Imperial College London

DrPierreNouvellet

Faculty of MedicineSchool of Public Health

Visiting Reader
 
 
 
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Contact

 

p.nouvellet

 
 
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Location

 

UG 11Norfolk PlaceSt Mary's Campus

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Summary

 

Summary

My current research focuses on developing statistical methods to estimate relevant parameters within complex ecological setting by integrating various sources of information in order to better understand the dynamics of diseases’ transmission and mitigate their public health risk.

The focus of my current work concentrates on 3 themes:

  • Vector-borne and zoonotic diseases, with a strong focus on rabies and Chagas disease.
  • Emerging diseases, with a strong focus on MERS-CoV, Ebola, Zika and antibiotic resistance.
  • Rapid response to outbreaks and real-time analysis, with a strong focus on developing tools and capacity ahead of future outbreaks.

In each of those 3 themes, my interests broadly sit at the interface between ecology and epidemiology where the potential for endemic and (re-)emerging zoonotic diseases lies.

Broadly speaking, I would define myself as a quantitative biologist and epidemiologist, as I seek to resolve concrete ecological and epidemiological problems using mathematical formalisation. Importantly, I see myself as belonging to the data-driven branch of modelling, and I strongly believe that models not only need to be fed with data, but also that in turn the analysis of data is greatly enhanced by modelling.

Publications

Journals

Ledien J, Cucunubá ZM, Parra-Henao G, et al., 2023, From serological surveys to disease burden: a modelling pipeline for Chagas disease., Philosophical Transactions of the Royal Society B: Biological Sciences, Vol:378, ISSN:0962-8436, Pages:1-12

Thrift E, Nouvellet P, Mathews F, 2023, Plastic Entanglement Poses a Potential Hazard to European Hedgehogs Erinaceus europaeus in Great Britain., Animals (basel), Vol:13, ISSN:2076-2615

Kim Y, Leopardi S, Scaravelli D, et al., 2023, Transmission dynamics of lyssavirus in Myotis myotis: mechanistic modelling study based on longitudinal seroprevalence data., Proc Biol Sci, Vol:290

Kim Y, Donnelly CA, Nouvellet P, 2023, Drivers of SARS-CoV-2 testing behaviour: a modelling study using nationwide testing data in England., Nat Commun, Vol:14

Wardle J, Bhatia S, Kraemer MUG, et al., 2023, Gaps in mobility data and implications for modelling epidemic spread: a scoping review and simulation study, Epidemics: the Journal of Infectious Disease Dynamics, Vol:42, ISSN:1755-4365, Pages:1-11

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