- New Flubird Database New Flubird Database e e ub d ub d a - - PowerPoint PPT Presentation

• New Flubird Database

New Flubird Database – e ub d a abase e ub d a abase Platform for Data Exchange and Knowledge Platform for Data Exchange and Knowledge B ildi i A i I fl S ill B ildi i A i I fl S ill Building in Avian Influenza Surveillance Building in Avian Influenza Surveillance

Staubach C1 Mathey A1 Kowalczyk S1 Tubbs N2 Wilking H1 Richter S1 Kranz P1 Staubach C , Mathey A , Kowalczyk S , Tubbs N , Wilking H , Richter S , Kranz P , Hagemeijer W2, Harder T1, Conraths FJ1 and the NFB consortium3

1Friedrich-Loeffler-Institut, Federal Research Institute of Animal Health, Germany 2Wetlands International Headquarters, The Netherlands 3Coordinated by Osterhaus A, Erasmus University Medical Center, The Netherlands

Outline:

• Background
• Background
• New FluBird

(Network for Early Warning of Influenza Viruses in (Network for Early Warning of Influenza Viruses in Migratory Birds in Europe)

• Challenges
• Challenges
• Database

D i / T h i l t

• Design / Technical aspects
• „Walk through“ / Data flow

I t ti f I t ti l W t bi d C

• Integration of International Waterbird Census
• Outlook
• Upcoming developments

New FluBird - Objectives:

• Interdisciplinary approach
• Integration of different data sources

Integration of different data sources

• Evidence based surveillance

Better understanding of avian influenza ecology Basis for predictive modelling Basis for predictive modelling More effective risk assessment

Project participants:

Avian influenza (AI) in wild birds

Reservoir of AIV: Birds of the genus Anseriformes and Anseriformes and Charadriiformes (water associated habitat) Isolation of all 16 HA- and 9 NA ) HA and 9 NA subtypes Virus are usually Virus are usually low pathogenic

Anseriformes Charadriiformes

Genetic reassortment

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Transmission Cycle of AI

Biotic and

Animal contacts,

abiotic vectors LPAIV

faeces / water Humans,

Free ranging

Humans, vectors, animal contacts Mutation f H5/H7

Intensive farms

• f H5/H7

High economic Losses in the poultry sector HPAI – Avian Influenza

Reservoir: wild water birds: LPAI H1 - H16

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Year-round prevalence of LPAI

600 7,00 5,00 6,00 3,00 4,00 1,00 2,00 0,00 , 1 2 3 4 5 6 7 8 9 10 11 12 Month

Challenges:

• Compatibility to external initiatives for possibility of data

exchange (e.g. EC, Wetlands International)

• Ad

t d d t b t t d l d i t f

• Adapted database structure and upload interfaces
• Code bridges, e.g. bird species codes (i.e. WBDB, EURING)
• Diverse and large user community
• Diverse and large user community
• User manager: flexible, decentralized user account administration
• User-based, configurable data access rights

, g g

• Possibility of including “User groups”, e.g. External Advisory

Board, EC, EFSA?

• I t

ti it T I t ti

• Interactivity, Transparency, Integration
• User friendly software modules for data interaction
• Visualization via map server
• Visualization via map server

Database structure:

• Table 1a/b: Laboratory results

compatible with EC/CRL compatible with EC/CRL

• Table 2: Bird observation questionnaire

compatible with GAINS (e.g. census data)

• Table 3:

Bird watching site description Table 3: Bird watching site description

compatible with GAINS (e.g. CSN tool)

T bl 4 Bi d b ti i i

• Table 4: Bird observation missions

background data (addition to table 2)

Partner Partner

Species

EC, CRL Laboratories Scientific Institutes Ornithology Partner

User manager:

German surveillance database

Number of Records per year and status of birds Active sampling Passive sampling Number of Records per year and status of birds g g Bird status alive hunted

Pos. H5N1 dead

sick

Pos. H5N1

status alive hunted H5N1 dead sick

H5N1

2006 5.800 1.136 23.104 40 316 2007 15.819 1.782 1 7.892 101 331 2008 14.454 2.883 4.744 35 2009 6.430 648 1 2.389 53

Report of all investigations of wild birds regarding AI in charge of the Federal States to the EC by FLI in charge of the Federal States to the EC by FLI

