to capture some essential system features and lead to inaccurate predictions.
Pellis L, Spencer SEF, House T, 2015, Real-time growth rate for general stochastic SIR epidemics on unclustered networks, Mathematical Biosciences, Vol:265, ISSN:0025-5564, Pages:65-81
et al., 2015, Modeling infectious disease dynamics in the complex landscape of global health, Science, Vol:347, ISSN:0036-8075, Pages:1216-U29
et al., 2015, Seven challenges for metapopulation models of epidemics, including households models, Epidemics, Vol:10, ISSN:1755-4365, Pages:63-67
et al., 2015, Nine challenges for deterministic epidemic models, Epidemics, Vol:10, ISSN:1755-4365, Pages:49-53
et al., 2015, Eight challenges for network epidemic models, Epidemics, Vol:10, ISSN:1755-4365, Pages:58-62