Dr Lorenzo Pellis
Dr Lorenzo Pellis
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.
My main focus is using structured models to gain insight on the social determinants of human respiratory syncytial virus (RSV) transmission in Kenya, as part of a project coordinated by Prof James Nokes, as well as improving methods for approximating epidemic dynamics on networks. Previous research has focused on understanding the evolution of HIV virulence (Lythgoe et al (2013)), 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.
8. Lythgoe K, Pellis L, Fraser C (2013). “Is HIV short-sighted? Insights from a multi-strain nested model”, Evolution, DOI: 10.1111/evo.12166.
7. Pellis L, Ball FG, Trapman P (2012). “Reproduction numbers for epidemic models with households and other social structures I: definition and calculation of ”, Mathematical Biosciences 235: 85–97.
6. Pautasso M, Döring TF, Garbelotto M, Pellis L, Jeger MJ (2012). “Impact of climate change on plant diseases – opinions and trends”, European Journal of Plant Pathology 133(1): 295-313.
5. Shirreff G, Pellis L, Laeyendecker O, Fraser C (2011). “Transmission selects for HIV-1 strains of intermediate virulence”, Plos Comput Biol 7(10): e1002185.
4. Pautasso M, Xu XM, Jeger MJ, Harwood TD, Moslonka-Lefebvre M, Pellis L (2010). “Disease spread in small-size directed trade networks: the role of hierarchical categories”, Journal of Applied Ecology 47(6): 1300-1309.
3. Pellis L, Ferguson NM and Fraser C (2010). “Epidemic growth rate and household reproduction number in communities of households, schools and workplaces”, Journal of Mathematical Biology 63(4): 691-734.
2. Pellis L, Ferguson NM and Fraser C (2009). “Threshold parameters for a model of epidemic spread among households and workplaces”, Journal of the Royal Society Interface 6: 979-987.
1. Pellis L, Ferguson NM and Fraser C (2008). “The relationship between real-time and discrete-generation models of epidemic spread”, Mathematical Biosciences 216(1): 63-70.