Veterinary syndromic surveillance: Current initiatives and potential for development

Preventive Veterinary Medicine 101 (2011) 1 17

Contents lists available at ScienceDirect

Preventive Veterinary Medicine

j ourna l ho me pag e: ww w.elsev i er .com/ locate /prev etmed

Review

Veterin nt infor dev

Fernanda ieDepartment of d IslandCanada

a r t i c l e i n f o

Article history:Received 4 February 2011Received in revised form 5 May 2011Accepted 8 May 2011

Keywords:Syndromic surVeterinary surAnimal healthEmerging diseAberration detProspective m

a b s t r a c t

This paper reviews recent progress in the development of syndromic surveillance systemsfor veterinary medicine. Peer-reviewed and grey literature were searched in order to iden-tify surveillance systems that explicitly address outbreak detection based on systematic

1. Introdu

The evonary medicphase of suSchwabe (1by the use otools for diawitnessed a

CorresponE-mail add

(F.C. Drea).

0167-5877/$ doi:10.1016/j.veillanceveillance

surveillanceasesectiononitoring

monitoring of animal population data, in any phase of implementation. The review foundthat developments in veterinary syndromic surveillance are focused not only on animalhealth, but also on the use of animals as sentinels for public health, representing a furtherstep towards One Medicine. The main sources of information are clinical data from prac-titioners and laboratory data, but a number of other sources are being explored. Due tolimitations inherent in the way data on animal health is collected, the development of vet-erinary syndromic surveillance initially focused on animal health data collection strategies,analyzing historical data for their potential to support systematic monitoring, or solvingproblems of data classication and integration. Systems based on passive notication ordata transfers are now dealing with sustainability issues. Given the ongoing barriers inavailability of data, diagnostic laboratories appear to provide the most readily available datasources for syndromic surveillance in animal health. As the bottlenecks around data sourceavailability are overcome, the next challenge is consolidating data standards for data clas-sication, promoting the integration of different animal health surveillance systems, andalso the integration to public health surveillance. Moreover, the outputs of systems for sys-tematic monitoring of animal health data must be directly connected to real-time decisionsupport systems which are increasingly being used for disease management and control.

2011 Elsevier B.V. All rights reserved.

ction

lution of disease control methods in veteri-ine from campaigns and mass action to a newrveillance and selective action was dened by982) as an epidemiological revolution, markedf epidemiological intelligence and analysis keygnosis and decision making. The last decade has

further step in this revolution, with epidemio-

ding author. Tel.: +1 902 566 0969; fax: +1 902 620 5053.resses: [email protected], [email protected]

logical intelligence being progressively improved throughnovel informatics and data mining techniques; these allowanalysis to be carried out on an unprecedented quantity ofdata to identify novel and useful patterns in an automatedmanner (Chen et al., 2005).

In this new context, providing effective and comprehen-sive approaches for systematic information managementand analysis plays a central role in achieving the goals ofdisease surveillance (Zeng et al., 2005). While the conceptsbehind integrating information from multiple sources arenot novel (McKendrick et al., 1995), the past decade hasseen an increase in research that is focused on develop-ing, the science and technologies needed for collecting,sharing, reporting, analyzing, and visualizing infectious

see front matter 2011 Elsevier B.V. All rights reserved.prevetmed.2011.05.004ary syndromic surveillance: Curreelopment

C. Drea , Javier Sanchez, Crawford W. Rev Health Management, Atlantic Veterinary College, University of Prince Edwaritiatives and potential

, 550 University Avenue, Charlottetown, PE, C1A 4P3,

2 F.C. Drea et al. / Preventive Veterinary Medicine 101 (2011) 1 17

disease data and for providing data and decision-makingsupport for infectious disease, which Zeng et al. (2005)dened as infectious disease informatics. This is an interdis-ciplinary eld, taking advantage of a range of informationtechnologieinformationknowledge(Chen et al.

The uptwhen biote2001, and oas SARS (Brrecognize phuman paththe tools ptime surveof pre-diagcollected (Mcounter mechief comporders (Wa

Due to diagnosis dgeneral grofore often Centers forsyndromic use of hesignal withto warrantDisease Conthan conrsurveillancereal-time odata (Frickeare represe(and usualleters, as vathey are sepopulation,of a diseassyndromic of disease, the continubeing accumthe use ofimmediate investigatio

In veterfor early dto that takMedicine cthat the eations, whetimportance

While thture dealinga great incrapplied retrthere existssyndromic

reviews the current progress towards developing syn-dromic surveillance in veterinary medicine, dening assuch all those systems that explicitly address outbreakdetection based on systematic monitoring of population

Whilems thaation tial fo

of data sense

pulatillanc

primam is t

theire) of

lationfore, sinimizods bts by cr-repoiagnocity ol survef repor

the clses, a ses (Sa

Fig. 1with dnted fock an

at reillancrmatiomatic,s in thn covss conconrm

notedrs ang schets thewith the scherveilla

Timeli outbre, 2001)tectionh, poinutbreaf the osignedse(s) tcteriss such as data sharing and security, geographic systems (GIS), data mining and visualization,

management, biostatistics and bioinformatics, 2005; Zeng et al., 2005).ake of these approaches gained momentumrrorist events, such as the anthrax attacks ofutbreaks of emerging infectious diseases, suchavata et al., 2004) underlined the necessity toatterns indicative of a possible introduction ofogens, natural or not, as early as possible. Using

rovided by infectious disease informatics, realillance systems were developed to make usenosis data already available and automaticallyandl et al., 2004a), such as sales of over-the-

dicine, absences from work or school, patientslaint upon emergency visit, or laboratory testgner et al., 2001; Sosin and DeThomasis, 2004).the lack of specicity associated with pre-ata, this new type of surveillance targetsups of diseases, or syndromes, and is there-referred to as syndromic surveillance. The

Disease Control (CDC, USA) has dened assurveillance those approaches which make

alth-related data that precede diagnosis and sufcient probability of a case or an outbreak

further public health response (Centers fortrol and Prevention, 2006). While less specicmatory diagnosis, data used for syndromic

is more timely (Shmueli, 2010), allowing forr near-real-time analysis and interpretation ofr, 2006). The assumption is not that the datantative of the disease burden in the populationy no attempt is made to estimate such param-rious biases are recognized to exist), but thatnsitive to changes to the level of disease in the

containing an early, though weak, signaturee outbreak (Yahav and Shmueli, 2007). Whilesurveillance denitions focus on early detectionHenning (2004) highlights the fact that withous use of such systems longitudinal data areulated, allowing for a broader achievement;

existing health data in real time to provideanalysis and feedback to those charged withn and follow-up of potential outbreaks.inary medicine the development of systemsetection of diseases followed a similar pathen in public health. Recent focus on the Oneoncept has resulted in an increased awarenessrly detection of outbreaks in animal popula-her zoonotic or not, can be of great public health.e past decade has seen a growth in the litera-

with novel surveillance approaches, includingease in the use of cluster detection techniquesospectively to data, to the authors knowledge

no systematic overview of the application ofsurveillance to veterinary medicine. This paper

data.systementpotentypesgain

2. Posurve

A systewhenor timpopuThereto mmethreporundeand dspecitionalist oity ofdiseadisea

Inated preselivestaimssurveconsystestageulatioprocetory haveownetestinreecated

Thas sucess.of anet al.of dehealtthe otics obe dediseachara this review focuses on syndromic surveillancet are already operational or are in their imple-

phase, we also review studies investigating ther early detection of disease using alternativea available in animal health, to help the reader

of potential future developments.

