Epidemiological modelling for the assessment of bovine tuberculosis surveillance in the dairy farm network in Emilia-Romagna (Italy)

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ContentslistsavailableatScienceDirect

Epidemics

jo u rn al h om ep age : w w w . e l s e v i e r . c o m / l o c a t e / e p i d e m i c s

Epidemiological

modelling

for

the

assessment

of

bovine

tuberculosis

surveillance

in

the

dairy

farm

network

in

Emilia-Romagna

(Italy)

Gianluigi

Rossi

a,∗

_,

_Giulio

_A.

_De

_Leo

b

_,

_Stefano

_Pongolini

c

_,

_Silvano

_Natalini

d

_,

Simone

Vincenzi

e,f

_,

_Luca

_Bolzoni

c,g

a_Dipartimento_di_Bioscienze,_Università_di_Parma,_Parco_Area_delle_Scienze_11/A,_I-43124_Parma,_Italy b_Stanford_University,_Hopkins_Marine_Station,_Paciﬁc_Grove,_CA_93950,_USA

c_Istituto_{Zooproﬁlattico}_Sperimentale_della_Lombardia_e_{dell’Emilia-Romagna,}_Via_dei_Mercati,_13/A,_Parma_I-43122,_Italy

d_Servizio_Veterinario_e_Igiene_Alimenti_Assessorato_Politiche_per_la_Salute_Regione_{Emilia-Romagna,}_Viale_Aldo_Moro_21,_Bologna_I-40127,_Italy e_Center_for_Stock_Assessment_Research,_Department_of_Applied_Mathematics_and_Statistics,_University_of_California,_Santa_Cruz,_CA_95064,_USA f_Dipartimento_di_Elettronica,_Informazione_e_{Bioingegneria,}_Politecnico_di_Milano,_Via_Ponzio_34/5,_I-20133_Milan,_Italy

g_Department_of_Biodiversity_and_Molecular_Ecology,_Research_and_Innovation_Centre_–_Fondazione_Edmund_Mach,_San_Michele_all’Adige,_Trento,_Italy

a

r

t

i

c

l

e

i

n

f

o

Articlehistory: Received17March2014

Receivedinrevisedform11February2015 Accepted25February2015

Availableonline6March2015 Keywords: Epidemiologicalmodel Diseasesurveillance Networkmodel Bovinetuberculosis SEImodel

a

b

s

t

r

a

c

t

Assessingtheperformanceofasurveillancesystemforinfectiousdiseasesofdomesticanimalsisa chal-lengingtaskforhealthauthorities.Therefore,itisimportanttoassesswhatstrategyisthemosteffective inidentifyingtheonsetofanepidemicandinminimizingthenumberofinfectedfarms.

Theaimofthepresentworkwastoevaluatetheperformanceofthebovinetuberculosis(bTB) surveil-lancesysteminthenetworkofdairyfarmsintheEmilia-Romagna(ER)Region,Italy.AbTB-freeRegion since2007,ERimplementsanintegratedsurveillancestrategybasedonthreecomponents,namely rou-tineon-farmtuberculinskin-testingperformedevery3years,tuberculinskin-testingofcattleexchanged betweenfarms,andpost-morteminspectionatslaughterhouses.Weassessedtheeffectivenessof surveil-lancebymeansofastochasticnetworkmodelofbothwithin-farmandbetween-farmbTBdynamics calibratedondataavailableforERdairyfarms.Epidemicdynamicsweresimulatedforﬁvescenarios: thecurrentERsurveillancesystem,anosurveillancescenariothatweusedasthebenchmarkto charac-terizeepidemicdynamics,threeadditionalscenariosinwhichoneofthesurveillancecomponentswas removedatatimesoastooutlineitssigniﬁcanceindetectingtheinfection.Foreachscenarioweran MonteCarlosimulationsofbTBepidemicsfollowingtherandomintroductionofaninfectedindividual inthenetwork.Systemperformanceswereassessedthroughthecomparativeanalysisofanumberof statistics,includingthetimerequiredforepidemicdetectionandthetotalnumberofinfectedfarms duringtheepidemic.

Ouranalysisshowedthatslaughterhouseinspectionisthemosteffectivesurveillancecomponentin reducingthetimefordiseasedetection,whileroutinesurveillanceinreducingthenumberofmulti-farms epidemics.Ontheotherhand,testingexchangedcattleimprovedtheperformanceofthesurveillance systemonlymarginally.

1. Introduction

Bovine tuberculosis (bTB) caused by Mycobacterium bovis is amongthemajordiseasethreatstofarmanimalsworldwide(Skuce etal.,2012).bTBisachronicdiseasecharacterizedbyavariable andgenerallylongincubationperiod,anditsaetiologicalagenthas anexceptionallywiderangeofhosts,includinghumans,domestic

∗ Correspondingauthor.

E-mailaddress:gianluigi.rossi@nemo.unipr.it(G.Rossi).

andwildanimals,aswellashighpersistenceintheenvironment (Morrisetal.,1994).Besidesitsdirectimpacttothecattle indus-try,bTBisazoonoticdiseaseofgreatconcern.Forthesereasons, regulatoryrestrictionsareinplacetopreventthetradeofinfected animalsandtheirproductswithinandbetweencountriesandto reducetheriskofspilloverfromtheanimaltothehuman compart-ment.Mostcountriesandtransnationalareas(suchastheEuropean Union)withathrivingcattleindustryhavedevelopedsurveillance systemstopreventbTBoutbreaks(Cousins,2001).Despitethe con-trolefforts,bTBisstillendemicinmanycountries,bothdeveloped anddeveloping(WorldOrganizationforAnimalHealth,2008)and http://dx.doi.org/10.1016/j.epidem.2015.02.007

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itseradicationhasprovedtobeverychallenging(Fitzgeraldand Kaneene,2013;IndependentScientificGroup,2007).Therefore,it isessentialtodevelopsurveillancestrategiesthatallowforarapid detectionofinfectedanimalsbothinendemicandbTB-freeareas. Theorganizationandthecomponentsneededforaneffective surveillancesysteminagiventerritorydependonbTB epidemi-ology,which islargely determinedbythecharacteristicsofthe farmingsysteminplace.Howthesefactorsmayinterplayto deter-minetheriskofoutbreaksinthespecificcaseofbTBhasbeenwell documentedintheUKandIreland,wheretherisingincidenceof theinfectioninthelast20yearshascausedsignificanteconomic losses(Abernethyetal.,2013;Reynolds,2006).Thedirectcosts ofbTBforUKtaxpayersin2009wereestimatedinaround

£

63M (about95MUSD)andover25,000cattlewereculled(Johnstonetal., 2011).Outdoorfarming,acommonbreedingsystemintheUKand Ireland,favourscontactsbetweenanimalsofdifferentherds,one ofthepathwaysforbTBtransmission.Inaddition,thisfarming sys-temexposescattletothepossiblyinfected wildlife,suchasthe Europeanbadger(Melesmeles)whosepopulationisendemically infectedbyM.bovisinlargeareasoftheBritishisles(Cheeseman etal.,1989;Grifﬁnetal.,2005;Morrisetal.,1994).Anotherfactor associatedwithrecurrent bTBoutbreaksintheUK is between-farmmovementofliveanimals(Johnstonetal.,2011;Reillyand Courtenay,2007).Therefore,preventingcontactsbetweencattle of different herds and between cattle and wildlife are consid-eredeffectivemeasurestocontrolbTBriskintheUKandIreland (Johnstonetal.,2011;Phillipsetal.,2003;ReillyandCourtenay, 2007).

Incountrieswhereindoorfarmingisthemostcommonbreeding practice,suchasItaly,bTBtransmissionrarelyoccursthrough con-tactswithinfectedwildlife.AstudyonbTBriskfactorsinNorthern ItalyshowedthatthemainriskfactorforbTBbreakdownwas cat-tlemovementbetweenfarms(Marangonetal.,1998).bTBisstill endemic,thoughatverylowprevalence,insomeItalianregions whileithasbeeneradicatedinothers.

