26 GEOGRAPHY to particular stagesinthelife of cycle to particular in thepreceding year influenceLyme disease(Lyme borreliosis) patterns. relatedThis islikely the datashowed suchastheaverage thatclimaticfactors temperatures inJulyandSeptember promotes thetransitandlocalizationrates Correlation ofcarriers. andregression analysisof discusses therole development: itlikely oftheanthropogenic innatural-ecosystem factor the dangerofinfection causedbynatural-focal diseasesinthe Vladimir region. The paper intheregion.administrative districts Aschematicmaphasbeencompiled;thereflects ArcView 3.1(GISsoftware). ofnaturalfoci forThe activity eachzooanthroponosis between varied rates ofthesenatural-focalmorbidity diseaseshave using beendifferentiated byterritory a complexofnatural-focal diseasesinthe Vladimir region (),from 1958to 2012. The 1 2 Tatiana A. Trifonova * Corresponding Author [email protected] e-mail: boundaries, infectious agents are transmitted transmissible infections) ecology. Within these control led byendotherm are formed. Natural foci are boundaries in specificterritories, where naturalfoci are localized arthropods cases, blood-sucking ofmany wildanimals and,Ranges inmany 2006]. gardening [Istomin, andoutdoor activities of becauseoftheincreasedrising popularity foci. Similarly, ofinfection hasbeen therisk system response zones invarious withnatural populations withoutanappropriate immune regions,migration to certain whichleadsto isgainingrelevancemonitoring due to active changes. Aside from that,natural-focal disease change andanthropogenic territorial relevant, ofclimate especially inthecontext focal diseasesisbecoming increasingly circulation oftheinfectious agentsofnatural- Today in thenatural thestudyofpatterns a mathematicalmodelfor thepredictionofLyme borreliosis hasbeencreated. patterns ABSTRACT. INTRODUCTION KEY WORDS: IN THE VLADIMIR REGION (RUSSIA) NATURAL-FOCAL DISEASES

A.G. andN.G.Stoletov [email protected] e-mail: Vladimir State University; Lomonosov State University, Faculty Science; ofSoil The paper describes astudythatmonitoredThe paperdescribes theepidemiological situationof Vladimir region, epidemiological situation, natural-focal diseases, predicting. 1 , Anton A.Martsev and arthr opod (in Ixodidae 2* ticks. Usingmultiplelinearregression analysis, [Utenkova, 2009]. [Utenkova, in research objectives todaypertinent groupsand defininghigh-risk are themost zoning bydegree ofepidemiological risk, rates, distribution, analyzingterritorial thatinfluencemorbidity factors Determining 2010]. [Kormilenko, rates forincrease themorbidity thesediseases ofactivity unpredictablespurts their often However, theenduranceofnaturalfoci and ofhumaninfectiousthe structure diseases. focal diseasesare relatively insignificant in ofregisteredterms rates, natural- morbidity their epidemiological manifestation. In agents ofnatural-focal diseasesandintensifies promotes thetransmissionofinfectious epizoological, andepidemiological activity, along withadeclineinconservational, onnaturalecosystems,Active humanimpact focus [Zuyeva, 2005]. infected onlywhentheyare withinanatural animals.between People onlybecome pines, andjuniperbushes andheather. The alders andaspens andsandyhillockswithtall water,turbid large wetlands overgrown with sandy ridges. There are many forest with lakes wetland area thatissometimesinterrupted by lowlands –ahomogenousandflat Meshchera oftheregion isoccupiedbythepart irregularity.minor terrain The southwestern European Plain whichhaslowelevationsand are byitslocationwithintheEast determined region. topographicThe territory’s features The studieswere inthe conducted Vladimir [Aminev, 2013,Kolominov, 2012]. been madeto create aprognostic model on statisticallysignificant data,attempts have rates.natural-focal Based diseasemorbidity that precede theepidemicseasoninfluence average monthlytemperature inthemonths such asprecipitation rates, humidity, and A numberofauthorsnote thatclimate indices 2004]. [Suntsova,is influencedbyclimaticfactors population size, whereas theinfection rate ofaregioncharacteristics influencethetick The ecological andgeographical be quite contradictory. 2004],however the datacan 2012, Utenkova, [Antov,factors 2005,Aminev, 2013,Kolominov, and climaticecological, socio-economical, zooanthroponoses and diseasepatterns Many authorsnote therelationship between situation andallowprediction. indicatecan objectively theepidemiological problems listed rates above, onlymorbidity for research. objective inmindthe Bearing obstacles sometimes impossible-to-overcome a carrier-present areas represent and serious infectious agentsofnatural-focal diseasesin of testing systems to gage the presence of Protection)Rights regional branchesand onConsumer for Surveillance Service in Rospotrebnadzor’s (theRussianFederal specialized entomologists andzoologists fundingandabsenceof A lackofnecessary T.A. Trifonova, A.A.Martsev MATERIALS AND METHODS