ion coverage and timeliness in syndromice

ry assumption of any syndromic surveillancehat the behaviour of the population changes

health is affected, and that clusters (in spacethese behavioral changes can be detected if the

is continuously monitored (Mandl et al., 2004a).yndromic surveillance systems can be designede the main limitations of passive surveillanceased on laboratory conrmation and diseaselinicians (Bravata et al., 2004), namely: chronicrting; a long time lag between outbreak onsetsis; and a low sensitivity as a result of the highf these methods. The low sensitivity of tradi-illance relates to the focus on one disease or atable diseases, and the dependence on the abil-inician to recognize the clinical signs of specicspecial limitation in case of rare or emerginglman, 2003; Shephard, 2006; Shaffer, 2007)., the timeline and population coverage associ-ifferent surveillance strategies is schematicallyor three different target populations: humans,d companion animals. Syndromic surveillance

ducing the time lag associated with passivee by monitoring populations before laboratoryn. Under-reporting is also minimized by the

continuous screening of information at earliere disease process. As illustrated in Fig. 1, pop-erage is reduced as the timeline of the diseasetinues from the general population to labora-ation of diseases. Doherr and Audige (2001)

that in this pyramid of scrutiny the animald the veterinary practitioners act as a serialme, and the volume of laboratory submissionir judgement on the cost-benet ratio associ-e laboratory tests.me in Fig. 1 also indicates the loss in timelinessnce is applied further along in the disease pro-ness refers to the difference between the onsetak and the discovery of the outbreak (Wagner. Buckeridge (2007) reviewed the determinants

in automated surveillance systems in publicting out characteristics of the system and ofk that affect detection. The exact characteris-utbreak are unpredictable, but systems should

based on the expected characteristics of thehat it aims to detect (Mandl et al., 2004a). Thetics of the system listed by Buckeridge were the

F.C. Drea et al. / Preventive Veterinary Medicine 101 (2011) 1 17 3

Fig. 1. Schemof data used. Tinitial populatbars should beto illustrate co

choice of daand the det

The gainto the top son to the ron the assulows the idIn reality, tdiseases incase is consoratory consuspected tthe laboratorect tests arrepresents adisease, its disease prostatistical m

3. Syndrommedicine

ScientiMedical Subatic representation of the disease continuum in a population, and the surveillanche scheme illustrates the proportions of subjects in each step of the disease proceion. The absolute number of livestock, companion animals and humans exposed

interpreted as the scaled total population. Proportions are illustrative only. Simmparative abundance.

ta source, the sampling strategy of the system,ection algorithm choice and settings.

in timeliness as surveillance is applied closerof the scheme shown in Fig. 1, in compari-eporting of laboratory results, is usually basedmption that outbreak discovery closely fol-entication of positive cases (Shaffer, 2007).his will only be true for the introduction of

previously free zones/countries (any positiveidered an alarm), and in this special case lab-rmation depends on the veterinarian havinghe disease despite its absence in the region, andry having the specic test for it. Where the cor-e not ordered/performed, or the outbreak event

sudden increase in the incidence of an endemicdetection would likely occur much later in thecess, if at all, in a situation where continuousonitoring is not in place.

ic surveillance initiatives in veterinary

c literature was reviewed using the followingject Headings (MeSH): cluster analysis, disease

outbreak/veics applicatsearches wAbstracts. TElectronic gand also warning. PInternationsymposiumEpidemioloResearch Wto 2000 wepapers foun

A cursorveterinary and that a cclassied aour reviewsystematic sources thalance (as inthe sake oprimarily oonly atypice opportunities according to population targeted, and typess, for each of the three populations, in comparison to their

to any given disease is not likely to be equal, and the topilarly, icons are not intended to represent a true count, but

terinary, biosurveillance, medical informat-ions, and public health informatics. Keywordere primarily applied on PubMed and CABhe search was last updated in January 20111.rey literature was searched using these termssyndromic surveillance and early diseaseroceedings of the annual conferences of theal Society for Disease Surveillance (ISDS),s of the International Society for Veterinarygy and Economics (ISVEE) and Conference oforkers in Animal Disease (CRWAD) dated backre screened individually. References within thed were also scrutinized.y look at syndromic surveillance initiatives inmedicine reveals that this is an incipient eld,lear denition as to which systems should be

s syndromic is hard to achieve. We focused on any surveillance systems based on themonitoring of animal populations, using datat are timelier than traditional passive surveil-dicated in the left-most brackets of Fig. 1). Forf structuring this review, systems that focusn detection of emerging diseases, registeringal cases, are listed separately from systems that

4F.C.

Drea

et al.

/ Preventive

Veterinary

Medicine

101 (2011) 1 17Table 1Published initiatives in veterinary syndromic surveillance.

System/ref. Location Data Focus Animal type Syndromes Additional notes

VetPAD (McIntyre et al., 2003) New Zealand Clinical data frompractitioners

Surveillance of animaldiseases

Livestock Not aggregated (all clinicalcases)

Use of handheld computers.Engages participation by providingsoftware that contributes topractice management

mergences (Vourch and Barnouin,2003; INRA website)

France Clinical data frompractitioners

Early detection ofemerging diseases

Any species, anycountry, any disease focus on atypical casesand model diseases

Access through website,information includesfollow-ups

Rapid Syndrome Validation Projectfor Animals (DeGroot, 2005)

United States Clinical data frompractitioners


Livestock Focus on 6 groups ofnon-routine clinicalsyndromes

Various options for electronictransfer of data

National cattle health surveillancesystem (Bartels et al., 2006)

The Netherlands Unsolved cases byfarmers orveterinarians


Cattle Focus on individualdiseases.

Data compilation and analyses aredone weekly by a surveillanceteam, not automated

BOSS (Shephard, 2006) Australia Observations fromproducers and stockworkers


Livestock Software (BOVID) receivesinput concerning diseasesigns, and groups episodesinto organ systems

Takes advantage of audience indaily contact with animals;software to help producer withdiagnosing the problem engagesparticipation

Purdue University-BaneldNational Companion AnimalSurveillance (Glickman et al.,2006)

United States Clinical and laboratorydata, direct transfer

Sentinels for zoonoticdiseases; portal forevidence-based medicine

Companion animals Retrospective pilot: tickand ea vector activity;leptospitosis and ILI. Planto focus on othersyndromes

Makes use of already computerizedand centralized database, allowingfor daily automated analysis andgreat geographical coverage

Using pre-dx data from vet. lab. todetect disease outbreaks incompanion animals (Shaffer,2007)

United States Laboratorymicrobiology testssubmissions

Sentinels for zoonoticdiseases

Companion animals Direct map of test ordersinto 11 syndromic groups

Makes use of already computerizeddatabase, allowing for dailyautomated analysis. Use of testorders is timelier than results

LAHVA: Linked Animal-HumanHealth Visual Analytics(Maciejewski et al., 2007)

United States Clinical data fromhuman and pethospitals

Sentinels for zoonoticdiseases

Companion animals Pilot: seasonal u andwastewater contamination

Links in one tool the surveillance inpublic and animal health

FarmFile (Gibbens et al., 2008) United Kingdom Laboratory results Surveillance of animaldiseases

Livestock Focus on Diagnostic NotReached events to assessthe risk of new diseasesemergence

Not real-time, post-result based,but the focus on non-diagnosed isinnovative and adds values to thecurrent surveillance

SAVSNET (Tierney et al., 2009) United Kingdom 2 steps: (1) laboratoryresults; (2) real-timepractice-based


Companion animals Piloted using GIT Focus on information sharing tobenet not only populationmedicine, but also individual,evidence-based medicine

Syndromic surveillance amonglivestock entering an auctionmarket (Van Metre et al., 2009)

United States Animal observations byveterinarian duringauction market days


Livestock Syndromic groups Conceptually, can be implementedin handheld computers and giveimmediate feedback

Alberta Veterinary SurveillanceNetwork (Checkley et al., 2009)

Canada Disease andnon-disease eventsfrom practitioners


Livestock Syndromic groups Part of a network supported alsoby pathologists and aninvestigation network

Ontario Swine Veterinary-basedSurveillance System (OSVS)(Amezcua et al., 2010)

Canada Clinical data frompractitioners


Livestock Summarized by bodysystem and productioneffects

Formally evaluated to assesscompliance, completeness,coverage and timeliness. Resultsshow good acceptance


Table 2Peer reviewed publications investigating the potential of different datasets in implementing veterinary syndromic surveillance systems.

