Spatial Statistical Analysis in Cow Disease Monitoring Based on GIS

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1 Spatial Statistical Aalysis i Cow Disease Moitorig Based o GIS Li Li 1, Yog Yag 2, Hogbi Wag 3, Jig Dog 1, Yuju Zhao 1, Jiabi He 1 1 School of Aimal Husbadry ad Veteriary Medicie, Sheyag Agricultural Uiversity, Sheyag, P. R. Chia, lili619619@yahoo.com.c 2 Correspodig Author: School of Iformatio ad Electrical Egieerig, Sheyag Agricultural Uiversity, Sheyag, P. R. Chia, yagsyau@163.com 3 School of aima Medicie, Northeast Agricultural Uiversity, Harbi, P. R. Chia Abstract. I this paper, we aalyze cow edemic fluorosis spatial distributio characteristics i the target district based o combiatio of spatial statistical aalysis ad GIS. This disease geographical distributio relates closely to the evirometal factors of habitats ad the correct aalysis of the spatial distributio of the disease for moitorig ad prevetio plays a importat role. However, the amout of moitorig spots is smaller ad the moitored data are very limited. How to use the limited data to show the geeral spatial distributio of the disease is a key issue i buildig the efficiet spatial moitorig method. We use GIS applicatio software ArcGIS9.1 to overcome the lack of data of samplig sites ad establish a predictio model i estimatio of the disease distributio. Comparig with the true disease distributio, the predictio model has a well coicidece ad is feasible ad provides a fudametal applicatio for other study o space-related cow diseases. Keywords: Cow disease, GIS, Spatial statistic aalysis 1 Itroductio The use of geographical iformatio systems (GIS) ad spatial statistical aalysis i public health is ow widespread. GIS is powerful automated system for the storage, retrieval, aalysis, ad display of spatial data. The system offers ew ad expadig opportuities for epidemiology because it allows a iformed user to choose betwee optios whe geographic distributios are part of the problem. Eve whe used miimally, this system allows a spatial perspective o disease. Used to their optimum level, as tools for aalysis ad decisio makig, they are ideed a ew iformatio maagemet vehicle with a rich potetial for aimal epidemiological research. Epidemiologists have traditioally used maps whe aalyzig associatios betwee locatio, eviromet, ad the disease (Busgeeth et al., 2004). GIS is particularly well suited for studyig these associatios because of its spatial aalysis ad display capabilities. Recetly GIS has bee used i the surveillace ad moitorig of vectorbore diseases (Glass GE et al., 1995; Beck et al., 1994; Richards et al.,1993; Clarke

2 et al., 1991), water bore diseases (Braddock et al.,1994), i evirometal health (Bares, 1994; Warteberg et al., 1993; Warteberg et al.,1992), ad the aalysis of disease policy ad plaig (Marily et al.,2004).spatial aalysis fuctio of GIS ca be wideed ad stregtheed by usig spatial statistical aalysis, allowig for the deeper exploratio, aalysis, maipulatio ad iterpretatio of spatial patter ad spatial correlatio of the aimal disease. Cow edemic fluorosis is a disease of Sigs of detal discoloratio, difficulty i masticatio, boy exostosis ad debility. It ca cause lameess, detal lesios ad illthrift i a extesive cow. The herd was lame ad the disease forced the cullig of large umbers of cows i orther Australia. Cow edemic fluorosis is biogeochemical disease. Its geographical distributio is related closely to the evirometal factors of habitats ad has some spatial characteristics, ad therefore the correct aalysis of the spatial distributio of cow edemic fluorosis for moitorig ad the prevetio ad cotrol of edemic fluorosis has a very importat role. However, the applicatio of classic statistical methods i some areas is very difficult because of the pastoral omadic cotext. The high mobility of livestock ad the lack of eough suitable samplig for the some of the difficulties i moitorig curretly make it early impossible to apply rigorous radom samplig methods. It is thus ecessary to develop a alterative samplig method, which could overcome the lack of samplig ad meet the requiremets for radomess. 2 Materials ad methods I this paper, we aalyzed the cow edemic fluorosis spatial distributio characteristics i the target district A (due to the secret of epidemic data we call it district A) based o the established GIS of the cow edemic fluorosis i this district i combiatio of spatial statistical aalysis ad GIS. We established a predictio model to estimate the edemic fluorosis distributio based o the spatial characteristic of the desity of cow edemic fluorosis. A two-stage radom samplig was used. It cosisted i geeratig samplig sites at radom ad the drawig the required umber of cow from the earest herd to each poit, ad this, for practical reasos, withi a specified radius. I this samplig method, the primary uit was defied as a settlemet, waterig poit or grazig area where aimals were expected to be foud. Due to a lack of a exhaustive list of these locatios i some areas ad because of the high mobility of pastoral herds, the upredictability of their movemet, the classical radom selectio of sites was ot feasible. Therefore, there was the eed to develop a alterative method that allows radom selectio of the first samplig uits. GIS computer applicatio software (ArcGIS) was used. 3 Cow edemic fluorosis data samplig based o GIS GIS computer applicatio software (ArcGIS) was used. ArcGIS Spatial aalyst is a extesio to ArcGIS Desktop that provides powerful tools for comprehesive, rasterbased spatial modelig ad aalysis. With its extesio to geerate radom

