High-resolution location data: Modeling and applications in veterinary medicine
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1
North Carolina State University, United States
Improvements in animal-tracking technologies have dramatically enhanced our ability to continuously collect individual-level location data. Real-time location systems (RTLS) based on radio frequency identification and global positioning system technologies are becoming increasingly accurate, temporally precise, and energy efficient, allowing researchers to generate large animal-location data sets that capture fine-scale movements. These big data are highly versatile and can be used in conjunction with other geographic data (e.g., remotely-sensed data) to answer a wide variety of research and applied questions pertaining to individual- and population-level animal behaviors, resource use, and disease transmission. Often, answering these questions first requires processing location data into animal-to-animal or animal-environment contact networks. To generate contact networks from RTLS data, two important conceptual and computational challenges need to be addressed: 1) contacts among animals (and their environment) must be explicitly defined, and 2) computational infrastructure must be developed to process location-based data into the desired networks of contacts. To generate contacts, location records need to be temporally aligned and aggregated; then spatial thresholds and temporal duration of a contact need to be defined to generate point-based contacts. Further, by processing point-based datasets into polygons, and considering the directionality of movement, we can generate polygon-based contacts that delineate contacts between different body regions.
Using RTLS data collected continuously at 5 to 10 second intervals on cattle, we show how RTLS data is used to build dynamic networks of animal-to-animal and animal-environment contacts. We show how the animal contact network is highly variable and dynamic, both in terms of the network topology and individual number of contacts, and that the animal-animal and animal-environment networks are highly coupled and synchronized. These issues are largely neglected in disease transmission models, which often assume static networks and independent transmission pathways. In addition, path-based randomization methods can be combined with RTLS data to derive social metrics and networks of “association” and “aversion” relationships between cattle dyads to describe the social structure of the animals. These social metrics can be further used to study how social behavior influence the transmission of pathogens among animals. We have created the R package “contact”, which contains a variety of functions for the processing of RTLS data into point- and polygon-based contact networks. This user-friendly package also contains tools for the analysis of contact data, including novel algorithms evaluating potential sociality between individuals. Lastly, outputs from this package can be combined with other data to gain inference into contact-associated patterns. For example, we are using concurrently collected video-footage and RTLS data to develop mathematical functions relating observed social behavior to contact metrics in feedlot herds. In summary, RTLS data can provide a wealth of information into animal behavior and disease transmission.
Acknowledgements
This work was supported by U.S. National Institute of Health (NIH) grant R01GM117618 as part of the joint National Science Foundation-NIH-United States Department of Agriculture Ecology and Evolution of Infectious Disease program
Keywords:
High-resolution location data,
networks,
agent-based model,
Disease transmission dynamics,
Contact networks
Conference:
GeoVet 2019. Novel spatio-temporal approaches in the era of Big Data, Davis, United States, 8 Oct - 10 Oct, 2019.
Presentation Type:
Keynote
Topic:
Spatial-explicit or spatio-temporal network analysis
Citation:
Lanzas
C,
Farthing
T and
Dawson
D
(2019). High-resolution location data: Modeling and applications in veterinary medicine.
Front. Vet. Sci.
Conference Abstract:
GeoVet 2019. Novel spatio-temporal approaches in the era of Big Data.
doi: 10.3389/conf.fvets.2019.05.00010
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Received:
30 Jul 2019;
Published Online:
27 Sep 2019.
*
Correspondence:
Prof. Cristina Lanzas, North Carolina State University, Raleigh, United States, clanzas@ncsu.edu