Study Location Type of data Goal Animal type Syndromes Evaluation

Salmonella outbreaksdetectionet al., 200

United Laboratory Public and Livestock Salmonella Assess the

Laboratory dsyndromisurveillan2007)

Livesto

West Nile vioutbreak (Leblond

Horses

Early-warnito reducein dairy c(Carpente2007)

Livesto

Detection ofin mares (2009)

Horses

target animbased on mnot only diof endemicwith the prpublic healreference, alogical ordeevaluating surveillancesystem, are

3.1. Syndroatypical cas

Vourch events assoin most of tion of unuThe same aatypical casunspecic crequiremengeneral syn

The mewas develofarmer comerinarian cveterinariannoticationtute for Agconrmationot reportidiseases wlic health i

es butny spessive nts of ment). Farmrstoodthe famationlemen

pathoeriod

novatin col (Kosmider6)

Kingdom results animal healthsurveillance

ata use forcce (Stone,

New Zealand Laboratorysubmissions

Animal diseasesurveillance

rusdetectionet al., 2007)

France Clinical datafrompractitioners

Sentinels forhumans

ng system abortionsattler et al.,

Denmark Clinical datafrompractitioners


abortionOdoi et al.,

United States Laboratorysubmission


al health surveillance as a whole. The latter areonitoring all clinical cases, aiming at detectingsease introduction, but also changes in trends

disease. Systems that monitor animal healthimary purpose of detecting zoonotic threats forth protection are also grouped separately. Forll the systems are listed in Table 1 (in chrono-r by publication date). Peer reviewed papers

the potential of specic datasets for syndromic, but not reporting the implementation of any

listed in Table 2.

mic surveillance based on notication ofes

bovinto, a

Paponeimple2006undevisit inforcompdata,and pan inmatioet al. (2006) presented a list of 14 emergenceciated with animal disease, and claimed thatthese the key to detection was the observa-sual signs or an unusual combination of signs.uthors argue that focusing on solely reportinges (as opposed to monitoring trends for severallinical signs) can reduce the reporting load andt for disciplined coverage associated with thedromic surveillance approach.rgences system (Vourch and Barnouin, 2003)ped in France based on two components: aponent via routine surveys on farms, and a vet-omponent (Vourch and Barnouin, 2003). The

participation is based on atypical clinical case on a website (INRA, 2010 National Insti-ricultural Research), and follow-ups. Monthlyn of vigilance is requested from veterinariansng any atypical cases. The system also tracksith emergence potential and/or known pub-mportance. The system currently focuses on

performed than automspecic emdiseases, raQuarterly re

The Rapdeveloped populationstions are grofocus on leand excludproblems. Sveterinarianputers, cell

3.2. Syndroclinical case

Becausemals can v(Davis, 200Typhimurium cases improvements neededin the data collectionprocess to allow for theimplementation ofearly detection systems

ck Test orders directlymapped intosyndromic groups

Discusses the potentialof the data for use insyndromicsurveillance, and theinherent biases

Neurologicalclinical cases

Retrospective analysisof an outbreak: alarmcould have been 4weeks earlier

ck Abortion Evaluation of thesystem included costsof false alarms versusthe cost of operatingthe system

Abortion Retrospective analysisof an outbreak: wouldhave detected 1 weekearlier

it is built to be generic allowing its applicationcies, any country, any disease.reporting of atypical cases is one of the com-the national cattle health surveillance systemed in The Netherlands in 2003 (Bartels et al.,ers or veterinarians report incidents not fully, motivated by the availability of specialists whorm free of charge, in order to collect detailed

and investigate the problem. The system isted by the continuous collection of censuslogy diagnosis in carcasses, toxicology tests,ical prevalence studies. While this representsve system for early disease detection and infor-lection, the data compilation and analysis is

by a surveillance team meeting weekly, ratherated. The team looks for signs of introduction oferging diseases, or analyzes trends of particularther than grouping information into syndromes.ports are made available to the public.

id Syndrome Validation Project (RSVP) was rstfor public health, and later applied to cattle

(RSVP-A) (DeGroot, 2005). Clinical presenta-uped into six syndromic groups that purposelyss common endemic disease presentations,

e the most common diseases and productioneveral forms of data capture are available fors to report observed cases, as hand-held com-phone, phone and fax lines, and the Internet.

mic surveillance based on analysis of alls

the clinical signs of diseases observed in ani-ary depending on a great number of factors4b), disease introduction events may initially


present as a collection of unspecic signs. Practitionersmay therefore fail to diagnose diseases outside their sphereof experience. Alternatively the signs may not be spe-cic enough to allow recognition that a new disease hasbeen introdmore surveeral signs, cases.

In 2003,in New Zealpractitioneity. Understkeep veterioffer some based on ptioner manawhich woulance prograttended byrecording amedication

Initiativdeveloped Surveillancet al., 2006based SurvIn the formtheir daily surveillancean investigother peopobservationans, using aforms and manageme

In Austrdrome data2006; ShepSurveillancsurveillancethe veterincontact witstock workis to provid based onthrough whferential dicattle, andinformationdecision arcan take aaffected, timease event,herd.

Recogniagnosed dua system obased on lanot reachednosis Analyet al., 2008

tical monitoring of the ratio of Diagnosis Not-Reached(DNR) samples to the total samples processed. Even thoughthis system is not designed to operate in real-time and usesinformation from the test results phase, a number of syn-

ic surding gre bodynds. Mnovatf the ses ba

l the sock. Sn animh, as is SmSNET) ), in des moaking ite.so in tInformtion fonce-bromic

of 60 toringcly avasts.

Syndroals as

hfordof vetem, basrrorisme CDCowitzrrorismeir detore of

increaation sivelyly becnimalal or inding ndeedstic a

ndromh dataotic di

on cans, bu

on t potenuced (Elbers et al., 2006). Recognizing this,illance systems are designed to monitor gen-rather than specic diseases or only atypical

McIntyre et al. reported on VetPAD, an initiativeand which aims to take advantage of veterinaryr data to improve disease surveillance capabil-anding that for the system to be sustainable andnarians engagement it needed to be simple, andadvantages for participation, the initiative wasroviding software that would help the practi-ge her/his practice using a handheld computer,ld electronically transfer data to the surveil-am. The data recorded includes all clinical cases

the veterinarian, and goes beyond diagnosis,lso procedures, treatment, laboratory samples,s, etc.es to collect practitioner data are also beingin Canada, through the Alberta Veterinarye Network (Berezowski et al., 2006; Checkley, 2009) and the Ontario Swine Veterinary-

eillance System (OSVS) (Amezcua et al., 2010).er veterinarians are encouraged to report allanimal health consultations. The veterinary

system is also supported by pathologists andation network, through which producers andle in contact with livestock can report atypicals. The OSVS is focused on swine veterinari-

variety of recording systems including paperhandheld computers, adapted to each clinicsnt.alia a system for electronic capture of syn-

from livestock has been piloted (Shephard,hard et al., 2006a,b). The Bovine Syndromice System (BOSS), a voluntary, producer-driven

system, extends the target audience beyondarians, including lay observers who are in dailyh cattle, such as stock inspectors, farmers anders. The method used to engage participatione a generic cattle disease diagnostic program

the BOVID system (Brightling et al., 1998) ich the producers can get a ranked list of dif-agnoses based on the signs observed in their

be advised of the precautions to take. The that the producer feeds to the software fore exactly those that the surveillance programdvantage of animal characteristics, numberse and place of occurrence, duration of the dis-

and management information regarding the

zing that new or emerging diseases can go undi-e to the lack of specic tests, in Great Britainf syndromic surveillance has been developedboratory submissions for which a diagnosis was. Building on the Veterinary Investigation Diag-sis (VIDA) system, the FarmFile system (Gibbens) among other improvements, included statis-

dromincluto thof trean inity odiseadata.

Allivestpaniohealttion (SAV2010Besidat mwebs

Alease attenevidesyndworkmonipubliforec

3.3. (anim

Asrole rorisbioteby thRabinbiotein thor mhaveincubintensimpand anaturdepecan idometest.