NATURAL-FOCAL DISEASES IN THE VLADIMIR REGION (RUSSIA) and ( field mice( and The Vladimir region isinatemperate abundance ofwetlands andlakes. meters above meansealevel andhasan Novgorodthe Nizhny region. This landis100 lowlandin merge withtheBalakhninskaya River.the Onitswestern side, they oftheregion, bankof alongtheleft part andnortheastern located inthenorthern lowlandsare The Nerlinsko-Klyazminskaya The relative elevationsreach 40–60meters. embedded riverbeds, ravines, andgulches. isheavily bydeeply dissected The surface the mostelevated area inthe Vladimir region. absolute elevation reaches 240meters. This is andflatmorainichills.shape ofridges The ridge. Dmitrovsky the These branchestake the region consistsofbranchestheKlinsko- of Kovrov. of elevated part The northwestern oftheregion, southofthecity part eastern limestone, stretches outlongitudinally inthe Oksko-Tsninskiy composedof embankment, tundra voles ( rodents –bankvoles ( epidemiologically significant speciesare 1500 invertebrate species. The most species, 6reptile species, andapproximately 43 fishspecies, 212bird species, 10amphibian The faunaconsistsof62mammalspecies, bog soil. coniferous trees dominate withunderbrush aspens dominate clay soilandclay loam,and sandy soilandloam,firtrees and 1200 species. Pines andbirches dominate The floraisquite diverse andconsistsofabout lowest precipitations rates part. intheeastern unevenly spread throughout thearea, withthe the region issufficientlyhumid. Precipitation is defined intermediate seasons. of The majority cold winter withstablesnowcover, andwell- summer, byawarm characterized moderately work on collecting epidemiological datain oncollecting work fromThe materials theauthors’ ownfield Mus musculus Ixodidae continental climate zone, whichis Apodemus agrariusApodemus ticks– ), brown rats( Microtus oeconomusMicrotus I. ricinus Myodes glareolusMyodes Rattus norvegicus and ), housemice I. persulgatus. ), striped ), ); );

27 GEOGRAPHY 28 GEOGRAPHY nonlinear regression analysis the most byusing multiple linearandAfterwards, rate). indices oneyear backrelative to themorbidity correlation coefficient (i.e. climate byshifting into considerationwhencalculating the rates wasalsotakenyears onmorbidity indices for thecurrent andpreceding Korotkov, 1999]. The influenceofclimate from aprognostic equation[Caughley, 1979, not leadto aremoval oftheseindicators the dynamicofprocess inquestionshould about itsrole inshaping lack ofknowledge as thecorrelation isstatistically significant, a effect relations cannotbeinterpreted. As long even whencause-and- these isworthwhile like correlation coefficient.Elucidatingfactors rates (p with morbidity that were statisticallysignificantly correlated Initially, (predictors) factors were determined in theatmosphere from 1977to 2012. pressure, snowcover size, andoxygen levels with precipitation permonth,atmospheric monthly temperature, numberofdays the following indiceswere used:average rates,focal dataon diseasemorbidity hydrometeorological onthenatural- factors order toIn evaluate theinfluenceof or 3(highrisk). 2(moderate risk), 1(lowrisk), degree ofrisk: was assigned arankthatcorresponded to the summed and, basedonthetotal, eachdistrict were district zooanthroponoses inaparticular people. rates forThe morbidity eachseparate rate wascalculated per100000morbidity a pointsystem. Eachnatural-focal disease inhuman populationwasgagedusing risk The degree ofnatural-focal diseaseinfection infectious andparasiticdiseases. as from Rospotrebnadzor’s on officialreports Center for Hygiene andEpidemiology, aswell rates were from taken the The dataonnatural-focal diseasemorbidity from 1958to 2012were usedinthisstudy. the Vladimir region, aswell asstatisticaldata GEOGRAPHY. ENVIRONMENT. SUSTAINABILITY. 