Data upload dialog: lab data

„Upload“ Files:

XML-File ASCII text file

Data access and digestion:

• Mi i

f d t b i f l i

• Mining of data on basis of complex queries
• User friendly interface to handle queries; stored for re-use
• Any database field can be selected as search criteria
• Any database field can be selected as search criteria
• single filter conditions can be flexibly combined by ‘AND’ or

‘OR’ logical links g

• Linking of different data types based on shared criteria

(e.g. spatial, temporal, species related)

• Output of data / Visual integration

f

• Predefined reports, automatically generated
• Presentation of user defined queries in table view
• Visualization of query results in map server e g combined
• Visualization of query-results in map-server, e.g. combined

with selected flyway map layer, census data, etc.

IWC sites in WP & SW Asia 1990 - 2007:

IWC data selection by area

IWC – Tabular presentation of count aggregates

Cygnus cygnus Median > 100

• Corine Land Cover

Integration of CLC data

• Corine Land Cover
• 44 classes of land coverage
• 1:100,000 mapping scale, minimum mapping unit 25 hectares
• Background data for Map Server
• Aim: Visual integration by overlaying with e.g. lab result layers,

g y y g g y

• rnithological data layers, etc.
• Coverage profiles per geographical / administrative unit
• Appropriately grouped categories => coverage profiles
• Aim: enable filtering of sample events based on adjoin

environmental parameters environmental parameters

• Example query:
• Select all M-PCR positive samples from areas with X % surface

covered by wetlands agriculture etc covered by wetlands, agriculture, etc.

NFB-DB

CORINE Landcover data

Map Filter Data records on NUTS level

Surveillance schemes:

The target of the surveillance must be fixed! The target of the surveillance must be fixed! HPAI and LPAI surveillance must be different! Surveillance can then be optimized to achieve these p goals and reduce resources HPAI surveillance must focus on dead birds (public li i th b di ti awareness, sampling in the breeding areas, cooperation with ornithologists, mortality reporting etc. required) LPAI surveillance could focus on some geographical LPAI surveillance could focus on some geographical (“representative”) hotspots, species (e.g. high prevalence and H/N diversity), time periods, specimen, y) mallard sentinel stations (e.g. Globig et al., 2009, EID) Both sample schemes could also include a small ti f th th ith t l i ti i ti proportions of the other without loosing optimization potential

Outlook:

Database Database

• Expansion of the currently 128,000 records of investigated wild

birds in time and space p

• Online calculation of raw prevalence maps and maps where

the estimate is corrected for the sampling error

• Automated threshold warnings by the database (changes in
• Automated threshold warnings by the database (changes in

prevalences, appearance of new subtypes, etc.)

• Integration of dynamic data on wild bird movements

g y

Seasonal movements to and from Germany & Denmark for Mallard (Anas platyrhynchos)

Season mallards ringed and recorded in Denmark & Germany in December until February

Outlook:

Database Database

• Expansion of the currently 128,000 records of investigated wild

birds in time and space p

• Online calculation of raw prevalence maps and maps where

the estimate is corrected for the sampling error

• Automated threshold warnings by the database (changes in
• Automated threshold warnings by the database (changes in

prevalences, appearance of new subtypes, etc.)

• Integration of dynamic data on wild bird movements

g y

Analysis/Modelling

• C

bi d ti l i / d lli f ll d t t

• Combined space-time analysis/modelling of all data sources to

understand better the ecology of AI in wild birds

• Optimize surveillance in space and time incl. identification of

p p hotspots

Distribution of the sample size per time and space

Months

Number of samples

High : 245 Low : 0

Municipalities Municipalities

Analysis/Modelling

1. Each bird i has the unknown probability πi that it is positive or negative depending on area j the bird lives, on the time t and

y g

g p g j , species x, the covariates, α1…n 2. The parameter πi is modeled with a logistic model

x s

n t t j i i

+ + + + + + + =         − α α ϕ θ µ π π ... 1 log

1

μ = intercept θj = spatial effect in the area j

i 



θj spatial effect in the area j ϕt = time effect on the time t st = seasonal effect for the whole region α = variables e g regarding CLC high risk IWC α1..n = variables e.g. regarding CLC, high risk, IWC x = species

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