Syhealtzoonbasedhumbasedtheirveillance techniques are adopted by FarmFile,ouping test requests (including DNR) according

system affected, and an on-going monitoringoreover, the focus on DNR samples represents

ive initiative, potentially increasing the abil-current surveillance to account for emergingsed largely on what was previously discarded

ystems previously mentioned are focused onyndromic surveillance systems targeting com-als are usually designed with focus on public

discussed in the following topic. An excep-all Animal Veterinary Surveillance Network(Tierney et al., 2009; University of Liverpool,evelopment at the University of Liverpool.nitoring disease trends, the project also aimsthe collected information via reports in a

he United Kingdom, the National Animal Dis-ation Service (NADIS) (Anon, 2010) deservesr its support to animal disease monitoring andased medicine. Even though the system is notor prospective, it does include a unique net-veterinary practices and six veterinary colleges,

diseases in cattle, sheep and pigs, and publishesailable reports of disease trends and parasite

mic surveillance focusing on public health sentinels for human diseases)

et al. (2000) and Davis (2004a) reviewed therinarians in the preparedness against bioter-ed on the fact that almost all the biological

agents listed by a group of experts gathered in the United States in 1999 are zoonotic.

et al. (2006) reviewed several diseases with potential and the role of animal populations

ection. This is based on the assumption of onethe following factors being true: the sentinelssed susceptibility, would present with a shorterperiod, are likely to be exposed sooner or more

and continuously through the environment; orause the concomitant observation in humanss would add condence to the detection of antroduced disease threat. As Fig. 1 illustrates,on the disease, the number of sick animals

exceed the number of sick humans. Moreover,nimal populations may be easier to observe or

ic surveillance systems collecting animal for public health surveillance focus mainly onseases. These systems have thus far been largelyompanion animals, due to their proximity tot their choice of targeted animals can also behe susceptibility of the different species, andtial to signal disease before humans. Examples


of the latter are the systems based on the higher suscepti-bility of crows and horses to West Nile virus (Mostashariet al., 2003; Davis, 2004b; Johnson et al., 2006; Shuai et al.,2006; Leblond et al., 2007). One unique initiative high-lighted the also on We

Animal implementpopulationsElectronic Sof Commun2003), the Nlance SystemThreat Dataof the NorConsumer gated the plance of leincluded dhospitals.

Glickmastanding of animals eveThe authorsCompanionUnited Staamounts ofhospitals inby access todiagnostic system allothe hospitainfestation,inuenza-lifeasibility oanimals and

Shaffer emals as senimplementlaboratory the alreadynetwork. Mand directlyindependenusing the smanner, anby one sing

Maciejewframeworkdata and vLinked AniHuman dareceived in mal data is the surveillconcerns rethe system data sourcehuman andformed by rand wastew

4. Data sources for syndromic surveillance inveterinary medicine

In public health it has been noted that the ultimatee of tachemety ands andther co(Shephurposenuousd and

animahumaneking ity of ock, byis not ction cas tradepharndromding tose, an

(in fooavailacine, duterizards. es listem ofcreasecation

olunt

veragased bies vays toa stratble (Hreportgs, usins. Thitting port oftion ody to tem baivestocd likelns pepartice of restems, suchrveilla), and eping potential of zoo animals as sentinels, focusingst Nile virus detection (McNamara, 2007).data has been incorporated into a few

ed syndromic surveillance systems for human. Those reported in the literature include: theurveillance System for the Early Noticationity-based Epidemics (ESSENCE) (Babin et al.,orth Dakota Electronic Animal health Surveil-

(Goplin and Benz, 2007) and the Multi-Hazardbase (MHTD), a disaster preparedness projectth Carolina Department of Agriculture andServices (2007). Brianti et al. (2007) investi-otential for improving public health surveil-

ishmaniosis by a retrospective survey whichata from veterinary practitioners and from

n et al. (2006) highlighted the gap in our under-the dynamics and disease burden in companionn though they are in daily contact with humans.

were responsible for implementing a National Animal Surveillance Program (NCASP) in thetes in 2004 which took advantage of large

computerized data from a major chain of pet that country (450 hospitals), complemented

the computerized database from a network oflaboratories serving 18,000 pet hospitals. Thews daily data analysis of all clinical visits tol network. Results based on monitoring tick

leptospirosis in dogs and the occurrence ofke illness (ILI) in cats, have demonstrated thef conducting parallel syndromic surveillance in

humans.t al. (2007) evaluated the use of companion ani-tinels of infectious diseases in humans by theation of syndromic detection of diseases usingsubmission requests, also taking advantage of

available, electronic database of a laboratoryicrobiology test orders were transferred daily,

mapped into 11 syndromic groups monitoredtly. The authors report the positive results inystem for population surveillance in a timelyd highlight the wide geographic coverage givenle source of data.ski et al. (2007) reported the construction of a

for joint analysis of human emergency roometerinary hospital data (mostly pets), calledmal-Human Health Visual Analytics (LAHVA).ta is processed daily, while animal data isbatches every 13 weeks. The inclusion of ani-considered to add sensitivity and specicity toance, and takes advantage of the lower privacygarding animal data. Besides temporal analyses,advantages include the integration of differents, and the visual analytic tools that integrate

animal data. Testing of the system was per-etrospective analysis using seasonal inuenzaater contamination.

choicthe squalialarmis furdata the pcontistoreover,than of seseverlivestsion collesuch

Shof syinclupurptionspoor medicompstandtiativproblto innoti

4.1. V

Coincreentiting wnd tainaet al. in donariasubmno reticipaa stua sysical lwoulnariawith in cas

Sycasestle su2006at kerget for syndromic surveillance (according to in Fig. 1) depends on the balance between

timeliness, and the weight of the costs of false missed alarms. In animal health the decisionmplicated by the scarce availability of suitableard, 2006; Smith-Akin et al., 2007), which fors of syndromic surveillance should be acquiredly, in an automated routine, be electronicallytimely available (Mandl et al., 2004a). More-l data is subject to more non-disease variation

disease data (Kosmider et al., 2006). The ratecare is not only related to the awareness anddiseases, as in humans, but also, especially in

cost. In turn, the rate of laboratory test submis-only a result of diagnostic concerns; specimenan also take place for a variety of other reasonse certication, food safety monitoring, etc.d (2006) listed barriers to the developmentic surveillance systems in animal health ashe great diversity in species, production andd the hierarchical structure of animal popula-d production). Additional barriers relate to thebility of data sources in comparison to humanue to less frequent capture, often in a non-ed format, as well as less well developed dataThis section will review how some of the ini-ed in the previous section have dealt with the

nding adequate datasets, and their strategies population coverage compared to voluntary

of conrmed cases.

ary notication

e of systems based on passive notication can bey understanding the behaviour of the reportingeterinarians, animal owners, etc. and design-

positively inuence them. The challenge is toegy that is not only successful, but also sus-oinville et al., 2009). As early as 1998, Gobared a program for surveillance of causes of deathng the Internet to survey small animal veteri-e novelty resulted in 25 veterinarians activelycase materials and promoting discussion, but

the sustainability of the system and rate of par-ver time was found. Shephard (2006) reportedinvestigate the sustainability of implementingsed on veterinary voluntary reporting of clin-k cases. The results indicated that the systemy not be sustainable, especially due to veteri-rceptions of limited personal value associatedipation, and a view of increased risk of penaltyporting.

that focus only on the reporting of atypical as RSVP-A (DeGroot, 2005), the national cat-nce system in The Netherlands (Bartels et al.,emergences (Vourch and Barnouin, 2003), aimveterinarians involved by reducing the time


demanded of them the systems provide easy and quickreporting, through handheld computers or websites. TheRSVP-A and the national cattle surveillance system in TheNetherlands also promote participation by giving informa-tion feedbaquarterly rpating vetecomplianceet al., 2004of these sys

4.2. Clinica

In contrapayment isno requiremas insurancdata recordmanagemeand implemet al., 2007)

Despite records is bmal medicisyndromic 2009; Univebased, real-step, will taclinics in thagement. Thhowever, mdifferent so

The oppthe growth2004). The ion Animala coverage in the Unitof Baneld,country (Mand medicafrom the saminitiative (M

Automasystems tacomputerizcompanionwillingnesseffort of susystem. Engsystem to or by offeriby means oAlberta Vet2006; Checments of sy

Robothadepend unclinical datout any reto increase

training, return of the information collected with addedvalue to the practitioners (Bartlett et al., 1986, 2010), andnancial incentives to reporting (Checkley et al., 2009).

Herd m

tomaators owed (Bever nillanc

(2009ata reptabasse eveinariagroupled an

e BOSisease t herd ers dir

be loming sinary ce limi. This asing nof come incr

tools cack ofcompl

problata.

