ProfessorNim Arinaminpathy
Professor in Mathematical Epidemiology
School of Public Health - Faculty of Medicine
Orcid identifier0000-0001-9502-6876 (opens in a new tab)
- Professor in Mathematical EpidemiologySchool of Public Health - Faculty of Medicine
- Praed Street, St Mary's Campus, United Kingdom
BIO
I apply mathematical and statistical tools to study the transmission dynamics of infectious diseases, with equal interest in basic science and policy-focused analysis.
Research interests
A major focus of my team’s research is in the control of human tuberculosis (TB) in high-burden countries. We work closely with the national TB programme in India, and other high-burden countries. We also collaborate with the Stop TB Partnership, and the World Health Organization at both headquarter and Regional levels. In all of this work we are particularly interested in how interventions against TB should be prioritised, and how these priorities are shaped by local needs and conditions. Some highlights include:
Work with WHO Global TB Programme to estimate the global burden of TB following COVID-related disruptions
Collaboration with the Stop TB Partnership, providing modelling analysis in support of strategic planning towards the End TB goals
Collaboration with India's National Tuberculosis Elimination Programme, contributing mathematical models to help inform intervention planning to meet India's ambitious goals for TB elimination
Additionally, we maintain an active interest in viral respiratory infections. I work with the Indian Council of Medical Research (ICMR, India’s apex institute for biomedical research) to assist in modelling major infectious diseases, recently with a focus on COVID-19. We also collaborate with the US CDC and Princeton University, applying mathematical modelling to the surveillance and control of seasonal and pandemic influenza.
Biographical sketch
I trained in Applied Mathematics (BA Cambridge 2000, D.Phil Oxford 2005); between these programmes I spent a year as a scientist in a government research lab (Dstl, 2001).
Following my DPhil I trained in mathematical epidemiology as a postdoctoral researcher, first at the University of Oxford and then at Princeton University (USA) before returning to the UK.
Research interests
A major focus of my team’s research is in the control of human tuberculosis (TB) in high-burden countries. We work closely with the national TB programme in India, and other high-burden countries. We also collaborate with the Stop TB Partnership, and the World Health Organization at both headquarter and Regional levels. In all of this work we are particularly interested in how interventions against TB should be prioritised, and how these priorities are shaped by local needs and conditions. Some highlights include:
Work with WHO Global TB Programme to estimate the global burden of TB following COVID-related disruptions
Collaboration with the Stop TB Partnership, providing modelling analysis in support of strategic planning towards the End TB goals
Collaboration with India's National Tuberculosis Elimination Programme, contributing mathematical models to help inform intervention planning to meet India's ambitious goals for TB elimination
Additionally, we maintain an active interest in viral respiratory infections. I work with the Indian Council of Medical Research (ICMR, India’s apex institute for biomedical research) to assist in modelling major infectious diseases, recently with a focus on COVID-19. We also collaborate with the US CDC and Princeton University, applying mathematical modelling to the surveillance and control of seasonal and pandemic influenza.
Biographical sketch
I trained in Applied Mathematics (BA Cambridge 2000, D.Phil Oxford 2005); between these programmes I spent a year as a scientist in a government research lab (Dstl, 2001).
Following my DPhil I trained in mathematical epidemiology as a postdoctoral researcher, first at the University of Oxford and then at Princeton University (USA) before returning to the UK.
FACULTY
- Faculty of Medicine
POSITION NAME
- Professor in Mathematical Epidemiology