Amongthelatter,Emilia-Romagna(ER),locatedontheright sideoftheriverPovalley(NorthernItaly),hasbeendeclared ofﬁ-ciallyfreefrombovinetuberculosis(UEDecisionn◦2007/174/CE). ThisRegionhasanimportantfood-farmingindustrycharacterized byan intensivedairy production, and istheregionof originof Parmigiano-Reggianocheese.Accordingtothelatestofﬁcial statis-tics(ItalianNationalStatisticsInstitute,2010),about550,000cattle arerearedin7343farmsinER.AsanepidemicofbTBinthisRegion wouldhave severeeconomic consequences, theregionalhealth authorities have implemented, in compliance with EU regula-tions(UEDecision2002/677/CE),anintegratedsurveillancesystem topreventbTB re-emergencein dairy cattle (Regional decision GPG/2010/1049).Thesystemisbasedonthreedetectionmethods, namely:(i)periodicroutineskin-testonallanimalsagedover24 months(RS);(ii)systematicskin-testonallexchangedcattle(ECT) atthedestinationfarm;and(iii)inspectionforbTBlesionsofall slaughteredanimals(SI).WhileERRegionhasmanagedtoremain bTB-freesofar,theeffectivenessoftheintegratedsurveillance sys-tem(anditsindividualcomponents)inthecaseofre-introduction oftheinfectionhasneverbeenassessed.

Thegoalofthisworkistoquantitativelyassesstheperformance ofthecurrentsurveillancesystemanditsindividualcomponentsin termsof(i)timeneededtodetectaprospectivenewlyintroduced bTBepidemicinthedairyfarmsofER,and(ii)themagnitudeofthe epidemic,intermsofnumberoffarmsinfectedbeforethedetection oftheepidemic.

SinceERhasbeenfreefrombTBformanyyears,noempirical dataontheepidemiologicaldynamicsoftheinfectionisavailable. Therefore,inordertotesttheperformanceofthethree surveil-lancemethodscurrentlyinplaceintheRegion,wedevelopedan individual-basedepidemicmodelcapableofsimulatingthespread

Dairy farms size (#individuals)

Frequency 0 500 1000 1500 0 250 500 750 1000 1250 1500 1750 2000

a)

Sample farms size (#individuals)

Frequency 05 0 150 250 0 500 1000 1500 2000

b)

Fig.1. DistributionofEmilia-Romagnadairyfarmssize.Everybarcorrespondstoa sizeintervalof50individuals(0–50,51–100,etc.).(a)Alldairyfarmssize distribu-tion(Min=1,Median=63,Mean=101.9,Max=1889);(b)sampleddairyfarmssize distribution(Min=1,Median=74,Mean=126.6,Max=1889).

ofbTBintheregionalnetworkofdairyfarmsfollowingtherandom introductionofinfectedanimalsinthesystem.

Considering that therearing systemin placein ERand that thecomponentsof bTBsurveillancedescribedabovearelargely widespreadin severaldeveloped countries, ourﬁndingscan be applicabletomanyfarmingandsurveillancesystemsworldwide.

2. Materialsandmethods

Toassesstheperformanceofthecurrentsurveillancesystem andeachofitscomponents,webuiltadata-driven,stochastic net-workmodelthatsimulatesbothwithin-farmandbetween-farm bTBdynamics.Weassumedthatthecontributionoftransmission routesotherthancattleexchanges–suchasairbornetransmission ortransmissionmediatedbywildlifeorfomites–wasnegligible withintheregionalsystemof industrialindoorfarms.Thus,we consideredcattleexchangesastheonlytransmissionrouteforbTB amongdairyfarms.

Since bTB is a chronic diseasecharacterized by slow trans-missiondynamics (see Agustoet al., 2011; Huang et al.,2013; Brooks-Pollocketal.,2014)theassumptionofendemicequilibrium withinagivenfarmisunrealistic.Then,weexplicitlyaccountedfor within-farmdiseasedynamicsdescribinghow epidemicsevolve insidefarmsfollowingtheintroductionofinfectedanimals.

WerepresentedthesystemofcattleexchangesofERasa con-tactnetworkwherenodesrepresentfarmsanddirectionaledges representbetween-farmanimalmovements.Wereproducedthe network of4353dairy farmsand 20 intermediarytrader farms (ITF)byusinginformationfromthecattlemovementdatasetas describedhereafter.

Allstatisticalanalysesandmodelsimulationswereperformed usingthesoftwareRwith“MASS”,“triangle”and“poilog”packages (http://www.r-project.com).

2.1. Cattletrademovementdata

Cattle movementdatawereprovidedbytheItalian National Bovinedatabase.Attheendof2010,4353dairyfarmsand20 inter-mediarytraderfarms(ITF)wereinoperationinER.Forallofthem wecollectedthefarmsize,i.e.numberofanimalsperfarm(Fig.1). ForalltheITFandasubsetof837dairyfarmswealsocollected individualrecordsofeverycattlemovement,incomingand outgo-ing,overa100-weektimespan(fromthebeginningofFebruary

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Table1

Detaileddataonsampleddairyfarmsandintermediarytraderfarms(ITF).

Cattlemovementdairyfarmsdata(February-2009/December-2010) CattlemovementITFdata(February-2009/December-2010) TotaldairyfarmsinEmilia-Romagna 4353 TotalITFinEmilia-Romagna 20

Samplesdairyfarms 837 SampleITF 20

Sampleindividualrecords 106,499 Sampleindividualrecords 3571

Incomingindividuals Incomingindividuals

Total 9173 Total 3571

F1romERdairyfarms 7036 FromERdairyfarms 1774

Fromotherregions/countries 2137 Fromotherregions/countries 1797

Outgoingindividuals Outgoingindividuals

Total 28,954 Total 3476

Totaltofarms 6328 Totaltofarms 3211

TootherERdairyfarms 5597 TootherERdairyfarms 2136

Tootherregions/countries 731 Tootherregions/countries 1075

Totaltoslaughterhouse 21,851 Totaltoslaughterhouse 230

ToERslaughterhouse 10,195 ToERslaughterhouse 147

Tootherregionsslaughterhouse 11,656 Tootherregionsslaughterhouse 83

Death/otherdestinations 775 Death/otherdestinations 35

Samplefarmsmeanin-degree 1.91(SE±0.096) ITFmeanin-degree 24.50(SE±7.185) Samplefarmsmeanout-degree 1.67(SE±0.075) ITFmeanout-degree 19.25(SE±5.537)

2009totheendofDecember2010).Thefarmsweresampled strat-ifiedbyprovinceandtheobtainedsampleisrepresentativeofthe farmssizedistributionobservedintheregion(Fig.1and Supple-mentarymaterialsS1.1fordetails).Eachindividualrecordhada uniqueidentifiercodefortheanimal,thebirthdate,sexandrace oftheanimal,identifiercodesforthefarmsoforiginand desti-nation,codesforfarmsproductionsector(beef,dairyormixed), andthemovementdate.Weconsideredthemovementsbetween dairyfarmsonly,andweexcludedthemovementsofyoungcalves orend-of-lifecattlesenttobeeffarmsordirectlytothe slaughter-house.Thefinaldatasetwascomposedof15,501individualrecords reportinganimalmovement(Table1).

ITFactivityconsistsintradingcattle,notinrearingthem.Asa consequence,animalsstayinITFforjustafewdays(median2days, lower[upper]quartile1[8]days)beforebeingmovedtoa desti-nationdairyfarm,amuchshortertimecomparedtothetimethat individualsspendindairyfarms(1815days,onaverage).In addi-tion,thenumberofanimalssimultaneouslyheldinITFsisusually verylow(median0,lower[upper]quartile0[5]animals).Given thelimitednumberofanimalsandtheshorttimethatindividuals spendinsidethem,weassumedthatthebTBtransmissioninside ITFswasnegligiblecomparedtodairyfarms(asensitivityanalysis ontheeffectofthisassumptiononbTBdynamicsandthe effec-tivenessofsurveillanceisshownintheSupplementarymaterials, SectionS3.4).

