| This project going to be… | Experimental (lab/field based) |
| Email | njc1001@cam.ac.uk |
| Position held | Professor of Plant Disease Modelling |
| Title of Project | Including time-dependent infectivity and detectability in epidemiological models for plant disease |
| Institution Department and Address | Department of Plant Sciences |
| University of CAMBRIDGE, CB2 3EA |
| United Kingdom |
| Full Name of Supervisor | Nik Cunniffe |
| Date of Project Commencement | 10/07/2023 |
| Duration (weeks) | 10 |
| Brief Description of Project | Citrus production in the United States, Brazil and worldwide is threatened by a number of exotic pathogens, most notably citrus canker and citrus greening. Due to intensive research interest and – particularly – good epidemiological data, including detailed disease surveys at both local and regional scales, citrus is an excellent model system for food security threats to agricultural & horticultural crops more generally. A number of recent mathematical models of citrus diseases have targeted spread at scales relevant to individual producers, often tracking the disease status of individual plants within a planting (e.g., Craig et al 2018). These models can be used, for example, to define the optimal radius for removal of plants as a disease control (e.g., Cunniffe et al 2015), as well as more elaborate strategies based on epidemiological risk (Hyatt Twynam et al 2017). However, the models can be extended in a number of ways. Perhaps most notably, models tend to assume that individual plants are either infectious or not, with an instantaneous change that causes an individual plant to move from being not infectious to fully infectious. A similar response is used for expression of symptoms. For reactive disease control, based on controlling in the vicinity of detected infection, both assumptions are important. This project will test how this can be relaxed, using multi-compartmental models, in which multiple sequential infected compartments are used as a proxy for time since infection (Cunniffe et al 2012, Hart et al 2019). This allows more resolution in both the transmissibility and detectability of an individual host as its pathogen load increases. The student would learn i) mapping a complex biological system to a parsimonious mathematical model; ii) modern methods for simulating epidemiological models; iii) high performance computing, via use of the a HPC cluster; iv) experience of applying mathematics to a biological system. The project would perhaps best suit a student with some knowledge of computer programming. However, students without such a pre-existing background have enjoyed and been successful in my laboratory in the past. Any interested students are therefore strongly encouraged to get in touch. |
| Attach the recommended reading for the project | Craig et al. (2018) Grower and regulator conflict in management of the citrus disease Huanglongbing in Brazil: a modelling study. Journal of Applied Ecology. 55:1956-1965 |
| Cunniffe et al. (2012) Time-dependent infectivity and flexible latent and infectious periods in compartmental models of plant disease. Phytopathology. 102:365-380. |
| Cunniffe et al. (2015) Optimising and communicating options for the control of invasive plant disease when there is epidemiological uncertainty. PLOS Computational Biology. 11:e1004211 |
| Hart et al. (2019) Accurate forecasts of the effectiveness of interventions against Ebola may require models that account for variations in symptoms during infection. Epidemics. 29:100371 |
| Hyatt Twynam et al. (2017) Risk-based management of invading plant disease New Phytologist, 214: 1317-1329 |
