NPDN Epidemiology Subcommittee Carla Thomas Chair

The NPDN Epidemiology Committee The goal of the Epidemiology Committee is to design, develop and implement analyses using the NPDN diagnostic record data as well as other data from other sources, to detect outbreaks at the earliest stage possible, whether intentionally introduced or not. Outbreaks may be local, state, regional or national events.

Epidemiology Committee Governance The governance - is conducted by a committee of NPDN staff and members who contribute to operational efforts in epidemiology.

NPDN Epidemiology Committee Members Carla Thomas, Chair, Plant Pathology and Epidemiology, UCD Andrew Coggshall, IT, Diagnostic Database Management, GIS, UCD Paul Jepson, Entomology and Epidemiology, IPPC, OSU Len Coop, Entomology, Weather-based Models, GIS, IPPC, OSU Hans Luh, Relational Database Design and Analysis, Text Mining, GIS, IPPC, OSU David Barber, IT, Diagnostic Database Management, U of GA Will Baldwin, IT, Diagnostic Database Management, KSU Howard Beck, IT and Diagnostic Database Management, Relational Database Taxonomy and Design, Distance Learning, U of FL Forrest Nutter, Plant Pathology and Epidemiology, ISU Casey Estep, IT, Diagnostic Database Management, Pathway Analysis, CDFA Mike Hill, IT, Diagnostic Database Management, Analyst, CERIS, Purdue Shen Wang, IT and GIS, CERIS, Purdue Eileen Luke, IT, Database Management, Project Management, CERIS, Purdue

Epidemiology Workshop 2003 Carla Thomas - UCD Richard Bostock – UCD Frank Ferrandino - Connecticut Experiment Station Jim Marois - University of Florida Borys Tkacz - USFS Eileen Luke – CERIS Jim Pheasant – CERIS Kitty Cardwell – USDA- CSREES Annette Sobel - Sandia Labs Stella Coakley - Oregon State University Paul Jepson - Oregon State University Larry Madden - Ohio State University Bob Zeigler - Kansas State University Roger Magarey – APHIS Coanne O’Hearn – APHIS Nandun Padokum - Silico Insights

Definition of Anomaly Types Host Characters – appears on new host or new part of host Pest Characters – change in pesticide resistance, virulence, etc. Geographic – appears in new place Temporal – appears at an unusual time of season Climatic –appears during unusual weather conditions Distribution – spreads in a new way, or at new rates Association – shows association with another factor that is new or unusual

Survey and analysis of legacy datasets, 2003 Sample submission forms from 43 states were collected –Most of these forms were in paper format Fields included symptomology, crop history, client information, date and location of sample collection, and host condition. Three databases were electronic and had a sufficient number of records: –CDFA 46,264 records covering 7 years –Kansas State University 19,439 records covering 9 years –University of Georgia 2,807 records covering 5 years Most of the records in paragraph format text entries Therefore a text mining approach was adopted.

20 Key Words Indicated Potential for Standardization Plant Leaves Seed Fruit Tip Stem Bark Root Large Spot Mottled Yellow Brown Discoloration Burn Margin Malform Canker Wilting Rot Scattered

Phase 1 required fields Lab Name - automated reporting Sample ID reporting Diagnosis ID association anomalies State / County geographic, first occurrence Host Genus Host anomalies Diagnosis Identification Genus Causal agent anomalies Confidence Level reporting Received Lab Date temporal anomalies

Phase 1 optional fields Host Common Name host anomalies Host Species host anomalies Host Sub-Species host anomalies Diagnostic Method reporting Identification Common Name causal agent anomalies Diagnosis Identification Species causal agent anomalies Diagnosis Identification Sub-Species causal agent anomalies Latitude / Longitude geographic anomalies Zip Code geographic anomalies Sample Notes general Diagnostic Notes general Date Collected temporal anomalies

Phase 2 Proposed Optional Fields Date First Observed temporal anomalies Plant Symptoms / Signs Observed host or causal agent anomalies Plant Parts Affected host or causal agent anomalies Entomology / Nematode Fields causal agent anomalies Host Plant Situation association anomalies, reporting Purpose of Submission reporting Program Supported reporting Submitter Type reporting Damage Distribution of Affected Plants geographic anomalies, host or causal agent anomalies Incidence host or causal agent anomalies Soil / Water Condition association anomalies, host or causal agent anomalies

Definition of Anomaly Types Host Characters – appears on new host or new part of host Host genus, species, subspecies, plant part affected, symptomology Pest Characters – change in pesticide resistance, virulence, etc. Pest/ Pathogen genus, species, subspecies, plant part affected, symptomology Geographic – appears in new place state, county, zipcode, lat./long. transportation routes Temporal – appears at an unusual time of season Date collected, date submitted, date of onset of symptoms, weather, risk models

Anomalies (cont.) Climatic –appears during unusual weather conditions Weather, climate and risk models Distribution – spreads in a new way, or at new rates Combination of geographic, temporal, and transportation fields Association – shows association with another factor that is new or unusual Host, pest, ecozone, soils, land use, crop history, satellite imagery

Benefits to Phase 2 Makes detection of unusual host or pest characteristics possible Makes automated programatic reporting possible Fills mission of NPDN in detecting and reporting unusual outbreaks earlier

Definition of Anomaly Types Geographic – appears in new place Temporal – appears at an unusual time of season Host Characters – appears on new host or new part of host Pest Characters – change in pesticide resistance, virulence, etc. Association – shows association with another factor that is new or unusual Climatic –appears during unusual weather conditions Distribution – spreads in a new way, or at new rates

Integrated GIS Platforms Distributed Integrated Interfaced National Database, Managed locally, distributed nationally

The value of a distributed multi- management system Specific expertise maintains specialized database Maintains data confidentiality for need to know groups through CERIS data security access Allows many different groups to interface. Distributes cost of maintenance of layers.

GVIS npdn.ceris.purdue.edu