Wedeﬁnedthein-degreeandtheout-degreeforfarmiasthe numberoffarmsfromwhichfarmireceivesandthenumberof farmstowhichfarmisendscattle,respectively.Altogether,thejoint setsofin-andout-degreesarereferredtoasthein-andout-degree distributionsofthenetworkofdairyfarms.Sincemovementdata wereavailableforallITFsinoperationinER,wedirectlyderived thein-andout-degreeofeachITFinthenetworkaswellastheir distributions.

RegardingfarmsotherthanITFs,movementdatawere avail-ableonlyfor asubsetof837farmsoutofthetotal4353inthe ERnetwork.Thus,wederivedthein-andout-degreedistributions ofthissubset.Then,weestimatedtheparametersofasetof can-didateprobabilitydistributions(namely:power-law,log-normal, PoissonandYule)ontheavailabledataandselectedthe distribu-tionthatbestﬁttedthedatabyusinglog-likelihoodratio(LLR)test asshowninVuong(1989)andClausetetal.(2009).Finally,we usedtheselectedprobabilitydistributiontogenerateasynthetic networkof4353farmshavingthesametopologicalproperties(i.e. thein-andout-degreedistributions)observedinthesubsetof837 dairyfarms.Theprocedurefornetworkgenerationisdescribedin thefollowingsection.

2.2. Networkmodel

Togenerateanetworkof4353farmswiththesame topologi-calpropertiesobservedinthesubsetof837dairyfarms,weﬁrst assigned in- and out-degreesindependentof each othertothe 4353nodesinthenetwork(asin-andout-degreeswereweakly correlated,seeSupplementarymaterialsS1.3).Moreover,asboth in-degreesandout-degreeswereveryweaklycorrelatedwithfarm sizeintheobserveddataset,theywereassignedtonodes irrespec-tiveoffarm size(seeSupplementarymaterialsS1.3). According totheavailablemovementdata,weassumedthatneitherthe in-degreenortheout-degreecouldbelargerthanthenodesize,i.e. thenumberofanimalsinthefarm.

Onceweassignedtoeachnodeanin-degreeandanout-degree, weusedaheuristicalgorithmtoexactlymatchthenumberof out-goingconnectionsfromfarmsinthenetworktothatofingoing connectionstofarmsinthesamenetwork(Supplementary mate-rialsS2).Then,wecompletedthenetworkbyadding20additional nodesrepresentingthe20ITFandassignedtoeachofthemthe observedin-andout-degrees.

2.3. Within-farmmodel

Wedescribedthewithin-farmbTBdynamicsthrougha com-partmentalstochasticSusceptible–Exposed–Infected(SEI)model with homogeneous frequency dependent transmission (Agusto etal.,2011;Brooks-Pollocketal.,2014).Thecompartmentofthe susceptible individuals (S) representscattlethat have notbeen infectedyet.Followingsuccessfulinfection,eachindividualis clas-siﬁedasexposed(E)butnotinfectiousyet.Theincubationperiod endswhentheinfectedindividualbecomesalsoinfectious(I).Then, theinfectiondynamicsforfarmicanberepresentedbythe follow-ingsystemofordinarydifferentialequations:

⎧

⎪

⎨

⎪

⎩

˙Ei=ˇi_NSiIi i −(+)Ei−

j/=i ijEi+

j/=i jiEj ˙Ii=Ei−Ii−

j/=i ijIi+

j/=i jiIj (1)

wherefarmsizeNi,i.e.thenumberofcattleinfarmi,wasassumed

constantintime,andthenumberofsusceptibleanimalsinfarm iwascomputedasSi=Ni−Ei−Ii;ˇirepresentsthewithin-farm

transmissionrate;therateatwhichexposedindividualsbecome infectious;ijthemovementrateofindividualsfromfarmitofarm

j,whichisproportionaltofarmiout-degree;andtherateatwhich individualsleavethefarmsystem,eitherbecausetheyaresentto

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Table2

Thelistandratesofthepossibleeventsforthestochasticmodel.

Eventinfarmi Transition Rateatwhich eventoccurs Infection Ei→Ei+1 ˇSiIi/Ni Exposedindividualbecome

Infectious

Ei→Ei−1,Ii→Ii+1 Ei Exposedindividualissent

tofarmj

Ei→Ei−1,Ej→Ej+1 iEi Exposedindividualleaves

thesystem

Ei→Ei−1 Ei Infectiousindividualsent

tofarmj

Ii→Ii-1,Ij→Ij+1 iIi Infectiousindividualleaves

thesystem

Ii→Ii−1 Ii

slaughterhouse,tofarmsoutsidetheERsystem,orbecausethey die.

Toexplicitlyaccountforthestochasticnatureofthecontactand transmissionprocessesinalargenetworkoffarms,weran stochas-ticsimulationsthroughanevent-drivenapproachinwhichtime stepsbetweentwoconsecutiveeventsweredescribedthrougha Poissonprocesswithexponentiallydistributedmeans(Gillespie, 1977;Rohanietal.,2002).Alltheeventssimulatedbythestochastic infectionmodelarereportedinTable2.

2.4. Parameterestimation

The basic reproduction number represents the mean num-berofsecondaryinfectionscausedbyasingleinfectedindividual introducedintoacompletelysusceptible population(Diekmann etal.,1990).AsERisanofﬁciallybTB-freeregionsince7years,there arenoavailableoutbreaksdatatoestimateawithin-farmbasic reproductionnumber,R0.Thus,wesetR0valueinoursimulations

usingestimatesforbTBfoundintheliterature(Brooks-Pollockand Keeling,2009;Agustoetal.,2011;Conlanetal.,2012;O’Hareetal., 2014).Inparticular,wedescribedtheuncertaintyinR0by

assum-ingatriangulardistributionwithmode2.40(whichcorresponds tothemeanvalueofliteratureestimates),lowerlimit0.83,and upperlimit4.9(whichcorrespondtotheextremevaluesfound), seeSupplementarymaterialsS3.1for details.Infurther simula-tionswetestedtheeffectofR0variability,comparingtheobtained

resultsagainstthoseofsimulationsranusingaﬁxedR0value(see

SupplementarymaterialsS3.2).

Moreover,inordertotesttherobustnessofourresultsagainst possiblevariationsinthewithin-farmbTBbasicreproduction num-ber,werunfurthersimulationsusingasﬁxedvaluethehighest (R0,i 4.9)andthelowest(R0,i 0.83)oftheliteratureestimates

(seeSupplementarymaterialsS3.3).

Theithfarmtransmissionrate,ˇi,wasindirectlyderivedfrom

theexpressionforthewithin-farmbasicreproductionnumberof model(1),i.e.assuminginfectioninithfarmonly,byusingNext GenerationMatrixtechniques(Diekmannetal.,1990):

ˇi=R0

(

_j_/=_i_ij₊₎₍

_j_/=_i_ij₊₊₎

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FollowingBarlowetal.(1997),wesetthemeantime(=1/) spentintheexposedclassEto202days.Likewise,parametersand mean[ij]wereestimatedfrommovementdataastheinverseofthe

meantimethatanimalsspentinsidethefarmbeforebeingmoved outofthesystemandtowardsotherERdairyfarms,respectively.

ITFsarecharacterizedbysubstantialdifferentbehaviourswith respecttodairyfarms.AsweassumedthatthereisnobTB trans-missioninsidethesefarms,wesetˇITF=0andweestimatedITF

speciﬁcmovement(ITF)andexit(ITF)ratesfromavailabledata.

2.5. Assessmentofthesurveillancesystem

WeusedthestochasticmodeldescribedinTable2toassessthe effectivenessofthethreesurveillancecomponentsofthecurrent surveillancesystemimplementedinERRegion.Speciﬁcally:(i)a systematiccontroloneveryfarmbasedonatuberculinskin-tests (RS).Thisisperformedoneveryindividualinthefarmolderthan24 months,whichrepresentsabout60%ofthetotalfarmpopulation. FollowingcurrentlegislationforbTB-freeareasinERallcattleherds aretestedwithaturnaroundperiodofthreeyears;(ii)askin-test performedatthedestinationfarmonmovedcattlewithin45days afterthearrival(ECT);(iii)avisualinspectionforbTBcharacteristic lesionsoncattleslaughtered(SI).