04(08)2015 m 0,05),usingPearson’s regard to theirdanger to public health.In all casesofzooanthoponosis infections with we suggestamethod thatinvolves equalizing inquestion, order toIn evaluate the territory inany area. human activity forfocal planningand diseases, isnecessary infection, butfor the entire complexofnatural- degree notjustfor ofinfection risk asingle showsthatinformationExperience onthe district. to 2012were recorded intheGus-Khrustalny rates fromhighest tularemia 1958 morbidity Petushinsky, the districts; andKovrovsky 2012 were recorded in theKolchuginsky, rates fromborreliosis 2005to morbidity thehighestLymePetushinsky districts; were and recorded intheGorokhovetsky rates inthesametimeperiodmorbidity thehighestleptospirosisPolsky districts; and Gorokhovetsky, Kameshkovsky, Yuryev- from 1978to 2012were recorded inthe rates (per100000people) HFRS morbidity in theregion varies. Therefore, thehighest ofdifferentThe focal activity zooanthroponoses among allinfections. rateshas byfarthehighestmorbidity and tularemia. However, Lyme borreliosis borreliosis (orLyme disease),leptospirosis fever withrenal syndrome (HFRS),Lyme natural-focal infections: hemorrhagic isendemicto thefollowingthat territory in thisstudy. The analysisdemonstrates bythestatisticaldatagatheredis confirmed of anumbernatural-focal diseases, which for thecirculation oftheinfectious agents Vladimir region’s naturalconditionsallow were usedto compileandeditthemaps. ArcView Paint 3.1,GISsoftware, andMicrosoft correlation andregressionconduct analysis. of residuals. STATISTICA wasusedto software derived usingtheR determined. Aprognostic equationwas significant predictors were incrementally RESULTS AND DISCUSSION 2 value and distribution value anddistribution T.A. Trifonova, A.A.Martsev increased by46%. rates have2005 to 2012,andthemorbidity 2005. 1211cases have been registered from rates have beenmonitoredmorbidity since the In Vladimir region, Lyme borreliosis this territory. disease were registered mostfrequently in epidemiological process, ascasesofthis that influenced theLymefactors borreliosis Furthermore, anattempt wasmadeto locate (Fig. 1). indifferentvaries intheregion districts that thenatural-focal diseaseinfection risk rates. Our analysisdemonstratesmorbidity ofthemulti-year zooanthroponosisranking the Vladimir region wascompiledbasedon fordisease infection risk thepopulationof thenatural-focalThe finalmapreflecting of Lyme borreliosis. asbecominginfectedhealth risk withacarrier oftularemia poses the samewith acarrier other words, inthismodel, becominginfected Fig. 1. Natural-focal disease infection risk in the Vladimir region. Vladimir the in risk infection disease 1.Fig. Natural-focal

NATURAL-FOCAL DISEASES IN THE VLADIMIR REGION (RUSSIA) decades ago, tick-borne infections generally ourview, attention. AfewIn deserves thisfact respectively) inthe Vladimir region. indices (r=–0.19p0.47;r –0.26p=0.33 significant theabove correlation between established thatthere is nostatistically and wetland cover were analyzed. This analysis rates; then,theinfluenceofforestmorbidity theLymeanalysis andcomparing borreliosis aspatialcorrelationanalyzed byconducting development onLyme borreliosis was First, thepotential influenceofland (Fig. 2). are more districts heavilysouthern populated the region ismostserious, even thoughthe ofepidemiological situationinthenorth the entire period. monitoring However, the during district infection intheMelenkovsky example, there hasonlybeenone caseof for throughoutdistributed theterritory: Petushinsky districts. The diseaseisunevenly recorded inthe Kovrovsky, Kolchuginsky, and rates have beenThe highestmorbidity

29 GEOGRAPHY 30 GEOGRAPHY GEOGRAPHY. ENVIRONMENT. SUSTAINABILITY. 04(08)2015 meadows andformer arableland. Abandoned are settled, often aswell astheir outskirts, populations.urban Forests, especially on of has alsochangedbecauseof thearrival built. ofruralsettlementsThe structure recreational zones, androads have been vacation homes, summer cottages, rural has increased andfarmore homes, suburban thus itsecological niche. Humanmobility has significantly changeditsway oflife and in thelast20–30years Russia’s population be quite isolated from oneanother. However, and landintended for development usedto thatcommercial landexplained bythefact in landscapecharacteristics. This canbe been increasing independently ofdifferences rates have morbidity changed dramatically: in suchareas. Today thesituationhas urgent needto organize medical monitoring Evidently, thatexplainswhy there wasno central oblastsoftheNonchernozem belt. populationincommon amongtheurban forested landscapes;theywere notvery affected peoplelivinginthetaigaor Fig. 2. Lyme borreliosis morbidity rates in the Vladimir region. Vladimir the in rates morbidity borreliosis Lyme 2. Fig. indices). 