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Dr Nik Cunniffe

Head of Group
Dr Nik Cunniffe

Nik Cunniffe is accepting applications for PhD students.

Nik Cunniffe is available for consultancy.

Department of Plant Sciences
University of Cambridge
Downing Street

Cambridge CB2 3EA
Office Phone: 01223 333954


Current positions

  • University Lecturer in Mathematical Biology, Department of Plant Sciences, University of Cambridge
  • Official Fellow and College Lecturer in Natural Sciences (part-time), Girton College, University of Cambridge

Previous positions

  • Teaching Associate, Department of Plant Sciences, University of Cambridge
  • Associate Lecturer in Mathematics (part-time), The Open University
  • Principal Technical Consultant and European Research Manager, Autonomy Corporation
  • Analyst Programmer, Alphametrics Ltd.


  • PhD, Mathematical Biology, Department of Plant Sciences, University of Cambridge 
  • MSc, Modern Applications of Mathematics, University of Bath
  • MPhil, Computer Speech and Language Processing, Department of Engineering, University of Cambridge
  • BA Mathematics, St Catharine's College, University of Cambridge

Research Interests

Mathematical modelling of the spread, detection, evolution and control of plant and tree diseases. Theoretical work focuses on modelling disease spread, including stochastic and spatial models. Applied work concentrates on fitting simulation models to pathogen spread data, to understand how detection and control can be optimised, and on developing computational techniques for efficient simulation and parameterisation of spatially-explicit stochastic models at very large spatial scales. An online interface focusing on control of citrus canker and that is intended to illustrate the sometimes counter-intuitive epidemiological principles that underlie successful disease control is available at


Deliver lectures and practical classes for courses in all four years of the Natural Sciences course. Topics taught include: mathematics, mathematical modelling, statistics, plant disease epidemiology, theoretical ecology and computer programming in R and MATLAB. I also organise and deliver post-graduate training in Enabling New Ways of Working for the BBSRC DTP programme, and contribute to an annual course on infectious disease dynamics for the Wellcome Trust.  In 2015 I was awarded the University's Pilkington Teaching Prize

Key Publications

Hyatt-Twynam, S.R., Parnell, S., Stutt, R.O.J.H., Gottwald, T.R., Gilligan, C.A. and Cunniffe, N.J. (2017) Risk-based management of invading plant disease. New Phytologist

Cunniffe, N.J., Cobb, R.C., Meentemeyer, R.K., Rizzo, D.R. and Gilligan, C.A. (2016) Modeling when, where and how to manage a forest epidemic, motivated by sudden oak death in California Proceedings of the National Academy of Sciences 

Thompson, R.N., Gilligan, C.A. and Cunniffe, N.J. (2016) Detecting presymptomatic infection is necessary to forecast major epidemics in the earliest stages of infectious disease outbreaks PLoS Computational Biology

Thompson, R.N., Cobb, R.C., Gilligan, C.A. and Cunniffe, N.J. (2016) Management of invading pathogens should be informed by epidemiology rather than administrative boundaries Ecological Modelling

Groen, S.C.*, Jiang, S.*, Murphy, A.M.*, Cunniffe, N.J.*, Westwood, J.H.*, Davey, M.P., Bruce, T.J.A., Caulfield, J.C., Furzer, O.J., Reed, A., Robinson, S.I., Miller, E., Davis, C.N., Pickett, J.A., Whitney, H.M., Glover, B.J. and Carr, J.P. (2016) Virus infection of plants alters pollinator preference: a payback mechanism for susceptible hosts? PLoS Pathogens  (* equal contribution).

Cunniffe, N.J., Stutt, R.O.J.H., DeSimone, R.E., Gottwald, T.R. and Gilligan, C.A. (2015) Optimising and communicating options for the control of invasive plant disease when there is epidemiological uncertainty PLoS Computational Biology

Cunniffe N.J., Koskella, B., Metcalf, C.J.E., Parnell, S., Gottwald, T.R. and Gilligan, C.A. (2015) Thirteen challenges in modelling plant diseases Epidemics

Cunniffe, N.J., Laranjeira, F.F., Neri, F.M., DeSimone, R.E. and Gilligan, C.A. (2014) Cost-effective control of plant disease when epidemiological knowledge is incomplete: modelling Bahia bark scaling of citrus  PLoS Computational Biology

For a full list of publications, please see Google Scholar

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