TosimulatetheRSweassumedthat,withinaturnaroundperiod, farmsaretestedinarandomorder(withafrequencyofabout4 farmsaday).Tosimulatetheothersurveillancecomponents,we assumedtotestallindividualsmovedbetweendairyfarmsand toallindividualssenttoslaughterhouses.Whenananimaltests positivetothetuberculinskin-test,bothforRSandECTcases,the procedurestoverifywhethertheanimalistrulyinfectedbyM.bovis lastusually2months.Theseproceduresconsistinacarcass inspec-tionforbTBtypicallesionsandinabacteriologicalexamination. Duringthisconﬁrmationperiod,cattlefromthesamefarmcannot betransferredtootherfarms.Thisprotocolwassimulatedinthe modelbyassumingthat,whenapositiveindividualisdetectedby theskin-test,thefarmcannothaveanycontactwithotherfarms, butthesimulationofdiseasedynamicslastsforother2months.

Theskin-testcurrentlyinuseinItalyisthecervicalSingle Intra-dermalTest(SIT).Intheirreview,delaRua-Domenechetal.(2006) showed that theSIT sensitivity estimates fallbetween 0.7 and 1.However,on-farmtestsensitivitycanbesubstantiallysmaller than in controlledlaboratoryexperiment trials asother factors than theimperfect natureof thetest may affecttest outcome, suchasthetraininglevel,careand experienceofthe veterinar-ianandtheactualprotocolused.Therefore,followingWelbyetal. (2012),wesimulatedtheuncertaintyintestsensitivitiesby ran-domlydrawingsensitivity values froma beta distributionwith mean0.60andcoefﬁcientofvariation0.10.AnalogouslytoBarlow etal.(1997),weassumedareductionoftestsensitivityininfective butnotinfectiousanimals(meanvalue0.54).Sensitivityof post-morteminspectionattheslaughterhousescanalsobeveryvariable (Assegedetal.,2004;Fischeretal.,2005;Smithetal.,2013;van Asseldonketal.,2005;Welbyetal.,2012).Therefore,similarlyto Welbyetal.(2012),wesimulatedtheuncertaintyinpost-mortem inspectionasabetadistributionwithmean0.60andcoefﬁcientof variation0.10(withareductionofsensitivityforexposedanimals asintuberculinskin-test).

In addition, with the intent to simulate different levels of accuracyandawarenessinthetestingprocedures,werana sen-sitivityanalysistoassesstheeffectofpost-morteminspectionand tuberculinskin-testsensitivityvaluesonsurveillancesystem per-formance (Hadorn and Stärk, 2008; Humblet et al., 2011).The resultsareshowninSupplementarymaterialsS4.

Diseasedynamicsfollowingtheintroductionofaninfected indi-vidualinthenetworkofdairyfarmsofERwereassessedunder ﬁvescenarios.Firstly,wesimulatedabaselinescenarioofbTB epi-demicsspreading without surveillanceso astounderstand the potentialimpactsofuncontrolledbTBepidemicsonthesystem. Secondly,weransimulationsontheintegratedsurveillance sys-temcurrentlyinplace,whichincludesallthethreesurveillance components(Scenario1).Then,inordertoassesstheperformances ofeachcomponentofthesurveillancesystem,weanalyzedthree alternativescenariosinwhichweremovedoneofthethree com-ponentsatatimeandsimulatedepidemicdynamicswiththeother twosystemsinplace.InScenario2a,Scenario2bandScenario2c weremovedRS,ECTandSIcomponents,respectively(Fig.2).

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Fig.2.Differentsurveillancesystemscenariossimulated.

Foreachscenarioweran10thousandstochasticMonteCarlo simulationsof diseasedynamicsuntil infectiondetectionor up to10 years at most.For each simulation we generated a net-workof4535dairyfarmsasdescribedaboveandassumed that aninfectedbutnotinfectiveindividualwasrandomlyintroduced inthenetwork.Tosimulatevariabletestsensitivity,thevaluesof skintestandpost-morteminspectionsensitivityweredrawnfrom therespectivebetadistributionseachtimeatestwasperformed. Then,the10thousandreplicateswereusedtoderiveanumberof statisticsondiseasedynamicsandontheperformanceofthe spe-ciﬁcsurveillancesystemunderassessment.Inparticular,foreach scenariowederived:(i)themeanand95thpercentileofthetime foranepidemictobedetected(whensurveillanceisinplace);(ii) themeanand95thpercentileofthenumberofinfectedfarms;(iii) thenumberofepidemicsthatinvolvedmorethanonefarm(i.e. multi-farmsepidemics);(iv)thenumberofundetectedepidemics after10years;and(v)thenumberofepidemicsthatwentnaturally extinctinlessthan10years.

3. Results

3.1. Farmsize,in-andout-degreedistributions,andparameters estimations

Thesizeof the4353ERcattlefarmswasquitevariable and rangedfromjustfewindividualstoalmosttwothousands:farm sizedistributionwashighlyskewedwithmeanequalto102and median63(Fig.1).

Theaveragenumberofoutgoing[ingoing]linksinthe837 sam-pledfarmswas1.67[1.91],themedian1[1]andthemaximum number23[29].Thein-degreeandout-degreedistributionswere not signiﬁcantly different (Kolgomorov-Smirnov test, D=0.044, p=0.387)andwerebestﬁttedbyadiscretelog-normal distribu-tion(Fig.3andTable3).In-degreeandout-degreewereweakly correlatedwithfarmsize(Kendall’s=−0.028and0.20, respec-tively). The correlation between in- and out-degrees was also very weak(Kendall’s =−0.064). Therefore, for each of the 10 thousandrunswe generateda networkof 4353farmsby inde-pendently drawingfarms’ in-degree and out-degree fromtheir corresponding distributions, assuming no correlation between

1 2 5 10 20 12 5 1 0 2 0 5 0 100 200

log(sampled farms degree)

log(frequency)

Data in−degree Data out−degree Lognormal fit Power−law fit Poisson fit Yule fit

Fig.3.Pointsrepresentstheobservedin-(circles)andout-degree(triangles) dis-tributionsofthesampleddairyfarms.Linesrepresenttheﬁttingfordifferent distributionstotheempiricaldata:discretelog-normal(solidline),Yule(dashed line),discretepower-law(dottedline)andPoisson(dashed-dottedline).

1 2 5 10 20 50 100 1 5 50 500 5 000

log(outbreaks size

)

log(counts)

Fig.4. Thedistribution(inlog–logscale)ofmaximumnumberofinfectedfarms (outbreaksize)ineachsimulatedoutbreak.

them.Theresultingnetworkswereweaklyconnected,withaverage densityof5.2×10−4(SD±8.7×10−6).Thevalueofparameters andmean[ij]estimatedfrommovementdatawas0.240y−1(S.E.

±0.044)and0.043y−1(S.E.±0.008),respectively,fordairyfarms and,19.981y−1(S.E.±1.499)and15.901y−1(S.E.±1.192)forITFs.

3.2. Thebaselinescenario:nosurveillance

In theabsenceof anysurveillancesystem,about halfofthe epidemicslastedfor10yearsormore(56.05%),whiletheothers (43.95%)naturallyfadedoutin amedian timeof14.92months (lower[upper]quartile5.95[32.89]months).Epidemicsizewas highlyskewed(Fig.4):themeannumberofinfectedfarmswas 2.91(S.E.±0.05),but62.66%oftheepidemicsinvolvedonlyasingle farm.Intheworst5%ofthecasesmorethan11farmsgotinfected, withamaximumof89.

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Table3

TheresultoftheVuongtestonthedegreedatafordiscretelog-normaldistributionvs.discretepower-law,PoissonandYuledistributions.

