Seasonality and pathogen transmission in pastoral cattle contact networks

Kimberly VanderWaal, Marie Gilbertson, Sharon Okanga, Brian F. Allan, Meggan E Craft

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

Capturing heterogeneity in contact patterns in animal populations is essential for understanding the spread of infectious diseases. In contrast to other regions of the world in which livestock movement networks are integral to pathogen prevention and control policies, contact networks are understudied in pastoral regions of Africa due to the challenge of measuring contact among mobile herds of cattle whose movements are driven by access to resources. Furthermore, the extent to which seasonal changes in the distribution of water and resources impacts the structure of contact networks in cattle is uncertain. Contact networks may be more conducive to pathogen spread in the dry season due to congregation at limited water sources. Alternatively, less abundant forage may result in decreased pathogen transmission due to competitive avoidance among herds, as measured by reduced contact rates. Here, we use GPS technology to concurrently track 49 free-roaming cattle herds within a semi-arid region of Kenya, and use these data to characterize seasonal contact networks and model the spread of a highly infectious pathogen. This work provides the first empirical data on the local contact network structure of mobile herds based on quantifiable contact events. The contact network demonstrated high levels of interconnectivity. An increase in contacts near to water resources in the dry season resulted in networks with both higher contact rates and higher potential for pathogen spread than in the wet season. Simulated disease outbreaks were also larger in the dry season. Results support the hypothesis that limited water resources enhance connectivity and transmission within contact networks, as opposed to reducing connectivity as a result of competitive avoidance. These results cast light on the impact of seasonal heterogeneity in resource availability on predicting pathogen transmission dynamics, which has implications for other free-ranging wild and domestic populations.

Original languageEnglish (US)
Article number170808
JournalRoyal Society Open Science
Volume4
Issue number12
DOIs
StatePublished - Dec 2017

Bibliographical note

Funding Information:
Ethics. Permission to conduct this research was granted by the National Environment Management Authority of Kenya, Kenya Wildlife Service, and the management of Ol Pejeta Conservancy. The study was approved by the Institutional Animal Care and Use Committee (Protocol No. 13330) and the Institutional Review Board (Protocol No. 13931) of the University of Illinois Urbana-Champaigne. No data on human subjects are presented in this manuscript. Data accessibility. Data and modelling code are deposited in the Data Repository for the University of Minnesota (DRUM: https://doi.org/10.13020/D60D42). Authors’ contributions. B.F.A. and S.O. designed and performed data collection in field. M.G. and K.V. performed the analysis of the movement, contact, network and spatial data. K.V. performed the modelling and wrote the manuscript. M.E.C. participated in interpretation of network and modelling data. All authors contributed to writing the manuscript and gave final approval for publication. Competing interests. We have no competing interests. Funding. This project was funded by NSF Grant CNH-1313822 to B.F.A.; M.G. was funded by the University of Minnesota Veterinary Summer Scholars program; M.E.C. was funded by National Science Foundation (DEB-1413925) and the University of Minnesota’s Office of the Vice President for Research and Academic Health Center Seed Grant. Acknowledgements. We are grateful to the management of Ol Pejeta Conservancy for permissions and support for data collection, especially Giles Prettejohn, Richard Van Aardt, Richard Vigne, Joseph Mathenge and the Ecological Monitoring Unit.

Keywords

  • Animal movement
  • Ecology
  • Infectious disease
  • Network analysis
  • Pathogen

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