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, Cucunuba ZM, Parra-Henao G, et al., 2021, Spatiotemporal variations in exposure: Chagas disease in Colombia as a case study, Bmc Medical Research Methodology, ISSN:1471-2288

Lushasi K, Hayes S, Ferguson EA, et al., 2021, Reservoir dynamics of rabies in Southeast Tanzania and the roles of cross-species transmission and domestic dog vaccination, Journal of Applied Ecology, Vol:58, ISSN:0021-8901, Pages:2673-2685

Desai A, Nouvellet P, Bhatia S, et al., 2021, Data journalism and the COVID-19 pandemic: opportunities and challenges, The Lancet Digital Health, Vol:3, ISSN:2589-7500, Pages:e619-e621

Forna A, Dorigatti I, Nouvellet P, et al., 2021, Comparison of machine learning methods for estimating case fatality ratios: an Ebola outbreak simulation study, Plos One, Vol:16, ISSN:1932-6203

Bhatia S, Parag K, Wardle J, et al., 2021, Global predictions of short- to medium-term COVID-19 transmission trends : a retrospective assessment

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