Discretelog-normaldistribution loglike.ratio mean.LLR sd.LLR Vuong p-Value

vs.Powerlaw 158.903 0.151 0.647 7.57 3.6e−14

vs.Poisson 314.182 0.299 1.735 5.58 2.4e−08

vs.Yule 107.584 0.102 0.583 5.69 1.3e−08

Table4

Themainindicatorforthesurveillancesystemperformances.Resultsfor5differentsurveillancescenarios:nosurveillance,1:currentsurveillance,2a:RSremoved,2b:ECT, 2c:SIremoved.

Scenario Mean(and95p)timeto detection(months)

Mean(and95p)numberof infectedfarms %ofmulti-farm epidemics %ofepidemicover10 years %ofepidemic extinct Nosurveillance – 2.91(11) 37.34% 56.05% 43.95% Scenario1 27.04(59.20) 1.12(2) 10.15% 0.10% 14.55% Scenario2a 33.77(76.91) 1.20(2) 16.80% 0.70% 16.23% Scenario2b 27.52(60.09) 1.11(2) 9.61% 0.14% 14.80% Scenario2c 35.45(71.34) 1.15(2) 12.01% 0.18% 33.57%

3.3. Performanceofthecurrentsurveillancesystem

Fig.5showsthecumulateddistributionofthetimeofepidemic detectioncomputedasthefractionofepidemicsmonthlydetected bythecurrentsurveillancesystem.Themeandetectiontimewas 27.04months(S.E.±0.19). Epidemicswereidentified more fre-quentlybyroutinesurveillance,RS,andbypost-morteminspection attheslaughterhouse,SI.In10years,RSidentified41.66%ofthe epidemicsand SIthe38.59%.Only5.10%oftheepidemicswere firstdetectedbyECT,i.e.intra-dermaltestingofexchanged cat-tle;14.55%oftheepidemicsfadedoutunreportedwhileonly0.1% oftheepidemicslasted10yearsormore.Theaveragenumberof infectedfarmsduringtheepidemicswas1.12(S.E.±0.003);the worst5%oftheepidemicslastedmorethan59.20monthsaffecting from2to5farmsatmost.Multi-farmepidemicsoccurredin10.15% ofthereplicates.Theperformancesofthecurrentsurveillanceand thealternativescenarios describedhereafteraresummarizedin Table4.

3.4. Performanceofalternativesurveillancesystems

Fig.6showsthecumulativefractionofmonthlydetected epi-demicsregisteredforasurveillancesystemimplementingonlytwo

0 20 40 60 80 100 120 0.0 0 .2 0.4 0 .6 0.8 1 .0 Time (months) Cumulativ e fraction of epidemics Total detected RS ECT SI 1 − extinct

Fig.5.Thecumulatedfractionofsimulatedepidemicsmonthlydetectedbythe cur-rentsurveillancesystem.Differentcolourscorrespondtothefractionofepidemics detectedbyeachsurveillancestrategy:inlightgreybyslaughterhouseinspection (SI),indarkgreybyroutinesurveillance(RS)andinblackbyexchangedcattletesting (ECT).Thesolidlinerepresentsthefractionoftotalofdetectedepidemicswhilethe dashedlinerepresentsthefractionofepidemicsnotnaturallyfadedout,orextinct, atanygiventime.

ofthethreesurveillancemethodscurrentlyinplace.Ouranalysis showed that removing RS testing from the integrated surveil-lance system (Scenario 2a) ledto an almost 7-monthdelay in thedetectiontimedrivenbyasigniﬁcantreductioninthe num-berofepidemicsdetectedstartingfromthesecondyear(Fig.6). Comparedwiththecurrent integrated surveillancesystem,this scenariowascharacterizedbyasmallincreaseinthemean num-berofinfectedfarms,inthetotalnumberofunreportedepidemics lasting10yearsormore(from0.10%to0.70%),andinthenumber ofunreportedepidemicsnaturallyfadingoutbeforetheendofthe simulationtime(from14.55%to16.23%).Intheworst5%ofthe cases,theepidemicsremainedundetectedforabout6andahalf years(76.91months)andinvolved2–5farms.

Removing the ECT (Scenario 2b) did not cause signiﬁcant changesinanyoftheperformanceindicators.Intheworst5%ofthe casesthetimetodetectionwasabout60monthsandthenumber ofinfectedfarmsbetween2and7.

TheremovalofSI(Scenario2c)ledtoamorethan8months increaseinthemeandetectiontimemostlydrivenbyasignificant reductioninthedetectionratesincethefirstmonths.Thenumber ofundetectedepidemicsstillongoingafter10yearsdidnot signifi-cantlychange,whilethefractionofmulti-farmepidemicsincreased from10.15%inScenario1to12.01%.Thelargestchangein sce-nario2cwasrepresentedbythenumberofunreportedepidemics thatnaturallyfadedout,increasedfrom14.55%intheScenario1

0 20 40 60 80 100 120 0.0 0 .2 0.4 0 .6 0. 1 8 .0 Time (months) Cumulativ e

fraction of detected epidemics

Scenario 1 Scenario 2a Scenario 2b Scenario 2c

Fig.6.Thecumulatedfractionofsimulatedepidemicsmonthlydetectedbythe surveillancesysteminScenario1(solidline);Scenario2a(onlyECTandSI compo-nents,dottedline);Scenario2b(onlyRSandSIcomponents,dashed-dottedline); andScenario2c(onlyRSandECTcomponents,dashedline).

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tothe33.57% inScenario2c,thatexplainsthelowernumberof totallydetectedepidemics(Fig.6).Intheworst5%ofthecases,the epidemicsremainedundetectedfor71monthsandinvolved2–7 farms.

4. Discussion

Thegoalofthis studywastoassesstheeffectiveness ofthe currentbTBsurveillancesysteminEmilia-Romagna(ER)andthe speciﬁcroleplayedbyitsthreecomponents:on-farmroutine test-ing,exchangedanimaltesting,andslaughterhouseinspection.To assesstheperformanceofthebTBsurveillancesystemwe com-putedtwomainindicators:thetimeneededtodetectaprospective newlyintroducedbTBepidemicinthedairyfarmsofER;andthe magnitudeoftheepidemic,intermsofnumberoffarmsinfected beforethedetectionoftheepidemic.Thetimetoepidemic detec-tionisaveryimportantmeasureofthesurveillanceperformance (HadornandStärk,2008);ﬁrstly,becauseanearlydetectionofthe epidemiclimitsthenumberofbothanimalsandfarmsinfected; secondly,becauseaccordingtoItalianandEuropeanlegislationsat least6monthswithoutbTBcasesarerequiredforfarmstoregain theirdisease-freestatus.Indeed,thetraderestrictionsfollowing anepidemicrepresentoneoftheworstconsequencesforthe cat-tleindustry(Gordon,2008).Themagnitudeoftheepidemicallows ustoassessthedamagescausedbyabTBincursionquantifying thenumber ofinfected farmsand, consequently,thetotal eco-nomicburden (i.e.lossofearnings,costsforanimal cullingand re-stocking).

Ouranalysisshowedthat theintegratedsurveillancesystem currentlyinplaceinERcaneffectivelyreducethenumberoffarms involvedina potentialepidemicofbTBwithrespecttoa base-linescenariowithnosurveillance(seeTable4).Themodelpredicts thataboutthe15%oftheepidemicscannotbedetectedbythe sys-tem.However,theundetectedepidemicsusuallyfadedoutduring theﬁrstninemonthsaftertheintroductionoftheinfectionand generallyinvolvedoneorfewindividualsinasinglefarm.

Numericalsimulationsalsoshowedthatpost-morteminspection attheslaughterhouse(SI)andon-farmroutinetesting(RS)werethe mosteffectivesurveillancemethods.Thesemethodswere signifi-cantlymoreeffectiveindetectingbTBintheERnetworkofdairy farmsthantestingcattleuponmovingtoanewfarm(ECT). Consid-eringthatthecattleexchangesweretheonlyhypothesizedroute ofbetween-farmtransmissioninthemodel,thisresultseems,at firstsight,unexpected.However,itcanbeexplainedbythelow bTBtransmissionrateandsomespecificproprietiesoftheERcattle system(suchasthelownetworkdensityandthelownumberof cattletraded).