Labora

borato are tromestoms oler et d theyndrowed tta. Theissionrinariae corrndromboratoronicago, 20tion ofepreseestockgness

, 2006)ck to the public (in the form of publicly availableeports on the latter, but restricted to partici-rinarians in the RSVP-A). However, maintaining

over time remains a great challenge (Mandla). Reports on evaluations of the sustainabilitytems could not be found in the literature.

l data

st to human medicine, in veterinary clinics the due, in most cases, at the time of service, withent to transfer data to third-party payers, such

e companies. This has caused veterinary clinicing to be primarily focused on client and invoicent, and there has been little incentive to developent standards for disease coding (Smith-Akin.these bottlenecks, the use of computerizedecoming standard practice in companion ani-ne, offering opportunities for the collection ofdata. The SAVSNET for instance (Tierney et al.,rsity of Liverpool), which plans to use practice-time collected data in its next implementationke advantage of the fact that around 20% of pete UK use the same software for practice man-e lack of data standards in veterinary medicine,eans that data integration among clinics usingftware remains problematic.ortunities for data integration increase with

of corporate veterinary practices (Moore et al.,Purdue University-Baneld National Compan-

Surveillance (Glickman et al., 2006) reportedof 2% of the total pet dog and cat populationed States, by using the centralized database

a pet hospital chain widely spread across theaciejewski et al., 2007), and whose demographicl information is completely computerized. Datae hospital network are also used by the LAHVAaciejewski et al., 2007).

ted collection of clinical data is harder forrgeting livestock due to the lower level ofation in large animal practices, compared to

animal practices. These systems depend on the of the veterinarian to comply and take the extrabmitting their routine data to a surveillanceagement is sought by adapting the recording

the routine recording process of the practiceng feedback to the veterinarians and farmersf a complete investigation network, as in theerinary Surveillance Network (Berezowski et al.,kley et al., 2006; Checkley et al., 2009). Assess-stem sustainability have not been reported.m and Green (2004) stated that systems thatiquely on voluntary transference of routinea by veterinarians are not sustainable with-turn to the veterinarian. Proposed methods

veterinarian engagement include continuous

4.3.

AuindicrevieHowsurveet al.the dtle dadiseaveterboth detaians.

Thon ddirecfarmicallybecoveterwill bputerincrehelp

ThmentThe lever sameical d

4.4.

LaTheysyndsympBuehtigatefor srevieof dasubm(veteing thfor sy

LaelectGallestrucalso rin livwillinet al.anagement data

ted monitoring of herd management data andf production quality have been reported andartlett et al., 2010; De Vries and Reneau, 2010).o reports on implementations of syndromice systems based on these data were found. Mork) compared data kept on farmers records toorted by veterinarians to a dairy industry cat-

e in Sweden, and showed that only 54% of thents registered by farmers were treated by an. Even for those events that were reported bys, the farmers kept information that was mored specic than that reported by the veterinari-

S system (Shephard, 2006), even though basedevents, can be considered a system based oninformation, as it represents an effort to involveectly. Rate of underreporting should theoret-w, as it targets the population of animalsick, not the population of animals for whichare was sought. However, population coverageted by access to (and willingness to use) a com-is becoming less and less of a problem, as anumber of herds are already managed with theputerized systems.ease in the use of computerized herd manage-ould offer another opportunity for surveillance.

uniform standards among systems may how-icate integration, and it would suffer from theems discussed for capture of computerized clin-

tory data

ry test requests are a type of syndromic data.imelier than results, and can be grouped in

according to the nature of the disease and/orbserved by the veterinarian (Pavlin et al., 2003;

al., 2004; Ma et al., 2005). Stone (2007) inves- potential of using laboratory test requestsmic surveillance in veterinary medicine andhe potential biases associated with this type

author also pointed out the variability in the rates year to year, and misclassication biasesn not submitting the right sample or request-ect test), but concluded that the data is suitableic surveillance.ry test requests are more often automated and

lly recorded than clinical data (Sintchenko and09) and therefore these data allow for the con-

a sustainable surveillance system. Laboratoriesnt a more centralized source of data, especially

medicine. However, their use depends on the of data owners to share these data (Glickman.


It has been reported that laboratory test orders suf-fer from the low submission of specimens as part of thediagnostic process in veterinary medicine (Zurbrigg andBlackwell, 2009). However, Shaffer (2007) assessed thepotential ofsurveillancesistency ofthese data in the numan increasedata is anotmedicine ova baseline olance to com

Laboratothe animal tested for dthat four dtest requestesting, gov

The usesurveillanceAnimal HeaCanadian Fwork of feddiagnostic ltem for aniwith zoono2009). Theter (Gluck the Veterindom is devereal-time, aTable 2 prothe potenti(Kosmider e

4.5. Others

The limilance systedata notedsources arepotential.

Egenvallassessed thinsurance dIf the use ofthese data tion, and thwidespread

The woruse of directage of thisscreen a larreaching smexcluded ofquency of vfor the popauction maclinical case

Abattoirs represent a unique source of data for veteri-nary surveillance, compared to public health. Engle (2006)used the condemnation data available through the elec-tronic Animal Disposition Reporting System (eADRS) from

ood Saoncluinnesp to 1had betial foh monroughcted byme, anove sucNaman ofter high

g statiiduals. Zoosed as a

life. Thest Niully fonding ith e

ideas beyonevelopfers a

by thd contal aggm, espable.

plem anim

g. 2 susourceatial abreviewillance; Mandocus of veteranion

enit

toma deniable, aved coally coevidenme hesually microbiology test submissions for syndromic in companion animals assuming that the con-

test orders over time allows for the use ofin prospective monitoring, and that increasesber of test orders can be used as indicators of

in disease burden. The availability of historicher advantage of laboratory data in veterinaryer other types of data, since some estimation off disease burden is needed in syndromic surveil-pensate for the lack of denominator data.ry test requests screen a larger proportion ofpopulation than sick animals, as animals can beifferent purposes. Zhang et al. (2005) reportedifferent purposes were recorded as reason forts in their laboratory data: diagnostic, exporternment monitoring, and industry monitoring.

of laboratory data in veterinary syndrome appears to be a growing eld. The Canadianlth Surveillance Network (CAHSN), part of theood Inspection Agency, is establishing a net-eral, provincial and university animal healthaboratories to implement an early warning sys-mal diseases in real-time, especially diseasestic potential (Canadian Food Inspection Agency,

website of the Gluck Equine Research Cen-Equine Research Center, 2010) reported thatary Diagnostic Laboratory in the United King-loping a syndromic surveillance system in nearlso based on monitoring sample submissions.vides additional examples of investigations ofal of laboratory data on early disease detectiont al., 2006; Odoi et al., 2009).

ted number of implemented syndromic surveil-ms in veterinary medicine use the sources of

above. However, a variety of alternate data being explored for their syndromic surveillance

et al. (1998) and Penell et al. (2007) havee quality and completeness of computerizedata from dogs and cats, and horses respectively.

health insurance grows in veterinary medicine,may provide a source of centralized informa-e use of coding standards may become more.k of Van Metre et al. (2009) investigated thet observation in auction markets. The advan-

method is associated with the opportunity toge number of animals at once, and especially ofaller operations which may be systematically

other surveillance methods due to a lower fre-eterinary care (Van Metre et al., 2009). Evenulation under veterinary care, observations inrkets may be timelier than the observation ofs.

the Fand cand Med udata potenhealta thocollegramimpr

Mare aauthotorinindivtimeformtheirfor Wcessfexpa

Smtive goes are dtranswornitoreanimsysteavail

5. Imfrom

Fidata or spsive surve2004The ftics ocomp

5.1. D

Auclearavailobseris usuis no the saare ufety and Inspection Services (FSIS) in the USA,ded that a swine erysipelas outbreak in Iowaota during July 2001 could have been identi-0 months earlier if automated analysis of theen in place. Weber (2009) also evaluated ther using condemnation data to set up an animalitoring system. Benschop et al. (2008) provided

temporal and spatial analysis of abattoir data the Danish Swine Salmonellosis Control Pro-d its potential for temporal monitoring and torveillance design.ra (2007) drew attention to the fact that zoos

n overlooked source of surveillance data. Thelighted their role as epidemiological moni-

ons, as they contain a population of known at a point-source location that are followed over

have historical data on animal tests that are per-nimals are received and regularly throughoute author reported the success of a Surveillancele Virus in Zoological Institutions that ran suc-r 4 years, and is now being used as a model forH5N1 surveillance in the United States.t al. (2006b) presented even more innova-to collect livestock health information thatd clinical, sporadic information. The authorsing a telemonitoring system that continuouslynimal health data from devices permanentlye animals. Data would be collected and mon-inuously through devices placed in points oflomeration within the farm. Evaluations of theecially of its cost-effectiveness, are not yet

entation of disease aberration detectional health data

mmarizes the process of using animal healths to monitor disease trends and detect temporalerrations in the number of cases. Comprehen-s of each of the components of a syndromic

system are available elsewhere (Lober et al.,l et al., 2004a; Shephard, 2006; Shaffer, 2007).f this review is on the particular characteris-inary syndromic surveillance, in livestock and

animals.

ion of events and syndromes

ted disease monitoring systems must make ation of what constitutes one event in the datas the statistical analyses are typically based onunts. For companion animals each patient entrynsidered to represent an event, as long as therece that repeated encounters are associated withalth event. In the case of livestock, health eventsenumerated at herd level.