3 coordiates, ArcGIS is able to geerate at radom the required umber of sites withi the area where samplig eeds to take place, be it at zoe, coutry, regio or eve district level, simultaeously allowig the applicatio of weight factors to differet sub-uits of the surveyed area. Each site is idetified by its logitude ad latitude coordiates. I reality, a geerated site is materialized oly as a poit o the map ad therefore a fixed radius is defied aroud each poit i order to determie a geographical area withi which the samplig of cow edemic fluorosis is carried out. The determiatio of legth of the radius takes ito cosideratio the cow edemic fluorosis desity. A radius below 5 km i a dry area may result i too may samplig sites with o aimals, whereas i a desely populated area aroud permaet waterig poits ad grazig areas, a relatively shorter radius suffices. A surplus of the total umber of sites is geerated at radom to replace target sites that could ot be accessed due to atural obstacles or sites with o aimals withi the defied circle. 4 Spatial cluster aalysis of cow edemic fluorosis desity Spatial cluster aalysis plays a importat role i quatifyig geographic variatio patters. It is commoly used i disease surveillace, spatial epidemiology, populatio geetics, ladscape ecology, crime aalysis ad may other fields, but the uderlyig priciples are the same. Spatial clusterig aalysis is the mai research field of spatial data miig. We aalysis cow edemic fluorosis data spatial cluster usig High/Low Clusterig tool i ArcGIS which ca measures the degree of clusterig for either high values or low values. The tool calculates the value of Z score for a give iput feature class. A z-score is a measure of the divergece of a idividual experimetal result from the most probable result, the mea. Z is expressed i terms of the umber of stadard deviatios from the mea value. where: x is ExperimetalValue, μ is Mea, ad σ is StadardDeviatio. This z-value or z score expresses the divergece of the experimetal result x from the most probable result μ as a umber of stadard deviatios σ The larger the value of z, the less probable the experimetal result is due to chace. For this tool, the ull hypothesis states that the values associated with features are radomly distributed. The higher (or lower) the Z score, the stroger the itesity of the clusterig. A Z score ear zero idicates o apparet clusterig withi the study area. A positive Z score idicates clusterig of high values. A egative Z score idicates clusterig of low values.

4 5 Spatial Autocorrelatio aalysis of cow edemic fluorosis desity Spatial Autocorrelatio is correlatio of a variable with itself through space. If there is ay systematic patter i the spatial distributio of a variable, it is said to be spatially autocorrelatio. If earby or eighborig areas are more alike, this is positive spatial autocorrelatio, Negative autocorrelatio describes patters i which eighborig areas are ulike. Radom patters exhibit o spatial autocorrelatio. Spatial autocorrelatio is importat, because most statistics are based o the assumptio that the values of observatios i each sample are idepedet of oe aother. Positive spatial autocorrelatio may violate this, if the samples were take from earby areas.goals of spatial autocorrelatio Measure is the stregth of spatial autocorrelatio i a map ad test the assumptio of idepedece or radomess. Spatial autocorrelatio is, coceptually as well as empirically, the two-dimesioal equivalet of redudacy. It measures the extet to which the occurrece of a evet i a area uit costrais, or makes more probable, the occurrece of a evet i a eighborig area uit. We aalysis cow edemic fluorosis data Spatial Autocorrelatio usig Global Mora's I tool i GEODA. Mora's I is a measure of spatial autocorrelatio developed by Patrick A.P. Mora. Mora's I is defied as: I i 1 j 1 w ij x x x x wij xi x i 1 j 1 i 1 i j 2 where: N is the umber of spatial uits idexed by i ad j, x is the variable of iterest, is the mea of x, ad w ji is a matrix of spatial weights. Negative (positive) values idicate egative (positive) spatial autocorrelatio. Values rage from 1 (idicatig perfect dispersio) to +1 (perfect correlatio). A zero values idicate a radom spatial patter. For statistical hypothesis testig, Mora's I values ca be trasformed to Z-scores i which values greater tha 1.96 or smaller tha 1.96 idicate spatial autocorrelatio that is sigificat at the 5% level. 6 Results The results of cow edemic fluorosis desity spatial cluster aalysis showed that the desity of dairy cattle edemic fluorosis distributio i space with a high cocetratio of the Z Score = 5.5, P < 0.01, which meas that there is a spatial clusterig model. The results of Cow edemic fluorosis desity spatial Autocorrelatio aalysis showed Mora's I = , which meas there is a itesely spatial autocorrelatio. Based o the spatial characteristic of the desity of cow edemic fluorosis we established a predictio model to estimate the edemic

fluorosis distributio by Ordiary Krigig tool i ArcGIS. Superimposig map of predict map ad Distributio of cow edemic fluorosis epidemic focus i district A (Fig1).