2004 to 2012(atotal ofabout100 different oxygen levels intheatmosphere from pressure, snowcover size, andmonthly with precipitation, humidity, atmospheric temperature, thenumberofdays permonth on Lyme borreliosis: average monthly influence ofthefollowing climate indices Our studyalsoanalyzed thepotential ecological balanceisthereal culprit. whichdisruptstheanthropogenic activity humans ofpersonalresponsibility!);rather, gained popularity, becauseitabsolves (by theway, thisexplanation hasrecently is to blamefor thespread ofLyme disease thatitisnotclimate changethat be asserted negative transformations. Evidently, itcan ecosystems are disintegrating, leadingto more orlessisolated (naturallystructured) their hosts. Therefore, of theboundaries for thecirculation ofIxodidae ticksand creating newconvenient ecological niches croplands are intensely overgrowing, equation isshown below: The finalmultiple nonlinearregression rates.correlation withmorbidity the epidemiological process, despite itshigh because itdidnothave asignificant effect on oftheprevious year inJanuary the humidity of theprevious year. ruledout The software and theaverage temperature inSeptember temperature inJulyoftheprevious year epidemiological process are theaverage most significant valuesinfluencingthe demonstrated thattheThe software ofresiduals.distribution validity wasdefinedbytheR validity and aprognostic equationwasderived. Its epidemiological predictors were determined STATISTICA software, themostsignificant nonlinear regression analysis, achieved via As aresult ofthisincremental multiple have paircorrelations witheachother). of thepreceding year (theseindicesdonot preceding year, inJanuary andthehumidity the average temperature inSeptember ofthe temperature inJulyofthepreceding year, used intheprognostic model:theaverage Ultimately, thefollowing predictors were correlations from theprognostic equation. modeling byremoving predictors withpair wasruledoutin the model. Multicollinearity epidemiological process canbeincludedin values thatprecede thebeginning ofthe process begins inlate April, onlythe Because Lyme borreliosis’ epidemiological (r =–0.94p<0.05). in theatmosphere inJulyoftheprevious year in March (r=0.94p<0.05),andoxygen levels previous year (r=0,71p<0.05), snowdepth ofthe inJanuary p <0.05),thehumidity in September oftheprevious year (r=–0.91 (r =0.77p<0.05),theaverage temperature temperature inJulyoftheprevious year and thefollowing indices:theaverage theLyme ratesbetween diseasemorbidity revealed astatisticallysignificant correlation Correlation ofindices analysisofthearray T.A. Trifonova, A.A.Martsev

2 value andthe NATURAL-FOCAL DISEASES IN THE VLADIMIR REGION (RUSSIA) to thelineandare distributed. normally demonstrates thatallthe valueslieclose residuals wasanalyzed: theplotinFig. 3 ofrecursiveequation, thedistribution To ofthederived ensure the accuracy The R is stronger. thedependenceonJuly’syear; temperatures temperature inSeptember oftheprevious July oftheprevious year andtheaverage depends ontheaverage temperature in epidemiological process ofLyme borreliosis According to theequation, year. average temperature inJulyoftheprevious in September oftheprevious x2– year; population; x1–average temperature y –Lyme rate inthe borreliosis morbidity y =–577.938–0.84(x1 0.037(x2 residuals. the predicted values, aswell asthe rate valuesand morbidity the observed The datain Table 1allows usto compare probability of99%. theepidemiologicaldescribes process witha Table 1. Predicted Values and Residuals of Multiple Multiple of Residuals and Values Table 1. Predicted 02292296.1 –2.9 0.5 212.9 200.9 –0.4 93.5 219 2.2 198 4.1 194.4 2012 94 176.8 –2 2011 91.9 194 –7.4 2010 179 129.0 2009 96 157.4 2008 127 2007 150 2006 2005 Year 2 valueallowsusto infer thatthismodel 3 ); R 2 Morbidity Morbidity Observed Observed Nonlinear Regression =0.99p<0.001 Rates 2 ) +58.538(x2)– Morbidity Morbidity Predicted Rates Residuals