Once thcriteria to band devisintocols are detection syThe classigoals, but mthe data grothe alert de

In publicpurposes udards for lathe integraor laboratorformed by data are nomust be traon the dataeach event/ing algorithnurses duridata) (Ivano

Vocabulnot as unias is the c(2002) repoFig. 2. Process of monitoring disease trends and detecting clusters using anim

e events have been identied, identifying thee used to group these into specic syndrome(s)g reliable/automated data classication pro-essential components of an early epidemicstem (Ivanov et al., 2002; Mandl et al., 2004a).

cation protocol must be based on the systemust also relate on the specic data in hand, asuping will likely inuence the performance oftection algorithm (Shaffer, 2007).

health, clinical data is usually coded for billingsing standard nomenclatures, and coding stan-boratory data are also available, allowing fortion of multiple sources of data. When clinicaly data are coded, classication can often be per-directly mapping codes into syndromes. Whent coded, automated classication algorithmsined to recognize relevant medical information, and determine the syndrome associated withunit. A common example is the use of text min-ms to extract information from text entered byng triage in emergency rooms (chief complaintv et al., 2002).

aries and standards for data classication areed in animal health (Smith-Akin et al., 2007)ase for human health. Wurtz and Popovichrted on the range of codes that do exist, but

noted that codes are ature for Vetthe Nationaaddition vegeneral hea(which has of Human areported thcreated in 1teaching hobecause sevupload to thospitals aldard systemthis paper r

In the abin the impltem is the to assign econsultatiowith staff dle data reorders shonal syndrowere: respal health data sources. Synd: syndrome.

these are not widely used in clinics. Numericalvailable through the Standardized Nomencla-erinary Diseases and Operations (SNVDO), andl Animal Health Reporting System (NAHRS). Interinary input has been incorporated into morelth ontologies such as HL7, LOINC, and SNOMEDbeen renamed the Systematized Nomenclaturend Veterinary Medicine). Bartlett et al. (2010)at the Veterinary Medical Data Base (VMDB),964 to store all clinical cases seen in veterinaryspitals across North America, is not up to dateeral schools are behind in coding their cases forhe database; a problem that would not exist ifready coded their cases routinely under a stan-. None of the surveillance systems presented ineported using a standard classication system.sence of standard nomenclature, a key elementementation of any syndromic surveillance sys-denition of syndrome groups and the rulesvents membership. Shaffer (2007) reported an with a group of seven veterinarians, togetherfrom the diagnostic laboratory who han-gularly, to determine which laboratory testuld be mapped into which syndromes. Themic groups identied during this consultation

iratory, GIT, neurologic, dermal, reproductive,


endocrine, hepatic, infectious, febrile, renal, and non-specic. Stone (2007) also mapped laboratory data intogroups based mainly on organ systems. After some stan-dardization of the data these records were groupedinto the foltion, alimencirculatory/phoreticulaperinatal losudden deanosis was n2008) weretogether wat submissisyndromic grespiratorylatory, reprand fetopatping withinthe data colby veterinaorder to im

For mosin this revidromic grouhad yet beestill being pand these hRSVP-A systion is decithe atypicaAlberta Vettem (Shephdiagnostic ble diagnosreported catem) to deliused withingenital and death or reabnormal.

5.2. Aberra

Monitorretrospectivis used to terns in dahypothesis.outbreak deing at deterange of dat(Buckeridge(2004b) sumdata for ousied into data to estacases; compabnormal ation of the and investig

The choice of algorithm to detect abnormal activities isbased on the type of data (number of time series to mon-itor, whether rates or counts are monitored, rare versusfrequent counts, temporal or spatio-temporal data); the

abilitye of thpecten asse

(abilitoid faave bnter, ; Frickoal he

e veteris revieir inis beinling thr temums ae mo

; Gopli; Webesource

Contrl numbof exp

obserbutionand thbservaenney

methf the d

assument, st). Hownimalially when hods cappliedspectivor prourdueurveilnalysis(Bensc

(2006itish lach.ograpinto dis app). Publconceriejewsroblemh evenstem lowing categories: reproductive system, abor-tary system/oral, anorexia/depression/malaise,oedema/anaemia; diarrhoea/dysentery, lym-r, mastitis, musculoeskeletal, nervous system,sses, respiratory system, skin/photosensitivity,th, and urinary/renal. Samples for which a diag-ot reached in the FarmFile system (Gibbens et al.,

mapped into syndromes based on body systemith the information given by the veterinarianon concerning observed clinical signs. The nalroups in that system were: systemic, digestive,

, urinary, musculoskeletal, nervous, skin, circu-oductive, other, disease type unknown, mastitishy. The implementation of this syndrome map-

FarmFile generated feedback which improvedlection forms. The list of clinical signs to be usedrians when submitting samples was revised, inprove syndromic classication of the data.t of the systems based on clinical data listedew the protocol for classifying data into syn-ps could not be found in the literature, or nonen implemented. A number of the systems areiloted using one or a few specic syndromes,ave so far been identied retrospectively. Thetem uses six syndromic groups, but classica-ded and entered by the veterinarian reportingl cases observed; this is also the case of theerinary Surveillance Network. In the BOSS sys-ard, 2006) the BOVID software, a rule-based

program designed to identify the most proba-is based on clinical signs reported, classies theses into syndromic groups (based on organ sys-ver counts by syndrome. The syndrome groups

BOSS/BOVID are body; ears/eyes; airways; GIT;urinary system; nervous; skin; cardio-vascular;duced production; and muscle, bone or gait

tion detection algorithms

ing of time series data in surveillance can bee or prospective. Retrospective surveillanceexplain temporal and spatio-temporal pat-

ta, and is therefore used in the generation of In syndromic surveillance, where the focus istection, statistical analysis is prospective, aim-cting meaningful changes from the expecteda values, which are referred to as aberrations

et al., 2005; Hohle et al., 2009). Mandl et al.marized the methodological stages to process

tbreak detection, once events have been clas-syndromic groups, as: evaluation of historicalblish a baseline model for the expect number ofarison of observed values to baselines, to detectctivities if occurring; culminating in an evalua-alert and a decision as to whether noticationation should take place.

availnaturare exand ativityto avtion hCarpe2005The gin thin thin thrithmdetai

Fotive sare th20062009data data.smalolds thosedistriolds, the oits (Bthesetent obasicpend2007and aespec

Wmethods aretrotial fthe Pmal Sthe adata et al.in Brappro

Gedata ysis 2007vacy (Macthe phealtthe sy of historical data to construct baselines; thee disease being monitored (whether outbreaksd to occur as spikes, a sudden or slow increase);ssment of the desired balance between sensi-y to detect true alarms) and specicity (abilitylse alarms). Algorithms for outbreak detec-een thoroughly reviewed elsewhere (Ward and2000a,b; Carpenter, 2001; Buckeridge et al.,er, 2006; Shephard, 2006; Moore et al., 2004).re is to list the methods that have been citedinary syndromic surveillance systems coveredew. However, as many of the systems are stilltial implementation phase, the types of algo-g used were often not identied; and a columnis information could not added in Table 1.poral analysis, control charts (such as cumula-nd exponentially weighted moving averages)st commonly employed algorithms (Shephard,n and Benz, 2007; Shaffer, 2007; Checkley et al.,r, 2009). This is not surprising, as for most of thes used there is limited availability of historicalol charts require limited baseline data, using aer of previous observations to establish thresh-ected values, based on the assumption thatvations came from a pre-specied parametric. New observations are compared to the thresh-e system is determined to be out-of-control iftions fall beyond the calculated expected lim-an, 1998). Performance is not optimal, sinceods do not exploit the full information con-ata, and because health data often violates theptions of control charts that events are inde-

ationary and normally distributed (Lotze et al.,ever, the popularity of these methods in public

health surveillance attest for they usefulness,hen historical data is limited.istorical information is available regressionn be used. Published work on regression meth-

to veterinary data have thus far focused one analyses, as a means of assessing their poten-spective modeling. For instance the work of

University-Baneld National Companion Ani-lance (Glickman et al., 2006) with clinical data,

of the Danish Salmonella Control Programmehop et al. (2008), and the work of Kosmider) based on laboratory detection of Salmonellaivestock, are all examples which adopt this

hical information is often used to aggregateemographic areas, after which temporal anal-lied to these areas independently (Shaffer,ic health systems are usually restricted by pri-ns regarding address information from patientski et al., 2007), while in animal health systems

is the lack of geo-location data relating tots. Often the only geographical information inrefers to the practitioner location or postal code


(Shaffer, 2007). This represents a challenge for use in spa-tial analysis of animal surveillance, since the geographicalradius of clients attended by each practitioner is not usuallydetermined and may vary greatly, particularly in regions oflow farm an

Where ssystems remethod waet al., 2009)able softwaSpatial clusthe R statisAlberta Vet2009).

