Enabling Comprehensive

Integrated Animal Health

Surveillance

Brian J. McCluskey, DVM, MS, PhD, Dip. ACVPM

Executive Director

Science, Technology and Analysis Services

Veterinary Services

CIS: The concept

Comprehensive

=

multi-disease

Integrated

=

Multiple surveillance

streams

Surveillance stream-centric

Efficient

=

Constantly evaluated

Scalable: emergency readiness

2

Approach

The 5 Rs we need to target

3

Representative

Reliable: Accurate information

Real-Time: Now

Resourceful: Efficient

Risk-Based

Syndromic

surveillance

Import-

export dataLivestock

marketsOn-farm

Laboratories

LMS SCS AHSM VSLSEMR

S2MIM

EPS

apps

Rapid

detection of

emerging or

foreign

disease

Outbreak

response

Substantiating

Disease Status

IndustryPrivate

practitionersState AHOs Academia

Trade

support

Historical

surveillance

DZ control/

eradication

Stake-

holder

demands

Planning/

budgeting

Objectives

Sources of surveillance information

Data systems

Partner interaction

Decision level

NSD

Comprehensive and Integrated Surveillance System structure

Certification

programs

Identifying

areas for more

intense

surveillance

Supporting

claims of

disease

freedom

Identifying

changes in

disease

prevalence or

distribution

Evaluation of

control

measures

Supporting

continuity of

business

Slaughter

Human

health

SS

PREM

ID

4

What do we have today?

5

Different levels of

development in each

commodity

6

Swine Health CISSwine Health CIS

Market swine slaughterSuspect

casesImport/

export dataHigh-risk on-farm

sampling

Diagnostic stream

LMS SCS AHSM VSLS EMRS2 NSD1 NSD2

IndustryPrivate

practitioners

State

AHOsAcademia

Trade

support

Historical

surveillance

DZ control/

eradication

Stake-

holder

demand

Planning

&

budgeting

Objectives

Sources of surveillance information

Data systems

Partner interaction

Decision level

NSD3

Integrated view, non integrated data sources

High-risk

slaughter swine

FSIS

condemn

monitoring

Supporting

claims of

disease freedom

Identifying changes in

disease prevalence or

distribution

Evaluation of

control

measures

Supporting

continuity of

business

Rapid detection of

emerging or foreign

disease

Outbreak

response

Identifying areas for

more intense

surveillance

Substantiating

Disease Status

7

What we have• Highly engaged stakeholders

• Laboratory network capable of testing for FADs and endemic diseases—scalable system

• Surveillance streams already identified and operable

• Variety of data sources and levels of standardization

• Limited geographic and population strata representation and risk characterization

Fast moving, high demands

CSF, ASF, FMD, PRV, SB, SECD

Over a dozen different sources

PIN /AID + methods to rapidly select samples. Undersampling or oversampling? Inefficiencies?

D-labs, high risk slaughter (sow boars, market swine, roaster), feral

swine, high risk farms (garbage feeders)

8

WS feral swine

spread-sheets

NVSL STRAND for

high-risk

WS feral swine

spreadsheets

Cull sow-boar data

(MS Access)

VSLS-UDB for D-labs

NAHLN spread sheets

Meat juice data (spreadsheets)

PRV

SB

ASF/FMD

NVSL STRAND

NAHLNLMS

NAHLN spreadsheets

IAV-S

SECD

NAHLNLMS

NAHLN spread-sheets

Swine surveillance data sources trivia

NAHLN LMS Cull sow-boar data

(MS Access)

CSF

12 sources of information, of which 6 are not standardized (NS)

9

What is missing

• Data standardization

• Data connectivity

• Once PINs widespread used

• Better use of our CA’s with states

• Validated diagnostics

• Capacity!

Limitations to growth driven by data management deficiencies, diagnostic limitations and capacity

MESSAGING !

System with convergence of epidemiology and

testing data

A system to rapidly select samples to represent

strata

Use of state data ?

Rapidly validate new testing protocols

Demand for analytical and laboratory services

exceeds VS capacity

10

11

Cattle CIS

Cattle Health CIS

Cull slaughterState

testing

Trace-out

testing

On-farm

sampling

Diagnostic labs

SCSVSLS AHSM

Outbreak

response

Disease

modeling

Industry Private practitioners State AHOs

Objectives

Sources of surveillance information

Data systems

Partner interaction

Decision level

Non integrated solid surveillance streams

Rapid detection

of emerging or

foreign disease

EMRS2

3D-4D Public

health labs

FSIS- inspected

plantsRenderers

NSDs

Planning/

budgeting

Stakeholder

demands

Disease

control &

eradication

Trade

support

Identifying areas

for more intense

surveillance

Supporting

claims of disease

freedom

Identifying changes in

disease prevalence or

distribution

Evaluation

of control

measures

Supporting

continuity of

business 12

What we have• Several strong, but separate,

disease surveillance programs

• Partnerships with States and resource agencies

• Laboratory diagnostics

• One functioning data management system for one disease

• Decision support in a number of key areas

We have some solid structures on which to build CIS for

cattle…now we need to integrate

BSE, TB, Brucellosis

Partner support,

communication,

valid diagnostics,

test development

VSLS/AHSM for BSE

Trade, consumer

confidence, disease control

13

What’s missing?

• Shift to comprehensive mind frame

• Resource limitations affect data entry into existing systems

• Gaps in access to data contained in existing systems

• Lack of population representativeness (geographic and strata specific)

• Under-utilization of contextual data to support testing data

Other diseases of interest?

Overlapping surv. streams?

Existing data unavailable or

difficult to access

PIN and traceability

Limited epi information

Integration of different industry sectors, surveillance sources of

information, and data. 14

DATA MANAGEMENT STRATEGIES:

HAVING YOUR CAKE AND EATING IT

TOO

Comprehensive Integrated Surveillance Emergency Response

NLRAD/EDFNotifiableMonitoredEmerging

EPSIntern’tl

Monitoring/Scanning

Emerging Disease

FADDomestic Disease

Business Process/PolicyData collection

Data accessData analysis

ReportingResources

IT ArchitectureSCSLMSVSLSMIM

AgConnectResources

EMRS2/CRM

Business Process/PolicyData collection

Data accessData analysis

ReportingSituational awareness

Resources

IT ArchitectureLMS

EMRS2/CRMMIM

AgConnectResources

Warehouse Warehouse

Integration Integration

18

Data collection

How are data requirements defined during

the surveillance planning process,

transformed into information technology

solutions, collected and recorded in the field,

and finally submitted to centralized

databases for various types of reporting and

analytical needs.

19

Data Integration

How do we effectively use unique identifiers

for lab submission, premises identification,

and animal identification that are integral for

merging surveillance and laboratory result

records.

20

Reporting

Animal health program reporting routinely occurs

using disparate intergovernmental and external

data sources requiring analysts to spend too much

time acquiring and preparing data in favor of time

spent on program or scientific analysis. Data

preparation procedures currently used need to be

closely examined to streamline data management

processes using electronic data processing and to

define data warehouse opportunities