Slaughterhouseinspectionturnedouttobeparticularly effec-tiveasanearlydetectiontooloftheepidemics.Thisseemedtobe duetothefairlyregularflowofcattlesentouttoslaughterhousesat theendoftheirproductivecycle,onaverageat5yearsofage. There-fore,whenanoutbreakoccurredinafarm,SIwaslikelytheearliest diagnosticinterventionabletodetectitcomparedtoECTandRS. However,thelargenumberofepidemicsthatnaturallyfadedout whenSIwasabsent(Scenario2c)pointsoutthatmostofthe out-breaksthatremainedundetectedattheendofthefirstyearwere likelytogetextinctafterwardswithoutdevelopingintosignificant epidemics.

On-farmroutine surveillancewas themosteffective in pre-ventingmajor,ormulti-farm,outbreaks.Thiswasprobablydue tothelow valueofwithin-farmbTBbasicreproductionnumber (R0).Indeed,alowwithin-farmR0 isfollowedbyalow number

ofinfectedindividualsinsidefarms,thatmeansalownumberof infectedindividualsexchangedorsenttoslaughterhouses.Inthese epidemiologicalconditions,RSwasthemosteffectivemethodto

detectoutbreaksbeforebTBspreadstootherfarms.Moreover,this methodwasalsothemosteffectiveindetectingthesmallportion ofminorepidemicsthatcanpersistunnoticedformanyyears.

TheperformancesofSIandRS,bothwithregardtotimetobTB detectionandtofractionofmulti-farmepidemics,were compara-bleandwhichoneperformedbetterdependedonthevalueofthe basicreproductionnumber(R0),seeSupplementaryMaterialsS3.3,

andonthevaluesofskin-testandSIsensitivity,seeSupplementary materialsS4.2.

AstheR0 valueincreased,thenumberofdetectedepidemics

bytheSItendedtoincreasecomparedtothenumberofepidemics detectedbyRS(seeSupplementarymaterialsS3.3).Thisresultcan beexplainedconsideringthatalargerwithin-farmdisease preva-lence,asaconsequenceofalargerwithin-farmR0,impliesahigher

probabilityofsendinginfectedindividualstotheslaughterhouse intheearlystagesofepidemics,speedingupbTBdetection,while thetimingofRSisnotaffectedbyfarmprevalence.

Thevaluesofskin-testandcarcassinspectionsensitivityplayed animportantroleaswell.Thiswasparticularlysigniﬁcantinthe caseofslaughterhouseinspection,sinceaverylargerangeofvalues ofinspectionsensitivityhasbeenestimatedindifferentcontexts, from0.1to0.65(Assegedetal.,2004;Fischeretal.,2005;Smith etal.,2013;vanAsseldonketal.,2005;Welbyetal.,2012).

TheRSturnaroundperiod,i.e.thetimeframeinwhichallfarms aretestedonceforbTB,wassetto3years,ascurrentlyisinER region.Theregionaladministrationdecidedtomovethisperiod from2to3yearsin2010.Ourresultssuggestedthatthischange hada negligible impactontheoverall surveillancesystem per-formanceand, moreover,thatmoving itto4 yearswould have marginalimpactaswell(SupplementarymaterialsS5).

ThelimitedperformanceimprovementprovidedbyECTraised questions about its usefulness and whether it could be safely droppedoutfromERintegratedsurveillancesystemwithout sig-niﬁcantlyweakeningit.Ofcourse,thiskindofdecisionshouldalso bebasedoncost-beneﬁtanalysis.Ontheotherhand,theroleof ECTonanimalsenteringabTB-freeregionisnotnegligible, espe-ciallyforcattleoriginatingfromregionswherebTBisendemicand ifpre-movementtesting-incontrasttopost-movementtesting– isadoptedtopreventtheriskofdiseaseintroduction(Cleggetal., 2008;Schilleretal.,2011).

Inagreementwithourresults,Shittuetal.(2013)showedthat SIplaysanimportantroleinbTBdetectionintheUK,bothinlow incidenceandendemicareas,butishighlyinfluencedby inspec-tionsensitivity.AnotherstudyconductedinBelgiumbyWelbyetal. (2012)showedthatSIisoneofthemosteffectivesurveillance strat-egyforbTB.However,furtheranalysesperformedwithourmodel showedthattheuseoftheSIcomponentonlydetermineda signif-icantreductioninthesurveillancesystemperformancecompared toasurveillancebasedonSIcoupledwithroutineskin-testing(see SupplementarymaterialsS6).Thisresultisinagreementwith find-ingsbyFischeretal.(2005)andvanAsseldonketal.(2005),who showedthatSIsurveillancealoneisineffectiveindetectingbTBin TheNetherlands.ThiswasconfirmedbySchöningetal.(2013)on UScattleherds,whoshowedhowSIneededtogoalongwiththe skin-testcomponentinordertoprovideaneffectivesurveillance system.

Inthisstudy,cattlemovementdatawereavailableforasubsetof thenetworkofdairyfarmsinER.Thesedatawereusedtoinferthe fundamentaltopologicalpropertiesofthewholesysteminterms ofconnectivity,soastogeneratesyntheticnetworksofthesame sizeoftheERone.Whileweareconﬁdentthatourapproach pro-videsasolidpreliminaryunderstandingofbTBdynamicsonthe networkofdairyfarmsinER,wedidnothavethepossibilityto derivemoresophisticatedmetrics,suchastheclustering coefﬁ-cientandtheDiseaseFlowCentrality,thatwouldhelptohavemore detailedpredictiononhowdiseasesspreadthroughthenetwork,

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asshownbyBajardietal.(2011)andNataleetal.(2011).We simu-lateddiseasedynamicsonastaticnetwork,ignoringseasonalityin cattlemovementandthedynamicalnatureofcattletrade. Dynam-icalnetworkscaneffectivelyrepresentthetime-varyingstructure ofthenetwork–whichiscrucialtosimulatethedynamicsofacute andhighlycontagiousdiseases,suchasFood-and-MouthDisease (Bajardietal.,2011,2012;VernonandKeeling,2009).However, sincebTBischaracterizedbyslowinfectiondynamicsanditisable toremainunnoticedforalongperiodoftime,webelievethatitis notessentialtotrackthesmallstructuralchangesoccurringona shorttimescaleforsuitablydescribebTBdynamics.

Otherstudieswerepreviouslyconductedoncattlemovements inItalybyNataleetal.(2009,2011).Inparticular,theyfoundthat thedegreedistributionofcattlemovementwasbestapproximated byafat-tailedpower-lawprobabilitydistribution.Ourdegree dis-tributionsbestﬁttingwasobtainedthroughadiscretelog-normal distribution,characterizedbylowerright-tailsthanthepower-law (Fig.3).Thiscouldbeaconsequenceofisolatingthedairysystem, i.e.bynotconsideringthemovementstowardsfatteningfarmsand slaughterhouses.

Thisstudywasspeciﬁcallyperformedonthedairycattle sys-tem,ignoringthebeefsector.ThemainreasonwasthatthebTB surveillancesysteminERisinplaceonthedairysectoronly. More-over,furtherreasonssupportedthischoice:(i)thehighereconomic valueofthedairysectorintheRegion;(ii)thefactthatgenerally beeffarmsdonotexchangeanimalswithotherfarms,theyonly receivemalecalveswithin100daysofagefromdairyfarmsand sendanimalstoslaughterhousesattheendofthefattening pro-cess;(iii)thetwosystemsarealmostfullyseparated,sothereis nopossibilityofspill-overfromthebeeftothedairysystem;and (iv)dairycattlelivesubstantiallylongerthanbeefcattle,i.e.upto 5yearsvs.2respectively,thusepidemicshavemorepossibilityto developinthedairysystembecauseofbTBlongincubationperiod. Our analysesshowedthat thefarms in-degree and the out-degreewerenotcoupled.Inotherwords,farmshavingahigher in-degree,i.e.thatimportrelativelymorecattlefromotherfarms intheRegion,donotnecessarilyhaveahighout-degree,andvice versa.AsimilarresultisoutlinedinVolkovaetal.(2010)onScottish farms,suggestingthatthiscouldbearecurrentpatternforcattle herdsindifferentmanagementcontexts.