5.3. Evalua

The fralance systereviewed in(Buehler et checklist toprocessing,sis, interprefor descriptincluding: ustability anda veterinaryuated for: pand transfegrammed afreedom.

More quposed to spdetection athus to evalation levelperformancincludes th(Buckeridgepropose meof the timesaved (Kleitional receifor diagnos

Ultimatesystem to dthe outbreadetection asuggested wholly simpatterns ofoverview oet al., 2005.nents operahas been imreview havdescribed astill in deveever, variouof different

even limited, evaluation was reported, notes have beenadded to Tables 1 and 2.

The OSVS (Amezcua et al., 2010) was the only system forwhich an evaluation of the characteristics of data acquired

gh praated tded be samry. Coletionimelinlly inc, as we mn 2003d of 6of whted thic sursts ni

were nciden

was nard fog. Theack ofe datahard, A indiximat

that dhave sewaterspectivion in

iscussi

pre-co discusfor efville eeet s

hat m diseaf emesource

develonovated sysn, andiscussns can

nly sys on sy

been oubt thnced bl appron. The

ose read/or practitioner density.patial cluster analyses were performed in theviewed here, the most commonly reporteds the scan-statistic (Odoi et al., 2009; Perez, which can be performed with the freely avail-re SaTScan (Kulldorff, 1997; Kulldorff, 2006).ter detection using algorithms available withintical package was reported in the case of theerinary Surveillance Network (Checkley et al.,

tion

mework for evaluating public health surveil-ms for early detection of outbreaks was

2004 by a working group promoted by the CDCal., 2004). The document contains an operations

review system-wide issues, data sources, data statistical analysis, and epidemiological analy-tation and investigation. It sets out a frameworkion and evaluation of any system as a whole,sefulness, exibility, acceptability, portability,

costs. In a similar way, Stone (2007) stated that syndromic surveillance system should be eval-opulation coverage, automation of data capturer, value to users, detection efciency of pro-lgorithms, and contribution to claims of disease

antitative evaluation methods have been pro-ecically evaluate the performance of variouslgorithms, using real or simulated data; andluate the systems performance at the popu-

in the similar way to which test diagnostice is evaluated for individual testing. Thise measurement of sensitivity, and specicity

et al., 2008). Kleinman and Abrams (2006)thodologies which also include an evaluationliness of a system and the number of lives

nman and Abrams, 2008), based on the tradi-ver operating characteristic (ROC) curves usedtic tests evaluation.ly, the factors that affect the ability of anyetect outbreaks also depend on the nature ofk (Buckeridge, 2007). Evaluation of outbreaklgorithms based on simulated data has beenin the literature. These evaluations may useulated data sets or may superimpose various

simulated outbreaks onto authentic data. Anf these approaches can be found in Buckeridge

A holistic evaluation of how all system compo-te in real time is only possible once the systemplemented. None of the systems listed in thise been formally evaluated using the metricsbove; this is not surprising as most of them arelopment and few are fully operational. How-s authors have attempted to assess the quality

system components. In those cases where any,

throuestimproviby thoratocompand tactuastudy

Thbut iperiotwo repordromrequetionsthe icitystandpeninthe lof th(ShepLAHVapproand also wastretroabort

6. D

A 2009ods (Hoincan ming tmanytion odata data,cal indesiripatiohas dulatio

Obasedhavetle denhanoveizatioto thctitioners reports was performed. The authorshe level of compliance by comparing the datay practitioners against the submissions madee veterinarians to Ontarios Animal Health Lab-mpleteness of data (measured in terms of the

of each form eld), coverage of the program,ess for reporting were evaluated. Completenessreased from the rst to the second year of theell as coverage of farms in the province.ergences system was not formally evaluated,, Vourch and Barnouin reported that over a

months the system received 33 notications,ich were considered atypical. Shaffer (2007)at during the pilot study of the described syn-veillance system based on microbiological testne clusters were detected. Follow-up investiga-able to link two of these to a true increase ince of disease. Assessing sensitivity and speci-ot considered viable due to the lack of a goldr determining when outbreaks were really hap-

BOSS system was also not evaluated due to a standard against which the completeness

received from producers could be assessed2006). Retrospective analysis of the data oncated that respiratory symptoms in dogs occurely 10 days earlier than is the case for humans,etection of eye-inammation in dogs woulderved as a sentinel for humans in a case of

contamination (Maciejewski et al., 2007). Alsoely, Odoi (2009) showed that an outbreak of

mares could have been detected 6 days earlier.

on

nference workshop at the ISVEE meeting insed the development and application of meth-fective surveillance in livestock populationst al., 2009). Syndromic surveillance systems

everal of the surveillance goals proposed dur-eeting, including: comprehensive coverage ofses within a single monitoring system, detec-rging diseases, maximizing the value of existings, integration of public health with veterinarypment of new analytical methods, technologi-

ion, exibility in the type of data available andtem outcome, encouraging stakeholder partic-

an increase in negative reporting. This papered how syndromic surveillance in animal pop-

help meet many of these goals.tems that explicitly address outbreak detectionstematic monitoring of animal population dataincluded in this review. However, there is lit-at disease control capabilities have also beeny systems for disease monitoring which adoptaches to data sharing, integration and visual-

authors recommend the following examplesders interested in exploring the broader appli-


cation of information systems to veterinary surveillance:the Michigan equine monitoring system (Kaneene et al.,1997); the Pathman project (Durr and Eastland, 2004);the Rapid Analysis & Detection of Animal-related Risks(RADAR) (SBioPortal Sytion system(Cameron, industry in(Davies et athe papers

The initsources ofimplementmedicine.

Due to teach syndroerinary medtheir own cdards are ncapability ting systemits own arcdata integracomparisonpractices ov

As a reto date havhealth dataalready avasurveillanceintegrationautomated on these eaconsiders thwith potening the useillustrating dromic surinitiatives cize the moin public hsurveillancea rather looreference tosystematic Not all of ththe classic

Despite reviewed aquality andhealth dataadvances inmal surveil

Many ofhave attempassive notdirectly fromin the systeability issueto participa

to sustain participation, but in general the lesson learned isthat if data transfer demands extra effort from participants,long term sustainability may not be possible.

