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Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases2010; 10(7); 1013-1018; doi: 10.1016/j.meegid.2010.06.014

Geographic distribution modeling and spatial cluster analysis for equine piroplasms in Greece.

Abstract: Maximum entropy ecological niche modeling and spatial scan statistic were utilized to predict the geographic range and to investigate clusters of infections for equine piroplasms in Greece, using the Maxent and SaTScan programs, respectively. The eastern half of the country represented the culminating area with high probabilities (p>0.75) of presence of equine piroplasms and encompassed most regions with high concentration of equid host populations. The most important environmental factor that contributed to the ecological niche modeling was land cover followed by temperature. Significant clusters (p<0.0001) were detected for Babesia caballi and Theileria equi infections in North and Central regions of Greece, respectively, which have significant equine populations. Maximum entropy ecological niche modeling and spatial scan statistic have proved to be useful tools for the surveillance of animal diseases.
Copyright 2010 Elsevier B.V. All rights reserved.
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Publication Date: 2010-07-01 PubMed ID: 20601173DOI: 10.1016/j.meegid.2010.06.014Google Scholar: Lookup
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Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

This research article focuses on predicting the geographical range, and researching clusters of equine piroplasms (a parasitic disease affecting horses) infection in Greece by employing maximum entropy ecological modeling and spatial scan statistic.

Research Methodology

  • The researchers used a form of ecological modeling known as maximum entropy (MaxEnt) and spatial scan statistics (SaTScan). These techniques are used in predicting geographical patterns and detecting spatial clusters, respectively.
  • MaxEnt is based on the principle of maximum entropy and can predict the spread and likely habitats of various species, including pathogens. It achieves this by taking into account environmental factors like land cover and temperature.
  • SaTScan is a spatial analysis tool that scans an area spatially and temporally for statistically significant clusters of disease incidence.

Findings and Observations

  • The study revealed that the eastern side of Greece had a higher probability (greater than 0.75) of equine piroplasm presence.
  • This eastern segment also housed most of the regions with a high concentration of equid (horse family) host populations.
  • Land cover and temperature were the most significant environmental factors contributing to the ecological niche modeling.
  • Significant clusters of two types of equine piroplasms, Babesia caballi and Theileria equi, were detected in the North and Central parts of Greece. These regions have considerable equine populations.
  • The findings signal potential high-risk zones for equine piroplasms in Greece which will help focus preventative measures and control methods more efficiently.

Implications of the Study

  • The maximum entropy ecological niche modeling and spatial scan statistic have proven to be effective in mapping the geographical distribution of equine piroplasms in Greece.
  • These tools can be beneficial for the surveillance of animal diseases, guiding preventive and control measures by identifying areas of high risk.
  • The study provides valuable insights for veterinary public health strategy planners, aiding them in implementing targeted interventions.

Cite This Article

APA
Kouam MK, Masuoka PM, Kantzoura V, Theodoropoulos G. (2010). Geographic distribution modeling and spatial cluster analysis for equine piroplasms in Greece. Infect Genet Evol, 10(7), 1013-1018. https://doi.org/10.1016/j.meegid.2010.06.014

Publication

Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases
ISSN: 1567-7257
NlmUniqueID: 101084138
Country: Netherlands
Language: English
Volume: 10
Issue: 7
Pages: 1013-1018

Researcher Affiliations

Kouam, Marc K
  • Department of Anatomy and Physiology of Farm Animals, Faculty of Animal Science and Hydrobiology, Agricultural University of Athens, 75 Iera Odos, Votanikos, Athens 11855, Greece.
Masuoka, Penny M
    Kantzoura, Vaia
      Theodoropoulos, Georgios

        MeSH Terms

        • Animals
        • Babesia / classification
        • Babesia / isolation & purification
        • Babesiosis / epidemiology
        • Babesiosis / parasitology
        • Babesiosis / veterinary
        • Cluster Analysis
        • Environment
        • Greece / epidemiology
        • Horse Diseases / epidemiology
        • Horse Diseases / parasitology
        • Horses
        • Models, Biological
        • Theileria / classification
        • Theileria / isolation & purification
        • Theileriasis / epidemiology
        • Theileriasis / parasitology

        Citations

        This article has been cited 6 times.
        1. Giubega S, Ilie MS, Luca I, Florea T, Dreghiciu C, Oprescu I, Morariu S, Dărăbuș G. Seroprevalence of Anti-Theileria equi Antibodies in Horses from Three Geographically Distinct Areas of Romania. Pathogens 2022 Jun 9;11(6).
          doi: 10.3390/pathogens11060669pubmed: 35745523google scholar: lookup
        2. Efstratiou A, Karanis G, Karanis P. Tick-Borne Pathogens and Diseases in Greece. Microorganisms 2021 Aug 14;9(8).
          doi: 10.3390/microorganisms9081732pubmed: 34442811google scholar: lookup
        3. Zannou OM, Ouedraogo AS, Biguezoton AS, Abatih E, Coral-Almeida M, Farougou S, Yao KP, Lempereur L, Saegerman C. Models for Studying the Distribution of Ticks and Tick-Borne Diseases in Animals: A Systematic Review and a Meta-Analysis with a Focus on Africa. Pathogens 2021 Jul 14;10(7).
          doi: 10.3390/pathogens10070893pubmed: 34358043google scholar: lookup
        4. Guidi E, Pradier S, Lebert I, Leblond A. Piroplasmosis in an endemic area: analysis of the risk factors and their implications in the control of Theileriosis and Babesiosis in horses. Parasitol Res 2015 Jan;114(1):71-83.
          doi: 10.1007/s00436-014-4161-9pubmed: 25280516google scholar: lookup
        5. Nogareda C, Jubert A, Kantzoura V, Kouam MK, Feidas H, Theodoropoulos G. Geographical distribution modelling for Neospora caninum and Coxiella burnetii infections in dairy cattle farms in northeastern Spain. Epidemiol Infect 2013 Jan;141(1):81-90.
          doi: 10.1017/S0950268812000271pubmed: 22370223google scholar: lookup
        6. Jongejan F, Du C, Papadopoulos E, Blanda V, Di Bella S, Cannella V, Guercio A, Vicari D, Tirosh-Levy S, Steinman A, Baneth G, van Keulen S, Hulsebos I, Berger L, Wang X. Diagnostic performance of a rapid immunochromatographic test for the simultaneous detection of antibodies to Theileria equi and Babesia caballi in horses and donkeys. Parasit Vectors 2024 Mar 28;17(1):160.
          doi: 10.1186/s13071-024-06253-1pubmed: 38549117google scholar: lookup
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