Spatial distribution and epidemiology of Echinococcus granulosus infection in sheep and goats slaughtered 1 in a hyperendemic European Mediterranean area

Background: Cystic echinococcosis (CE) is a parasitic zoonosis caused by the larval stage of Echinococcus 21 granulosus , highly widespread in livestock, particularly sheep and goats. This study aimed to evaluate the spatial 22 distribution of CE in sheep and goats slaughtered in a hyperendemic Mediterranean area. 23 Methods: A survey was conducted in Basilicata region (southern Italy) from 2014 to 2019. A total of 1454 animals 24 (1265 sheep and 189 goats) from 824 farms were examined for hydatid cysts detection by visual inspection, palpation 25 and incision of target organs. All the CE cysts were counted and classified into five morphostructural types 26 (unilocular, multisepted, calcified, caseous and hyperlaminated). The molecular analysis was performed on 50 cysts. 27 For spatial analysis, kriging interpolation method was used to create risk maps, while the clustering was assessed by 28 Moran’s I test. 29 Results: CE prevalence of 72.2% (595/824) and 58.4% (849/1454) were observed at the farm and animal level, 30 respectively, with higher values in sheep (62.9%) than goats (28.0%). The liver and lungs were the most frequently 31 infected organs both in sheep and goats. Most of recovered cysts belonged to the calcified and multisepted 32 morphotypes. All the isolates were identified as E. granulosus sensu stricto (genotypes G1-G3). Spatial distribution 33 showed a moderate clustering of positive animals. 34 Conclusions: The findings of this study can be used to better understand the eco-epidemiology of echinococcosis 35 and to improve the CE surveillance and prevention programs in regions highly endemic for CE. This study is part of a research project concerning the disease mapping caused by viral, bacterial and other parasitic infections found in ruminants in the Basilicata region using GIS. These maps are intended to be used in control programs to prevent and control CE in ruminants. In this context, a multidisciplinary program using a One Health perspective is required in order to control the transmission of E. granulosus . Over eight years, the EchinoCamp project demonstrated that the reduction of E. granulosus infection rates of dogs, humans and livestock (e.g. a decrease of up to 30% was observed in sheep) is feasible in Campania region, an endemic area of the Mediterranean [10].


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The global burden of CE has been estimated at approximately 1 million Disability Adjusted Life Years (DALYs) 58 and the world's livestock industry loss has been estimated around $3 billion a year [5, 6].

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Echinococcus granulosus is a cosmopolitan species, but it is mainly widespread in rural areas of central

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The first step produced empirical semi-variograms, which represented half of the mean square difference 108 between pairs of sampling locations (Equation 1). Where partial sill (s ≥ 0), range (r ≥ 0) and power (0 ≤ e ≤ 2) parameters are to be estimated. If e = 2, the semi-120 variogram model is Gaussian. This model is more flexible and retains desirable properties.

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The second step involved estimation mapping to predict the presence or absence of disease in an unknown 122 location. Indicator kriging was used to estimate mapping distributions under a given threshold (zk) [18]. The resulting 123 data were interpreted as values between zero and one. If the value is nearly one, it is considered to be positive and, 124 conversely, if the value is nearly zero, it is considered to be negative. The indicator kriging function used is given Where m is the power and dij represents the distance between region i and region j.

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The global spatial autocorrelation index, Moran's I, was then calculated. There were n area units in the 137 study area, and the observed values on the I unit were Xi. The mean value of the observation variable in the N unit 138 was X. Wij was a spatial weight matrix. Thus, Moran's I was defined as: The Ii value is positive, this area presents high incidence. When Ii is negative, this area has low incidence.

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All analyses were performed using the ESRI ArcGIS ArcMap 10.6 software.

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Therefore, the areas with low and high clusters of cases identified in the present study (Fig. 2)