Richard Stutt Nik Cunniffe Erik DeSimone Matt Castle Chris Gilligan February 2012
Example results from landscape-scale models ◦ SOD in California (precursor to this model) ◦ SOD in UK How the model works ◦ Host landscape ◦ Environmental conditions ◦ Pathogen dispersal Uses of the model ◦ Predictions of spread ◦ Effects of control
Key components: ◦ Host ◦ Environment ◦ Pathogen dynamics and dispersal Expressed as a compartmental model
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
Broadleaved Young TreesFelled Coniferous
Identify favourable conditions for P. ramorum ◦ moisture ◦ temperature Parameterise using experimental results Relative Sporulation Temperature
Calculate underlying suitability of locations in the landscape Statistical used to model future conditions
Dispersal kernel is a statistical description of transport of inoculum between locations Implicitly incorporates many mechanisms
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 Survey Positive for P. ramorum Survey Negative for P. ramorum
Prediction in the absence of control Effect of controls ◦ Felling infected stands ◦ Felling infected stands + proactive control Effect of any delay in implementing control Application to surveying for P. Ramorum
Total Infection Symptomatic Symptomatic at time of Survey
Total Infection Symptomatic Symptomatic at time of Survey
Total Infection Symptomatic Symptomatic at time of Survey
Total Infection Symptomatic Symptomatic at time of Survey
Examine region of South Wales
Cull: no delay after survey 6 month delay
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?
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)
Continue to improve the model Refinement of country wide strategies: Region specific control Effect of non compliance User friendly models
Frank van den Bosch, Stephen Parnell ◦ Rothamsted Research Forestry Commission, FERA ◦ (in particular Bruce Rothnie and Keith Walters) Funding from DEFRA, BBSRC and USDA