Given the current barriers, diagnostic laboratoriesar to promic e of lcine, bata. Int andfor conimaleady c

will iillanceatory le souatorie

raphictmentock lan of de exph has

and ase in ork o

h has iness o). Impalth htious ded therovid

uct pr; Lombfore, a

vailablillanced com

all st is sdetectlacem), andtion a(Buehlnto acng subin caseable frner, 20be wasannot

alarmfore b

systemndromh go arpenmith et al., 2006a; Paiba et al., 2007); the FMDstem (Perez et al., 2009); geographical informa-s for the surveillance of bluetongue in Australia2004) and Italy (Conte et al., 2005); the swineitiative for disease data sharing in Minnesotal., 2007); GLiPHA (Clements et al., 2002); and

of Egbert (2004) and Durr and Eastland, 2004.iatives reviewed have made use of several

clinical and diagnostic data in order to syndromic surveillance system in veterinary

he lack of commonly adopted data standards,mic surveillance system implemented in vet-icine to date has tended to develop and validatelassication system. As long as common stan-ot adopted, new systems will have limited

o take advantage of the progress made by exist-s. While each method may be valid withinhitecture, the use of standards would enabletion across heterogeneous datasets, and allows among geographical locations and veterinaryer time (McIntyre et al., 2003).sult of the current limitations, most effortse been directed towards developing animal

collection strategies, analyzing historical datailable for their potential to support syndromic, or solving problems of data classication and

; rather than focusing on the development ofsyndromic analysis. The concentration of effortrly stages of development is evident when onee relatively plentiful supply of papers dealing

tial data sources, in contrast to those report- of various aberration detection algorithms,systems outputs or evaluating operational syn-veillance systems. In fact, none of the listedontain all the components which character-

re mature systems for early disease detectionealth. In consequence, the term syndromic has been applied throughout this review inse manner, since the term has been coined in

early disease detection systems based on themonitor or large amount of pre-diagnosis data.e systems reviewed here are strictly based onation of data into syndromes.differences in structure, all of the initiativesre making efforts to improve the quantity,

speed of information extraction from animal, and the lessons learned will support further

the development of the eld of syndromic ani-lance.

the systems developed in veterinary medicinepted to solve data limitations by encouragingication of cases or transfer of clinical data,

farmers, or by enrolling private veterinariansm. All these systems are dealing with sustain-s. Information feedback or nancial incentivesting veterinarians have been used as strategies

appesyndnaturmediical dcurreboth ion ais alrdardssurvelaborreliabLaborgeoginveslivestgratio

Thhealtmentfor uthe wwhictimel2006lic heinfecmentand pcond2003Thereily asurvecal aneld.

Inoutpution a rep2003detecgists take imakitem, avail(Wagmay gist cto antherelance

Sywhicand Crovide a readily available source of data forsurveillance in animal health. The less timelyaboratory data is compensated, in veterinaryy its greater specicity when compared to clin-n addition there is reasonable availability of

historical laboratory data in digital format,mpanion and livestock animals. In compan-

medicine, where computerization of recordsommon, investments in the use of data stan-ncrease the value of clinical data for syndromic

use. In livestock health, however, the use ofdata remains the most readily available andrce of electronic, continuously recorded data.s are typically centralized and can cover largeal areas. However, it is also important that

be made in data standardization within theboratory sector as this would allow for the inte-atabases across broader geographical areas.ansion of syndromic surveillance in publicfomented great improvements in the develop-daptation of aberration detection algorithmshealth data, as demonstrated for instance byf several teams within the BioALIRT project,been sponsoring research on improving thef outbreak detection since 2001 (Wagner et al.,

lementation of syndromic surveillance in pub-as also resulted in the expansion of the eld ofisease informatics. Several teams have docu-ir experiences in creating information systemsed guidelines on the architecture necessary toospective, real-time surveillance (Tsui et al.,ardo and Buckeridge, 2007; Zeng et al., 2011).s quality animal health data become more read-e, the development of veterinary syndromic

will be able to take advantage of the statisti-putational advances made in the public health

yndromic surveillance systems the primaryome form of alarm in the event of aberra-ion. However, syndromic surveillance is notent for traditional surveillance (Pavlin et al.,

therefore once an alarm is triggered by thelgorithm it must be reviewed by epidemiolo-er et al., 2004). The design of the system shouldcount the information that will be needed whensequent decisions and the outputs of the sys-

of any alarm, should contain all the informationom the syndromic dataset that may be of value03). The investment in syndromic surveillanceted if, once a decision is made, the epidemiolo-count on an investigation team ready to respond; the process for aberration follow-up shoulde described as part of the syndromic surveil-

design (Stone, 2007).ic surveillance systems can confer benets

beyond the detection of true alarms (Wardter, 2000b). They can support additional goals


associated with animal health surveillance, such as: mon-itoring disease trends; facilitating the control of diseaseor infection; supporting claims for freedom from diseaseor infection; providing data for use in risk analysis, foranimal andthe rationamost systemto the longicontribute for epidemand testingmedicine. Ato deliver tsurveillanceparticipatin2005; Glick

The devsurveillancecommunityThe automadata also fatems, and rA recent revbeen on-goin surveillaout in that rbeen evaluaing betweenare not as republic healtrelated to dbarriers to adopted stawithin and

The longsyndromic tem maintejustied bydetection (Cprogress instimulated collection mity issues anotication

While inbased on sgency visitsdata availathat there continuousmentationssustainablepractitionesystems wpractitioneare often ment. Clinimore oftenauthors havand unrelialance teams

Until investments have been made to solve these issues,laboratory data continues to offer the greatest poten-tial for syndromic surveillance in veterinary medicine.Similar problems to those associated with clinical comput-

d datainvestm

acrossever, ug eachraphical datder wis revillancing. Aloped able of automent

mputeack of databmplem

by thertunitiding in.

icts o

ere ar

owled

is woultureal Hea

ences

al Anime Netwtp://wwua, M.R

the Onented fod, D.A., gical tert. Med.

S.M., carkom, Hle biote

eeting os, C.J.M.,n der Zww to inth Intermics, 20tt, P.C., velopmmiologitt, P.C., Vce and

ed. 94, 2yan, J.C.l and h

fect. Con/or public health purposes; and substantiatingle for sanitary measures (OIE, 2010). In practice

s designed for early detection of disease, duetudinal nature of their data collection, will alsoto situational awareness, building a foundationiological research and hypotheses generation, and thus provide support for evidence-based

number of the systems reviewed here intendhe information extracted from the syndromic

process to the public (Tierney et al., 2009) or tog veterinarians (McIntyre et al., 2003; DeGroot,man et al., 2006).elopment of the eld of syndromic animal

progressively enhances the animal healths ability to detect and to respond to outbreaks.ted and continuous collection of animal healthcilitates the integration with public health sys-epresents a further step towards One Medicine.iew (Vrbova et al., 2010) noted that there haveing efforts to integrate human and animal datance initiatives since 2000. However, as pointedeview, none of the integrated systems have yetted and there are several barriers to data shar-

the two domains. Ethical and privacy concernsstrictive in animal health data, as they can be inh. Nevertheless, barriers to data sharing, mainlyata ownership and proprietary information, anddata integration due to the lack of commonlyndards continue to impair the communicationbetween animal and public health data sources.er experience of public health systems with

surveillance has indicated that the cost of sys-nance and response to false alarms can only be

the systems contribution to more than eventhretien et al., 2009). In veterinary medicine, the

the development of early warning systems hasthe review, improvement and expansion of dataethods in animal health, though sustainabil-

re now evident for systems based on voluntary or passive data transfer from veterinarians.

public health, syndromic surveillance can beales of over-the-counter medicine, or emer-, in animal health the earliest type of syndromicble is clinical. Several initiatives have shownis potential for clinical data to be used in the

monitoring of animal populations, but imple- in real-time still depends primarily on nding

ways to collect and process clinical data fromrs. To achieve this, investment is needed inhich enable information ow from livestockrs to surveillance teams, and nancial incentivesnecessary to guarantee practitioner engage-cal data from companion animal medicine is

computerized, and offers greater potential, bute indicated problems concerning data sharing,ble ow of data from providers to the surveil-.

erizeand tionsHowitoringeogclinicprovi

Thsurvegrowdeveavaillack oimpleof cothe ltiple and itiedoppoproviment

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BennetroIn such as the need for data sharing agreementsents in data classication to allow integra-

different plataforms, apply to laboratory data.ntil data integration problems are solved, mon-

single source of laboratory data may still offeral coverage greater than any single source ofa. Systems implemented directly with the dataill minimize data ow issues.iew has illustrated that the eld of syndromice in veterinary medicine is incipient, but fasts syndromic animal surveillance systems haveover the past decade, limitations in the datan animal health have become apparent. Themated data collection limited opportunities foration of systematic monitoring systems; lackrized records limited automated analysis; andstandards limited the integration across mul-ases. The costs of overcoming these barriersenting real-time monitoring systems are jus-ir utility. Syndromic surveillance systems offeres that go beyond early detection of diseases,formation to aid planning and policy develop-

f interest

e no conicts of interest.

gements

rk was supported by the Ontario Ministry of, Food and Rural Affairs (OMAFRA) through anlth Strategic Investment (AHSI) grant.

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