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<lom:identifier>
  
<lom:catalog>DOI</lom:catalog>

  
<lom:entry>
  
<lom:langstring xml:lang="x-none">10.1038/s41467-024-49967-7</lom:langstring>

  
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</lom:identifier>

  
<lom:title>
  
<lom:langstring xml:lang="en">A One Health framework for exploring zoonotic interactions demonstrated through a case study</lom:langstring>

  
</lom:title>

  
<lom:description>
  
<lom:langstring xml:lang="en">The eco-epidemiology of zoonoses is often oversimplified to host-pathogen interactions while findings derived from global datasets are rarely directly transferable to smaller-scale contexts. Through a systematic literature search, we compiled a dataset of naturally occurring zoonotic interactions in Austria, spanning 1975-2022. We introduce the concept of zoonotic web to describe the complex relationships between zoonotic agents, their hosts, vectors, food, and environmental sources. The zoonotic web was explored through network analysis. After controlling for research effort, we demonstrate that, within the projected unipartite source-source network of zoonotic agent sharing, the most influential zoonotic sources are human, cattle, chicken, and some meat products. Analysis of the One Health 3-cliques (triangular sets of nodes representing human, animal, and environment) confirms the increased probability of zoonotic spillover at human-cattle and human-food interfaces. We characterise six communities of zoonotic agent sharing, which assembly patterns are likely driven by highly connected infectious agents in the zoonotic web, proximity to human, and anthropogenic activities. Additionally, we report a frequency of emerging zoonotic diseases in Austria of one every six years. Here, we present a flexible network-based approach that offers insights into zoonotic transmission chains, facilitating the development of locally-relevant One Health strategies against zoonoses.</lom:langstring>

  
</lom:description>

  
<lom:language>eng</lom:language>

  
<lom:keyword>
  
<lom:langstring xml:lang="en">Bacterial infection; Ecological networks; Epidemiology; Parasitic infection; Viral infection</lom:langstring>

  
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<lom:datetime>2024-09-06T09:59:15.190Z</lom:datetime>

  
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N:Desvars-Larrive;Amélie;
FN:Amélie Desvars-Larrive
X-ORCID:https://orcid.org/0000-0001-7671-696X
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<lom:centity>
  
<lom:vcard>BEGIN:VCARD
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N:Käsbohrer;Annemarie;
FN:Annemarie Käsbohrer
X-ORCID:https://orcid.org/0000-0003-2966-2713
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<lom:centity>
  
<lom:vcard>BEGIN:VCARD
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N:Joachim;Anja;
FN:Anja Joachim
X-ORCID:https://orcid.org/0000-0003-3082-6885
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<lom:vcard>BEGIN:VCARD
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N:Yang;Liuhuaying;
FN:Liuhuaying Yang
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<lom:vcard>BEGIN:VCARD
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N:Vogl;Anna Elisabeth;
FN:Anna Elisabeth Vogl
X-ORCID:https://orcid.org/0009-0007-9813-3854
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<lom:vcard>BEGIN:VCARD
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N:Puspitarani;Gavrila Amadea;
FN:Gavrila Amadea Puspitarani
X-ORCID:https://orcid.org/0000-0002-8826-5606
END:VCARD</lom:vcard>

  
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