Modelling the spread, control and detection of Ramorum Disease

Cambridge Richard StuttUniversity Nik Cunniffe

Erik DeSimoneMatt CastleChris Gilligan

Rothamsted Stephen ParnellResearchFrank van den Bosch

May 2012

Introduction – Prediction 2010-20

Model must integrate◦ Location of hosts◦ Environmental drivers◦ Pathogen dispersal

Compartmental model

Model – Spatial Stochastic Model

250m x 250m resolution

Combine data on Larch Rhododendron Vaccinium NIWT (other tree hosts)

Weight host types by sporulation/susceptibility

Model – Host Landscape

Pathogen responds totemperature/moisture

Model underlying suitability for each location

Statistical climate model then used to predict future fluctuations about this

Model – Environment

Dispersal kernel describes pathogen spread

Implicitly incorporates many mechanisms

Model – Dispersal

Positive Negative

Spread in the absence of control

Effect of extent of control◦ Felling infected stands◦ Felling infected stands + proactive control

Surveying for P. Ramorum on heathland

Results – Typical Applications

Results – Prediction 2010-20

Results – Need for Control

Results – Effect of Control Radius

Results – Sampling on HeathlandHazard map + known infections = sampling pattern

Continuous model improvement (data driven)

Region specific control

Effect of non compliance

Transition strategies

User friendly models

Current and Future Work

Forestry Commission◦ Bruce Rothnie◦ Joan Webber

FERA◦ Keith Walters◦ Phil Jennings◦ Judith Turner◦ Kate Somerwill

Funding from DEFRA, BBSRC and USDA

Acknowledgements

Extra material for questions

Susceptible hosts in the landscape are divided into a metapopulation at a chosen resolution (250m)

UK Sudden Oak death landscape assembled from:◦ National Inventory of Woodland Trees (NIWT)◦ Forestry Commission commercial Larch data◦ Maximum Entropy suitability models for Rhododendron and

Vaccinium (FERA/JNCC)

Different hosts have different weightings for sporulation and susceptibility

Model - Host

Broadleaved

Young Trees Felled

Coniferous

Construction of Host Landscape

Identify favourable conditions for P. ramorum◦ moisture ◦ temperature

Parameterise using experimental results

Model - Environment

Rela

tive S

poru

lati

on

Temperature

Fit model using historic spread data

Used Maximum Likelihood to assess goodness of fit

Predicted probability of infection by 2010 given starting conditions in 2004

Model - Validation

Survey Positive for P. ramorum

Survey Negative for P. ramorum

Probability of Infection

Risk – Reactive Control

Risk – Proactive Control (250m)

Risk Update – 20 year horizon

Disease Progress – No Control

Disease Progress – Stand Control

Total Infection

Symptomatic

Symptomatic at time of Survey

Disease Progress – 100m Radius

Total Infection

Symptomatic

Symptomatic at time of Survey

Disease Progress – 250m Radius

Total Infection

Symptomatic

Symptomatic at time of Survey

Disease Progress – 500m Radius

Total Infection

Symptomatic

Symptomatic at time of Survey

Disease Progress – Comparisons

Effects of Delay Before Culling

Examine region of South Wales

Cull: no delay after survey 6 month delay

Effect of Delay Before Culling

Key Questions When Surveying for Disease:◦ Where is the disease likely to be?◦ Where is it likely to be most severe and spread

most rapidly?◦ How to optimise the sampling?

Sampling Strategies

Sampling Strategies Uses:

• Currently known outbreaks • Predicted severity of

outbreaks• => Sampling weighting

Survey pattern formed• => sampling from

weightings Map shows a weighting and

a set of survey points (green)