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Understanding the incidence and spread of Phytophthora ramorum using epidemiological modelling - SD0413

Description
This project will use epidemiological modelling to analyse and better understand the large-scale pattern and distribution of Phytophthora ramorum in the UK as a basis for determining future control strategies. Our underlying hypothesis is that the large scale spread of the disease, as reported by DEFRA Plant Health Division, is largely driven by the large scale movement of infected plants within the hardy nursery sector, and subsequently through escapes through retail sales or other mechanisms currently poorly understood, elsewhere. Emphasis will be placed on the dissemination of infection through the network structure of nurseries, garden centres, private gardens, and down watersheds, and prediction of the likely future development of the disease in the semi-natural environment ('wild sites'). Information on the local dispersal and development of disease within a nursery site or at known major outbreak sites elsewhere will be analysed using fine-scale epidemiological models to assess the risk of further spread from the nursery network.

The first step in the modelling will be to characterise the network structure of the hardy nursery trade in the UK. Each nursery location can be considered as a vertex in the network, connected to other vertices with which it trades. The number of nurseries connected to a given vertex is called the degree of the vertex, and the frequency distribution of nurseries with different degrees of connection in the entire network needs to be determined, because it can have a controlling influence in how a disease can spread through the network. If this distribution can be determined by sampling then complete details of the network structure are not required for some purposes; however the more complete our determination of the trade structure, the more specific a management tool we will be able to produce at the end of the project. We will then consider the time course of confirmed cases of P. ramorum on different species within the network and calculate the case reproductive number for the situation where eradication of diseased plants has been practised according to Plant Health Division regulations. It should then be possible to calculate the corresponding case reproductive number and predict the time course of cases for the situation without eradication. This provides a baseline against which alternative or complementary practices can be compared.

Existing data on confirmed P. ramorum cases do not represent all case. To estimate an upper limit on the true number of cases that have occurred the data need to be related to the sampling protocols used, key parameters relating to nursery practice such as propagation and standing time, and epidemiological information on the disease cycle on individual plants. Careful modelling of these relationships will help in revisiting and refining the sampling protocols used to date. Furthermore, one feature of current regulations imposed by Plant Health Division is that susceptible plants within a given radius of a confirmed case in a nursery are left in place without treatments being applied, to observe any subsequent disease symptoms. From these data it may be possible to estimate a nursery-specific basic reproductive number.

There are also data on the occurrence of P ramorum outside the nursery trade, in gardens, parks and the semi-natural environment. Measures of spatio-temporal association will be used to assess whether these outbreaks are related to nursery cases, either through supply of infected plants, through watersheds or as yet unknown means. Similarly, depending on data availability, the secondary and more local spread of disease in these outbreak areas will be analysed and a site-specific basic reproductive number (for diseased plants) will be calculated. The case reproductive number for the nurseries and the basic reproductive number for local epidemics in outbreak areas, and the nursery and watercourse network structures in the UK together provide a basis for predicting the time course of the P. ramorum epidemic in the UK and the likely success of control interventions. Finally for P ramorum, a series of scenarios will be explored, with different network characteristics (representing possible regulatory actions), inspection regimes and possible other pathogen life histories on different species that will be useful as a tool for Plant Health Division when the current project ends and as further epidemiological information becomes available. A particular application of the approach will be to determine the risk of extensive spread of P. kernovii if this pathogen enters the nursery distribution network.
Objective

1. Construct a dynamic network model representative of trade flows in the UK hardy nursery sector.

2. Analyse the DEFRA Plant Health Division data on confirmed cases of P. ramorum in the UK hardy nursery sector and understand the relationship between reported numbers and the likely true numbers of cases, in particular the likely maximum size and geographic extent of undetected outbreaks.

3. Determine whether there is an association between the spread of P. ramorum as recorded in the nursery trade, and the occurrence of the disease in ‘wild areas’. 

4. (a) Assess the likely long-term effectiveness of eradication as a control strategy for P. ramorum in the UK
(b) Develop the model framework so that alternative or complementary measures can be compared for
managing the disease both in the nursery sector and in potential outbreak areas.

5. Devise future strategies for management (including eradication) of P. kernovii in the UK.
Project Documents
• Final Report : Understanding the incidence and spread of Phytophthora   (1410k)
Time-Scale and Cost
From: 2005

To: 2007

Cost: £265,985
Contractor / Funded Organisations
Imperial College, Wye Campus
Keywords
Countryside              
Epidemiology              
Forestry              
Plant diseases              
Plant health              
Plants and Animals              
Sustainable Farming and Food              
Sustainable Production