skip to primary navigationskip to content

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

Biography:

Current positions

  • University Senior 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

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

Qualifications

  • PhD, Mathematical Biology, Department of Plant Sciences, University of Cambridge 
  • MSc, Modern Applications of Mathematics, University of Bath
  • MPhil, Computer Speech and Language Processing, Departments of Engineering and Computer Science, University of Cambridge
  • MA 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 illustrating this type of work -- focusing on small-scale control of a newly invading pathogen -- and that can be used to demonstrate the sometimes counter-intuitive epidemiological principles that underlie successful disease control is available at www.webidemics.com

Research Supervision

I have supervised four PhD students to successful completion, and am currently lead supervisor of three further students at the University of Cambridge, as well as co-supervisor of one student based at Centre for Ecology and Hydrology (CEH).

Teaching

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. I also contribute to an annual course on infectious disease dynamics for the Wellcome Trust.  In 2015 I was awarded the University's Pilkington Teaching Prize 

Other Professional Activities

  • British Society for Plant Pathology, Elected Board Member (2019-date)
  • Phytopathology, Senior Editor for papers in quantitative epidemiology (2018-date)
  • Tropical Plant Pathology, Section Editor for papers in epidemiology (2017-date)

Key Publications

For a full list of publications, please see Google Scholar

Bussell, E.H. and Cunniffe, N.J. (2020) Applying optimal control theory to a spatial simulation model of sudden oak death: ongoing surveillance protects tanoak whilst conserving biodiversity Journal of the Royal Society: Interface

Laranjeira, F.F., Silva, S.X.B., Murray-Watson, R.E., Soares, A.C.F., Santos-Filho, H.P. and Cunniffe, N.J. (2020) Spatiotemporal dynamics and modelling support the case for area-wide management of citrus greasy spot in a Brazilian smallholder farming region Plant Pathology

Hamelin, F.M., Allen, L.J.S., Bokil, V.A., Gross, L.J., Hilker, F.M., Jeger, M.J., Manore, C.A., Power, A.G., Rúa, M.A. and Cunniffe, N.J. (2019) Coinfections by noninteracting pathogens are not independent and require new tests of interaction PLOS Biology

Bussell, E.H., Dangerfield, C.E., Gilligan, C.A. and Cunniffe, N.J. (2019) Applying optimal control theory to complex epidemiological models to inform real-world disease management Philosophical Transactions of the Royal Society, B

Thompson, R.N., Gilligan, C.A. and Cunniffe, N.J. (2018) Control fast or control smart: when should invading pathogens be controlled? PLOS Computational Biology 

Elderfield, J.A.D., Lopez-Ruiz, F.J., van den Bosch, F. and Cunniffe, N.J. (2018) Using epidemiological principles to explain fungicide resistance management tactics: why do mixtures outperform alternations? Phytopathology

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

Hilker, F.M., Allen, L.J.S., Bokil, V.A., Briggs, C.J., Feng, Z., Garrett, K.A., Gross, L.J.Hamelin, F.M., Jeger, M.J., Manore, C.A., Power, A.G., Redinbaugh, M.G., Rúa, M.A. and Cunniffe, N.J. (2017) Modeling virus coinfection to inform management of maize lethal necrosis in Kenya Phytopathology 

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

 

Filed under:

The Department has carried out a comprehensive COVID-19 risk assessment process and has opened to allow research work to take place. To ensure the safety of our staff, a range of measures to reduce building occupancy and allow strict social distancing have been introduced, including increased cleaning and hygiene regimes. We are currently not accepting visitors so please continue to contact us by email until further notice.