IntheUKand inother countrieswhere bTBisendemic,the highcostofthesurveillanceanderadicationprogramme,namely

£

74–99million ayear forUK (Smithand Clifton-Hadley, 2008; TorgersonandTorgerson,2008)hasbeenputunderscrutiny.Our resultsindicatethataproposalforsimpliﬁcationorstrengthening ofanintegratedsurveillancesystemshouldbealwaysbasedona rigorous,quantitativeunderstandingoftheroleplayedbyeachof itscomponents.Infactunderstandingtheactualeffectivenessof aspeciﬁccontrolstrategy,isnotalwaysstraightforward,andcan beaffectedbyfactorslikenetworktopology,thetradingsystem, rearingmethods,testfrequencyandtestsensitivity.Moreover,the increasingdensityofungulatesandotherpotentialwildlife reser-voirspeciesoccurringin severalEuropeancountriesposesnew challengesfortheexistingsurveillancesystemseveninareasthat arecurrentlybTB-free(Schöningetal.,2013).

A priori information on the network structure of cattle exchangesduringnon-emergencyperiodscanhelporienting con-trolstrategiestopreventepidemicsinareascharacterizedbyahigh numberofindustrialfarms(Bajardietal.,2012;Gilbertetal.,2005). Ourframework,basedonamathematicalmodellingapproach, pro-videddecision-makerswithapowerfulcost-effectivetooltoassess theeffectivenessofthecurrentbTBsurveillancesysteminER,by highlightingstrengthsandweaknessesitsdifferentcomponents.

Moreover, the methodological approach proposed here will allowthehealth authoritiestoassesstheeffectivenessoffuture alternative strategies for bTB detection that are not currently

implementedinER,suchasantigendetectingtests(ELISA)onmilk andbloodsample(vanAsseldonketal.,2005).Furthermore,by tak-ingintoconsiderationthecostofdifferenttestingstrategies,itwill bepossibletouseourmodellingapproachtoruncost-effectiveness analysesofdifferentoptionsofsurveillance(Andersonetal.,2013; Cameron,2012).

Acknowledgements

We would like to thank Marco Tamba for the constructive discussionsaboutbovinetuberculosissurveillanceandtheherd systemofEmilia-Romagna.

Thisstudywasfunded byIZSLERwithintheresearchproject AUTIFIN-TBC-EMILIA-2011 ‘Development of an Epidemiological ModelforBovineTuberculosisinEmilia-Romagna’.

AppendixA. Supplementarydata

Supplementarymaterialrelatedtothisarticlecanbefound,in theonlineversion,atdoi:10.1016/j.epidem.2015.02.007.

References

Abernethy,D.A.,Upton,P.,Higgins,I.M.,McGrath,G.,Goodchild,A.V.,Rolfe,S.J., Broughan, J.M., Downs, S.H.,Clifton-Hadley, R.,Menzies, F.D., de la Rua-Domenech,R.,Blissitt,M.J.,Duignan,A.,More,S.J.,2013.Bovinetuberculosis trendsintheUKandtheRepublicofIreland,1995–2010.Vet.Rec.172,312.

Agusto,F.B.,Lenhart,S.,Gumel,A.B.,Odoi,A.,2011.Mathematicalanalysisofamodel forthetransmissiondynamicsofbovinetuberculosis.Math.MethodsAppl.Sci. 34,1873–1887.

Anderson,D.P.,Ramsey,D.S.L.,Nugent,G.,Bosson,M.,Livingstone,P.,Martin,P.A.J., Sergeant,E.,Gormley,A.M.,Warburton,B.,2013.Anovelapproachtoassessthe probabilityofdiseaseeradicationfromawild-animalreservoirhost.Epidemiol. Infect.141,1509–1521.

Anonymous,2008.Bovinetuberculosis.In:ManualofDiagnosticTestsand Vac-cinesforTerrestrialAnimals.WorldOrganizationforAnimalHealth(OIE),Paris, France,pp.683–697.

Asseged,B.,Woldesenbet,Z.,Yimer,E.,Lemma,E.,2004.Evaluationofabattoir inspectionforthediagnosisofMycobacteriumbovisinfectionincattleatAddis Ababaabattoir.Trop.Anim.HealthProd.36,537–546.

Bajardi,P.,Barrat,A.,Natale,F.,Savini,L.,Colizza,V.,2011.Dynamicalpatternsof cattletrademovements.PLoSONE6,e19869.

Bajardi,P.,Barrat,A.,Savini,L.,Colizza,V.,2012.Optimizingsurveillanceforlivestock diseasespreadingthroughanimalmovements.J.R.Soc.Interface9,2814–2825.

Barlow,N.D.,Kean,J.M.,Hickling,G.,Livingstone,P.G.,Robson,A.B.,1997.A simu-lationmodelforthespreadofbovinetuberculosiswithinNewZealandcattle herds.Prev.Vet.Med.32,57–75.

Brooks-Pollock,E.,Keeling,M.J.,2009.Herdsizeandbovinetuberculosispersistence incattlefarmsinGreatBritain.Prev.Vet.Med.92,360–365.

Brooks-Pollock,E.,Roberts,G.O.,Keeling,M.J.,2014.Adynamicmodelofbovine tuberculosisspreadandcontrolinGreatBritain.Nature511,228–231.

Cameron,A.R.,2012. Theconsequencesofrisk-basedsurveillance:developing output-basedstandardsforsurveillancetodemonstratefreedomfromdisease. Prev.Vet.Med.105,280–286.

Cheeseman,C.L.,Wilesmith,J.W.,Stuart,F.A.,1989.Tuberculosis:thediseaseandits epidemiologyinthebadger,areview.Epidemiol.Infect.103,113–125.

Clauset,A.,Shalizi,C.,Newman,M.,2009.Power-lawdistributionsinempiricaldata. SIAMRev.51,661–703.

Clegg,T.A.,More,S.J.,Higgins,I.M.,Good,M.,Blake,M.,Williams,D.H.,2008.Potential infection-controlbeneﬁtforIrelandfrompre-movementtestingofcattlefor tuberculosis.Prev.Vet.Med.84,94–111.

Conlan,A.J.K.,McKinley,T.J.,Karolemeas,K.,Pollock,E.B.,Goodchild,A.V.,Mitchell, A.P.,Birch,C.P.D.,Clifton-Hadley,R.S.,Wood,J.L.N.,2012.Estimatingthehidden burdenofbovinetuberculosisinGreatBritain.PLoSComput.Biol.8,e1002730.

Cousins,D.V.,2001.Mycobacteriumbovisinfectionandcontrolindomesticlivestock. Rev.Sci.Tech.20,71–85.

delaRua-Domenech,R.,Goodchild,A.T.,Vordermeier,H.M.,Hewinson,R.G., Chris-tiansen,K.H.,Clifton-Hadley,R.S.,2006.Antemortemdiagnosisoftuberculosis incattle:areviewofthetuberculintests,gamma-interferonassayandother ancillarydiagnostictechniques.Res.Vet.Sci.81,190–210.

Diekmann,O.,Heesterbeek,J.,Metz,J.,1990.Onthedeﬁnitionandthe compu-tationofthebasicreproductionratioR0inmodelsforinfectiousdiseasesin

heterogeneouspopulations.J.Math.Biol.28,365–382.

Fischer,E.A.J.,vanRoermund,H.J.W.,Hemerik,L.,vanAsseldonk,M.A.P.M.,de Jong,M.C.M.,2005.Evaluationofsurveillancestrategiesforbovinetuberculosis (Mycobacteriumbovis)usinganindividualbasedepidemiologicalmodel.Prev. Vet.Med.67,283–301.