31 GEOGRAPHY 32 GEOGRAPHY between Lymebetween borreliosis andtheaverage of Lyme borreliosis. The negative correlation and thusthespread ofthe infectious agent towhich contributes theticks’ development by theabundanceofhosts in thisperiod, average temperature inJulycanbeexplained correlation Lyme between borreliosis and the is statistically accurate [Trukhacheva, 2013]. is statisticallyaccurate [Trukhacheva, Therefore, theconclusionwe have reached GEOGRAPHY. ENVIRONMENT. SUSTAINABILITY. 04(08)2015 the life of cycle year. beexplainedbyThis canmostlikely temperature inSeptember oftheprevious July oftheprevious year andtheaverage depends ontheaverage temperature in epidemiological process ofLyme borreliosis modelindicatesThe derived thatthe of themodel. (Fig.the normal thevalidity 4)alsosupports ofresidualsshows thedistribution closeto The histogram ofstandard residuals that Fig. 3. Normal Probability Plot of Residuals Fig. 4. Distribution of Standard Residuals. in Multiple Nonlinear Regression. Ixodidae ticks. The positive ticks. The July andcoldSeptember. are associated withayear preceded byhot that higherLyme rates borreliosis morbidity Lyme borreliosis. The modeldemonstrates theappropriateand to steps take to fight used to predicttheepidemiological situation 4. mathematicalmodelcanbeThe derived the region. theepidemiological situationinimpacts affect theticks’ life which inturn cycle, of theprevious years) are to have likely to and theaverage temperature inSeptember temperature inJulyoftheprevious year 3. The identified climate indices(theaverage and thusthediseaseitself. and thedelocalizationofinfectious agents ofnaturalecosystemslead to thedestruction demonstrated thatanthropogenic factors to definethespread of These parameters were traditionallythought forest orwetland cover inany oftheterritories. Vladimir region isnotrelated of to theextent 2. The Lyme rate inthe borreliosis morbidity region. the Vladimir natural-focalcaused byvarious diseasesin ofinfectioncompiled mapreflectstherisk are confinedto different territories. The tularemia. The naturalfoci oftheseinfections HFRS, Lyme borreliosis, leptospirosis, and the following natural-focal infections: 1. The Vladimir region isendemicto Conclusions winter. successfully endure lowtemperatures inthe molted female ticksto enter diapauseand temperatures inSeptember causerecently and hadasufficientfood supply).Lower of theireggs(ifthefemales found ahost first frost) leadsto theirdeathandthe to search for the hosts;whichthen(after September causerecently molted females hightemperaturesin thefollowing in way: temperature inSeptember canbeinterpreted Ixodidae  ticks.have We 1. Aitov, K. (2005) Prirodno-ochagovye transmissivnye kleshchevye infektsii Pribaykal’ya infektsii Aitov, transmissivnye Pribaykal’ya (2005)Prirodno-ochagovye kleshchevye K. 1. 1. Zue 11. E.O. Utenkova, infektsii (2009)Prirodno-ochagovye v Volgo-Vyatskom regione (Natural- 10. issle- Trukhacheva, vmediko-biologicheskikh N.V. statistika (2012)Matematicheskaya 9. ochagov prirodnykh SuntsovaO.V. kharakteristika (2004)Ekologo-parazitologicheskaya 8. Korotkov Yu.S., strukturachislennostitaezhno- N.M.(1999) Khronologicheskaya Okulova 7. Ku KGL, I.V. Likhoradki aspekty Kormilenko, iepidemiologicheskie (2010)Ekologichskie 6. rasprostraneniya i aspekty Kolominov, iekologicheskie S.I.(2012)Epizootologicheskie 5. Istomin, A.V. infektsiy (Regional (2006)Regional’nyy prirodno-ochagovykh monitoring 4. Caughley, G.(2004)Analysisof Vertebrate Populations. Caldwell, N.J.: Press.The Blackburn 3. Aminev, iepizootologicheskie osobennostigemmor- R.(2013)Epidemiologicheskie 2. eevd1.021 Accepted 06.11.2015 Received 10.10.2015 T.A. Trifonova, A.A.Martsev REFERENCES sercat.com (Natural-Focal Transmissive Region): n.pag. inthe Baikal Tick-Borne Infections 38 pages. 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NATURAL-FOCAL DISEASES IN THE VLADIMIR REGION (RUSSIA) www.dis-

33 GEOGRAPHY 34 GEOGRAPHY GEOGRAPHY. ENVIRONMENT. SUSTAINABILITY. 04(08)2015 Anton A.Martsev Tatiana A. Trifonova medical geography. at A.G.andN.G.Stoletovs Vladimir State University. Hestudies several monographs andtextbooks. She istheauthorofover 250scientificpublications, including protection, humanecology, andenvironmental management. ofenvironmental aspects in thefieldoftheoretical andpractical N.G. Stoletovs Vladimir State University. Herresearch interests are ofBiologyandEcologyatA.G. also HeadoftheDepartment the Faculty ofSoilScience, Lomonosov State University. Sheis isagraduate student ofBiologyandEcology , D. Sc., isProfessor andSeniorResearcher at