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      Global and local drivers of Echinococcus multilocularis infection in the western Balkan region

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          Abstract

          The cestode , Echinococcus multilocularis, is one of the most threatening parasitic challenges in the European Union. Despite the warming climate, the parasite intensively spread in Europe's colder and warmer regions. Little is known about the expansion of E. multilocularis in the Balkan region. Ordinary least squares, geographically weighted and multi-scale geographically weighted regressions were used to detect global and local drivers that influenced the prevalence in red foxes and golden jackals in the southwestern part of Hungary. Based on the study of 391 animals, the overall prevalence exceeded 18% (in fox 15.2%, in jackal 21.1%). The regression models revealed that the wetland had a global effect (β = 0.391, p = 0.006). In contrast, on the local scale, the mean annual precipitation (β = 0.285, p = 0.008) and the precipitation seasonality (β = − 0.211, p = 0.014) had statistically significant effects on the infection level. The geospatial models suggested that microclimatic effects might compensate for the disadvantages of a warmer Mediterranean climate. This study calls attention to fine-scale analysis and locally acting environmental factors, which can delay the expected epidemic fade-out. The findings of our study are suggested to consider in surveillance strategies.

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              Multicollinearity and misleading statistical results

              Jong Kim (2019)
              Multicollinearity represents a high degree of linear intercorrelation between explanatory variables in a multiple regression model and leads to incorrect results of regression analyses. Diagnostic tools of multicollinearity include the variance inflation factor (VIF), condition index and condition number, and variance decomposition proportion (VDP). The multicollinearity can be expressed by the coefficient of determination (Rh 2) of a multiple regression model with one explanatory variable (Xh ) as the model’s response variable and the others (Xi [i≠h] as its explanatory variables. The variance (σh 2) of the regression coefficients constituting the final regression model are proportional to the VIF ( 1 1 - R h 2 ) . Hence, an increase in Rh 2 (strong multicollinearity) increases σh 2. The larger σh 2 produces unreliable probability values and confidence intervals of the regression coefficients. The square root of the ratio of the maximum eigenvalue to each eigenvalue from the correlation matrix of standardized explanatory variables is referred to as the condition index. The condition number is the maximum condition index. Multicollinearity is present when the VIF is higher than 5 to 10 or the condition indices are higher than 10 to 30. However, they cannot indicate multicollinear explanatory variables. VDPs obtained from the eigenvectors can identify the multicollinear variables by showing the extent of the inflation of σh 2 according to each condition index. When two or more VDPs, which correspond to a common condition index higher than 10 to 30, are higher than 0.8 to 0.9, their associated explanatory variables are multicollinear. Excluding multicollinear explanatory variables leads to statistically stable multiple regression models.

                Author and article information

                Contributors
                gabor.nagy.oh@gmail.com
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                1 December 2023
                1 December 2023
                2023
                : 13
                : 21176
                Affiliations
                [1 ]One Health Working Group, Institute of Physiology and Animal Nutrition, Kaposvár Campus, Hungarian University of Agriculture and Life Sciences, ( https://ror.org/01394d192) Guba S. U. 40., Kaposvár, 7400 Hungary
                [2 ]Institute of Wildlife Biology and Management, Faculty of Forestry, University of Sopron, ( https://ror.org/05nj7my03) Sopron, 9400 Hungary
                [3 ]Zselic Wildlife Estate, Somogy County Forest Management and Wood Industry Share Co. Ltd., Kaposvár, 7400 Hungary
                [4 ]Institute of Geomatics and Civil Engineering, Faculty of Forestry, University of Sopron, ( https://ror.org/05nj7my03) Sopron, 9400 Hungary
                Article
                46632
                10.1038/s41598-023-46632-9
                10692075
                38040783
                32b9a8ec-7cbf-44ee-ae3a-cb4a0c68a685
                © The Author(s) 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 1 September 2023
                : 3 November 2023
                Funding
                Funded by: Hungarian National Laboratory project
                Award ID: RRF-2.3.1-21-2022-00007
                Award ID: RRF-2.3.1-21-2022-00007
                Award Recipient :
                Categories
                Article
                Custom metadata
                © Springer Nature Limited 2023

                Uncategorized
                zoology,diseases
                Uncategorized
                zoology, diseases

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