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.
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.
20. Pellis L, Cauchemez S, Ferguson NM, Fraser C, “Epidemic in socially structured populations: when are simple models too simple?” (in preparation).
19. Keeling MJ, Pellis L, House TA, Cooper AJ “Approximations to SIS-infection dynamics on simple networks” (in preparation)
18. Kinyanjui TM, Pellis L, House TA, “Information Content of Household-Stratified Epidemics” (submitted to Epidemics).
17. Lythgoe K, Blanquart F, Pellis L, Fraser C, “The shifting-mosaic of HIV within-host ecological dynamics” (submitted to PLoS Biology).
16. Ball FG, Pellis L, Trapman P, “Reproduction numbers for epidemic models with households and other social structures II: comparisons and implications for vaccination” (available on arXiv:1410.4469 and submitted to Mathematical Biosciences).
15. Pellis L, House T, Keeling MJ (2015). Exact and approximate moment closures for non-Markovian network epidemics, Journal of Theoretical Biology 382: 160-177. (ArXiv version)
14. Pellis L, Spencer S, House T (2015). Real-time growth rate for stochastic SIR static network models in the absence of clustering, Mathematical Biosciences 265: 65-81. (ArXiv version)
13. Ball FG, Britton T, House T, Isham V, Mollison D, Pellis L, Scalia-Tomba G (2015). Seven challenges for metapopulation models of epidemics, Epidemics 10: 63-67.
12. Pellis L, Ball FG, Bansal S, Eames K, House T, Isham V, Trapman P (2015). Eight challenges for network epidemic models, Epidemics 10: 58-62.
11. Roberts M, Andreasen V, Lloyd A, Pellis L (2015). Nine challenges for deterministic epidemic models, Epidemics 10: 49-53.
10. Gog JR, Pellis L, Wood JLN, McLean AR, Arinaminpathy N, Lloyd-Smith JO (2015). Seven challenges in modelling pathogen dynamics within-host and across scales, Epidemics 10: 45-48.
9. Heesterbeek H et al (2015). Modelling infectious disease dynamics in the complex landscape of global health, Science.
8. Lythgoe K*, Pellis L*, Fraser C (2013). Is HIV short-sighted? Insights from a multi-strain nested model, Evolution 67(10): 2769–2782. *Equal contribution
7. 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.
6. Pellis L, Ball FG, Trapman P (2012). Reproduction numbers for epidemic models with households and other social structures I: definition and calculation of R0, Mathematical Biosciences 235: 85–97.
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. 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.
3. 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.
-- Pellis L (2009). Mathematical models for emerging infections in socially structured populations: the presence of households and workplaces, Doctoral dissertation, Imperial College London.
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.