Early warning and mitigation planning: Epidemiological models add value to surveillance

D.P. Hodson1 & C.A. Gilligan2 1CIMMYT-Ethiopia

2Department of Plant Sciences, University of Cambridge, UK

Overview: Partnerships adding value

1.  Surveillance Component � Where are we now? � Starting to add value to surveillance � Foundation for epidemiological models

2.  Epidemiological Modelling Component � How can epidemiological models help? ®  Predicting pathogen arrival and spread ®  ‘What if’ scenarios for management ®  Sampling strategies

� Data/information needs

Global Wheat “Footprint” Rust Survey “Footprint” 2006 Rust Survey “Footprint” 2012

•  13,000+ survey records •  30+ countries •  large % of developing world wheat

Information from Surveys: Stem Rust Hotspots

Ug99 races, Hotspots & Wheat

•  Ug99 races detected in many hotspots (but not all)

•  Current stem rust hotspots occupy a tiny fraction of wheat area

•  What is the risk or hazard in those other wheat areas???

Information from Surveys: Yellow Rust Hotspots

•  Different distribution •  More widespread than stem rust

2009

2010

2011

2012 •  Ethiopia: Yellow rust hotspots very dynamic! •  Why??

Ethiopia: Less food for rusts? 2010

Yellow rust severity - surveys Susceptible vs resistant cultivars - surveys

Ethiopia: Less food for rusts? 2012

Yellow rust severity - surveys Susceptible vs resistant cultivars - surveys

Ethiopia: Estimated Wheat Area Susceptibility to Ug99 races

2005/06 2013/14

BGRI Cornell Screening Dbase CIMMYT Wheat Atlas

S

MR/MS

?

MR/MS

MR MS

S

?

Early warning – Ethiopia 2013

Action Steps: •  Informal rust planning meeting: 12th June 2013 (CIMMYT, EIAR, FAO) •  Comprehensive Belg season surveys (EIAR/CIMMYT) •  Formal rust planning meeting , 6th August 2013 (CIMMYT, EIAR, MoA Extension Directorate, ATA, FAO, Animal & Plant Health Directorate) •  MoA, Extension Directorate + EIAR: Early, main season surveys

Global Rust Monitoring System Assessment

CWANA – Yellow Rust Outbreaks (surveys)

Climatic Conditions – favourable for yellow rust? Regional Winds

Rust Caution – May 17th

Moving Forward: Value Addition from Epidemiological Models ● Good inputs = Good outputs � Surveillance platform providing critical foundation

layers: Host distribution, pathogen sources + environments, susceptibility distribution

● Despite an extensive surveillance network, many gaps remain e.g., where are the risks and hazards? Models have a key role here.

● Early warning. Some progress (e.g., Ethiopia 2013), but with model inputs can make substantial gains

Epidemiological toolbox ● Landscape-scale models for disease spread ● Stochastic models: allow for uncertainty and

variability ● Coupling meteorological with epidemiological

models to predict: �  Risk – where might the pathogen arrive? �  Hazard – likely rates of spread if pathogen arrives? �  Control – ‘what if’ scenarios

Landscape scale models ● Chalara fraxinea � Ash dieback

● Detected in UK in 2012

Landscape scale models ● Chalara fraxinea � Ash dieback

● Meteorological model � risk of spore arrival

Landscape scale models ● Chalara fraxinea � Ash dieback

● Meteorological model � risk of spore arrival

● Consider all potential sources 2008-2011

Landscape scale models ● Chalara fraxinea � Ash dieback

● Meteorological model � risk of spore arrival

● Consider all potential sources 2008-2011

●  Data supplied by UK Met Office ●  Computational analysis based on NAME: also tested HYSPLIT

Landscape scale models ● Chalara fraxinea � Ash dieback

● Meteorological model � risk of spore arrival

● Identify principal sources that pose risk

2008

Landscape scale models ● Annual

variation

2009

Landscape scale models ● Annual

variation

2010

Landscape scale models ● Annual

variation

2011

Landscape scale models ● Annual

variation

2008 - 2011

Landscape scale models ● Cumulative

risk

2008 - 2011

Landscape scale models ● Model

predictions independent of disease observations

● Very strong agreement

● Good predictor of arrival

UK Spread Model: Infected Area

28

2013

● Epidemiological model � Transmission � Spread

®  Wind dispersal ®  Trade dispersal

● Host distribution � Density, connectedness

● Environmental conditions � Infection and sporulation

S I D R Susceptible Infected Detected Removed

UK Spread Model: Infected Area

29

2014

UK Spread Model: Infected Area

30

2015

UK Spread Model: Infected Area

31

2016

UK Spread Model: Infected Area

32

2017

UK Spread Model: Infected Area

33

2018

UK Spread Model: Infected Area

34

2019

UK Spread Model: Infected Area

35

2020

UK Spread Model: Infected Area

36

2021

UK Spread Model: Infected Area

37

2022 ●  Risk maps Where is invasion

most likely?

●  Hazard maps Where is impact of

spread most severe? ●  Inform control

and sampling

Wheat stem rust: 1) Long distance spore dispersal

Meteorological dispersal model

Integrate multiple

sources of inoculum

Very low probability of long distance

dispersal

Generating risk and hazard maps

Wheat stem rust: 2) Density and connectivity of host

Generating risk and hazard maps

Wheat stem rust: 3) Environmental suitability

Coincidence: Temp X Leaf wetness X Light Infection Sporulation

Generating risk and hazard maps

UK Met Office data @3-6h intervals

Wheat stem rust: Generating risk and hazard maps

●  Hazard maps Where is impact of

spread most severe?

●  Risk maps Where is invasion

most likely?

Wheat stem rust: Input from BGRI community

● Environmental suitability � Infection � Sporulation

● Host �  where when and how much? � Alternative hosts

● Pathogen dispersal � Data on dispersal � Snapshots of disease maps

Generating risk and hazard maps

Acknowledgements Dr Matt Castle Rich Stutt James Cox

Dr Nik Cunniffe Dr Stephen

Parnell Dr Alex Archibald

43

● Sampling method varies depending on question � First detection in new area � How much disease is present at time of first detection � Optimizing new detections after pathogen is introduced

Optimising Sampling

● Use of epidemiological models for sampling � Citrus greening in Florida � Chalara fraxinea in UK � Phytophthora ramorum in UK

Optimising Sampling Chalara fraxinea again

disease hazard map (potential outbreak size)

x distance to known outbreaks (probability of an outbreak)

= risk weighting

locations to sample

BBSRC

UK Research Councils

UK Government

& Industry

International sponsors

Acknowledgements