5 fluorosis distributio by Ordiary Krigig tool i ArcGIS. Superimposig map of predict map ad Distributio of cow edemic fluorosis epidemic focus i district A (Fig1). Comparig with the true distributio, the predictio model has a well coicidece ad is feasible to the applicatio. Fig. 1. Superimposig map of predict ad the true distributio of cow edemic fluorosis 7 Coclusio Applied Spatial Statistics used i cojuctio with geographic iformatio systems (GIS) provide a efficiet tool for the surveillace of diseases. I the veteriary epidemiology, the advatage of mappig the locatios of farms ad other facilities with aimals is obvious. I a outbreak of a disease it could make the maagemet of the situatio easier, ad it could also provide a tool to evaluate differet strategies to prevet the spread of ifectious diseases. GIS have tremedously ehaced ecological epizootiology, the study of diseases i relatio to their ecosystems. They have foud icreasig applicatio for surveillace ad moitorig studies, idetificatio ad locatio of evirometal risk factors as well as disease predictio, disease policy plaig, prevetio ad cotrol. I this article we discuss the applicatio of GIS to veteriary ad medical research ad the moitorig method of cow edemic fluorosis based o GIS ad spatial statistical aalysis. The method usig a GIS tool facilitates is implemeted sigificatly i the cow edemic fluorosis moitorig ad ivestigatio, ad the space statistics - related predictio model provides a fudametal use for other study o space-related aimal diseases. Ackowledgemet Fudig for this research was i part provided by chia postdoctoral sciece foudatio (NO ), the postdoctoral fud of Sheyag Agricultural Uiversity, Research fud for youg teachers of Sheyag Agricultural Uiversity, Dr. Start Fud of Liaoig Provice, P. R. Chia. The authors are grateful to the Sheyag Agricultural Uiversity for providig coditios with fiishig this research.

6 Refereces 1. Warteberg D.: Screeig for lead exposure usig a geographic iformatio system. J. Eviro Res Dec. 59, (1992) 2. Glass GE, Schwartz BS, Morga JM III, Johso DT, Noy PM, Israel E. :Evirometal risk factors for Lyme disease idetified with geographic iformatio systems. Am J. Public Health. 85, (1995) 3. Beck LR, Rodrigues MH, Dister SW, Rodrigues AD, Rejmakova E, Ulloa A, et al. :Remote sesig as a ladscape epidemiologic tool to idetify villages at high risk for malaria trasmissio. Am J. Trop Med Hyg. 51, (1994) 4. Jubb TF, Aad TE, Mai DC, Murphy GM.: Phosphorus supplemets ad fluorosis i cattle--a orther Australia experiece. J. Aust Vet.70, (1993) 5. Richards FO, Jr.: Use of geographic iformatio systems i cotrol programs for ochocerciasis i Guatemala. J. Bull Pa Am Health Orga. 27, (1993) 6. Clarke KC, Osleeb JR, Sherry JM, Meert JP, Larsso RW. : The use of remote sesig ad geographic iformatio systems i UNICEF's dracuculiasis (Guiea worm) eradicatio effort. J. Prev Vet Med.11, (1991) 7. Braddock M, Lapidus G, Cromley E, Cromley R, Burke G, Braco L. : Usig a geographic iformatio system to uderstad child pedestria ijury. Am J. Public Health. 84, (1994) 8. Queiroz, JW. Dias, GH, Nobre, ML, et al.: Geographic Iformatio Systems ad Applied Spatial Statistics Are Efficiet Tools to Study Hase's Disease (Leprosy) ad to Determie Areas of Greater Risk of Disease. J. America joural of tropical medicie ad hygiee. 82, (2010) 9. Bares S, Peck A. :Mappig the future of health care: GIS applicatios i Health care aalysis. J. Geographic Iformatio systems. 4, (1994) 10. Warteberg D, Greeberg M, Lathrop R. : Idetificatio ad characterizatio of populatios livig ear high-voltage trasmissio lies: a pilot study. J. Eviro Health Perspect.101, (1993) 11. Busgeeth, K.: The use of a spatial iformatio system i the maagemet of HIV/AIDS i South Africa, J. Iteratioal Joural of Health Geographics. 3, (2004)

Spatial Statistical Analysis in Cow Disease Monitoring Based on GIS

Spatial Statistical Analysis in Cow Disease Monitoring Based on GIS Lin Li 1, Yong Yang 2,*, Hongbin Wang 3, Jing Dong 1, Yujun Zhao 1, and Jianbin He 1 1 School of Animal Husbandry and Veterinary Medicine,