Fitzgerald,S.D.,Kaneene,J.B.,2013.Wildlifereservoirsofbovinetuberculosis world-wide:hosts,pathology,surveillance,andcontrol.Vet.Pathol.50,488–499.

(9)

Gilbert,M.,Mitchell, A.,Bourn, D.,Mawdsley,J., Clifton-Hadley,R.,Wint, W., 2005.CattlemovementsandbovinetuberculosisinGreatBritain.Nature435, 491–496.

Gillespie,D.,1977.Exactstochasticsimulationofcoupledchemicalreactions.J.Phys. Chem.93555,2340–2361.

Gordon,S.,2008.BovineTB:stoppingdiseasecontrolwouldblockallliveexports. Nature456,700.

Grifﬁn,J.M.,Williams,D.H.,Kelly,G.E.,Clegg,T.A.,O’Boyle,I.,Collins,J.D.,More,S.J., 2005.Theimpactofbadgerremovalonthecontroloftuberculosisincattleherds inIreland.Prev.Vet.Med.67,237–266.

Hadorn,D.,Stärk,K.,2008.Evaluationandoptimizationofsurveillancesystemsfor rareandemerginginfectiousdiseases.Vet.Res.39,57–68.

Huang,Z.Y.X.,deBoer,W.F.,vanLangevelde,F.,Xu,C.,Jebara,K.B.,Berlingieri,F., Prins,H.H.T.,2013.Dilutioneffectinbovinetuberculosis:riskfactorsforregional diseaseoccurrenceinAfrica.Proc.R.Soc.B280,20130624.

Humblet,M.-F.,Moyen,J.-L.,Bardoux,P.,Boschiroli,M.L.,Saegerman,C.,2011.The importanceofawarenessforveterinariansinvolvedincattletuberculosisskin testing.Transbound.Emerg.Dis.58,531–536.

Italian National Statistics Institute (ISTAT), 2010. 6◦ _Censimento _Generale

dell’Agricoltura.http://censimentoagricoltura.istat.it/

Johnston,W.T.,Vial,F.,Gettinby,G.,Bourne,F.J.,Clifton-Hadley,R.S.,Cox,D.R.,Crea, P.,Donnelly,C.A.,McInerney,J.P.,Mitchell,A.P.,Morrison,W.I.,Woodroffe,R., 2011.Herd-levelriskfactorsofbovinetuberculosisinEnglandandWalesafter the2001foot-and-mouthdiseaseepidemic.Int.J.Infect.Dis.15,e833–e840.

IndependentScientiﬁcGroup,2007.BovineTuberculosis:TheScientiﬁcEvidence. DEFRAPublications,London.

Marangon,S.,Martini,M.,DallaPozza,M.,Neto,F.,1998.Acase-controlstudyon bovinetuberculosisintheVenetoRegion(Italy).Prev.Vet.Med.34,87–95.

Morris,R.S.,Pfeiffer,D.U.,Jackson,R.,1994.TheepidemiologyofMycobacteriumbovis infections.Vet.Microbiol.40,153–177.

Natale,F.,Giovannini,A.,Savini,L.,Palma,D.,Possenti,L.,Fiore,G.,Calistri,P.,2009.

NetworkanalysisofItaliancattletradepatternsandevaluationofrisksfor potentialdiseasespread.Prev.Vet.Med.92,341–350.

Natale,F.,Savini,L.,Giovannini,A.,Calistri,P.,Candeloro,L.,Fiore,G.,2011. Evalua-tionofriskandvulnerabilityusingaDiseaseFlowCentralitymeasureindynamic cattletradenetworks.Prev.Vet.Med.98,111–118.

O’Hare,A.,Orton,R.,Bessell,P.R.,Kao,R.R.,2014.Estimatingepidemiological param-etersforbovinetuberculosisinBritishcattleusingaBayesianpartial-likelihood approach.Proc.R.Soc.B281,1783.

Phillips,C.J.C.,Foster,C.R.W.,Morris,P.A.,Teverson,R.,2003.Thetransmissionof Mycobacteriumbovisinfectiontocattle.Res.Vet.Sci.74,1–15.

Reilly,L.A.,Courtenay,O.,2007.Husbandrypractices,badgersettdensityandhabitat compositionasriskfactorsfortransientandpersistentbovinetuberculosison UKcattlefarms.Prev.Vet.Med.80,129–142.

Reynolds,D.,2006.AreviewoftuberculosisscienceandpolicyinGreatBritain.Vet. Microbiol.112,119–126.

Rohani,P.,Keeling,M.J.,Grenfell,B.T.,2002.Theinterplaybetweendeterminismand stochasticityinchildhooddiseases.Am.Nat.159,469–481.

Schiller,I.,Waters,W.R.,Vordermeier,H.M.,Jemmi,T.,Welsh,M.,Keck,N.,Whelan, A.,Gormley,E.,Boschiroli,M.L.,Moyen,J.L.,Vela,C.,Cagiola,M.,Buddle,B.M., Palmer,M.,Thacker,T.,Oesch,B.,2011.BovinetuberculosisinEuropefromthe perspectiveofanofﬁciallytuberculosisfreecountry:trade,surveillanceand diagnostics.Vet.Microbiol.151,153–159.

Schöning,J.M.,Cerny,N.,Prohaska,S.,Wittenbrink,M.M.,Smith,N.H.,Bloemberg,G., Pewsner,M.,Schiller,I.,Origgi,F.C.,Ryser-Degiorgis,M.-P.,2013.Surveillance ofbovinetuberculosisandriskestimationofafuturereservoirformationin wildlifeinSwitzerlandandLiechtenstein.PLoSONE8,e54253.

Shittu,A.,Clifton-Hadley,R.S.,Ely,E.R.,Upton,P.U.,Downs,S.H.,2013.Factors asso-ciatedwithbovinetuberculosisconﬁrmationratesinsuspectlesionsfoundin cattleatroutineslaughterinGreatBritain,2003–2008.Prev.Vet.Med.110, 395–404.

Smith,N.H.,Clifton-Hadley,R.,2008.BovineTB:don’tgetridofthecatbecausethe micehavegone.Nature456,700.

Smith,R.L.,Schukken,Y.H.,Lu,Z.,Mitchell,R.M.,Grohn,Y.T.,2013.Developmentof amodeltosimulateinfectiondynamicsofMycobacteriumbovisincattleherds intheUnitedStates.J.Am.Vet.Med.Assoc.243,411–423.

Skuce,R.A.,Allen,A.R.,McDowell,S.W.J.,2012.Herd-levelriskfactorsforbovine tuberculosis:aliteraturereview.Vet.Med.Int.2012,621210.

Torgerson,P.,Torgerson,D.,2008.Doesrisktohumansjustifyhighcostofﬁghting bovineTB?Nature455,1029.

vanAsseldonk,M.A.P.M.,vanRoermund,H.J.W.,Fischer,E.A.J.,deJong,M.C.M., Huirne,R.B.M.,2005. Stochasticefﬁciency analysisofbovine tuberculosis-surveillanceprogramsintheNetherlands.Prev.Vet.Med.69,39–52.

Vernon,M.,Keeling,M.J.,2009.RepresentingtheUK’scattleherdasstaticand dynamicnetworks.Proc.R.Soc.B:Biol.Sci.276,469–476.

Volkova,V.V.,Howey,R.,Savill,N.J.,Woolhouse,M.E.J.,2010.Potentialfor transmis-sionofinfectionsinnetworksofcattlefarms.Epidemics2,116–122.

Vuong,Q.,1989.Likelihoodratiotestsformodelselectionandnon-nested hypothe-ses.Econom.J.Econom.Soc.57,307–333.

Welby,S.,Govaerts,M.,Vanholme,L.,Hooyberghs,J.,Mennens,K.,Maes,L.,VanDer Stede,Y.,2012.BovinetuberculosissurveillancealternativesinBelgium.Prev. Vet.Med.106,152–161.