Accounting for space and uncertainty in real‐time location system‐derived contact networks

Abstract

Point data obtained from real‐time location systems (RTLSs) can be processed into animal contact networks, describing instances of interaction between tracked individuals. Proximity‐based definitions of interanimal “contact,” however, may be inadequate for describing epidemiologically and sociologically relevant interactions involving body parts or other physical spaces relatively far from tracking devices. This weakness can be overcome by using polygons, rather than points, to represent tracked individuals and defining “contact” as polygon intersections. We present novel procedures for deriving polygons from RTLS point data while maintaining distances and orientations associated with individuals' relocation events. We demonstrate the versatility of this methodology for network modeling using two contact network creation examples, wherein we use this procedure to create (a) interanimal physical contact networks and (b) a visual contact network. Additionally, in creating our networks, we establish another procedure to adjust definitions of “contact” to account for RTLS positional accuracy, ensuring all true contacts are likely captured and represented in our networks. Using the methods described herein and the associated R package we have developed, called contact, researchers can derive polygons from RTLS points. Furthermore, we show that these polygons are highly versatile for contact network creation and can be used to answer a wide variety of epidemiological, ethological, and sociological research questions. By introducing these methodologies and providing the means to easily apply them through the contact R package, we hope to vastly improve network‐model realism and researchers' ability to draw inferences from RTLS data.

Combining epidemiological and ecological methods to quantify social effects on Escherichia coli transmission

“…We used the contact package (v. 1.2.6), introduced in Farthing et al . [ 32 ] to clean point-location data and create contact and social networks. We used the transtat package (v. 0.3.4, available at github.com/ekenah/transtat [ 48 ]) for all pairwise hazard modelling and analysis.…”

“…8: 210328 transmission models using point-based locational data and represents a trade-off between the inclusion of biologically relevant behaviour and non-contact noise that can obscure actual contact patterns [30]. Here, we chose 0.71 m as our proximity-based contact threshold because it approximates the estimated maximum distance between two calves' tags during shoulder or chest allogrooming events (0.5 m), while accounting for the positional accuracy of our system [32]. Using location data processing procedures described by Dawson et al [30], we accepted a temporal sampling window of 10 s and created 1104 and 1608 hourly aggregated contact networks for our 2017 and 2018 herds, respectively.…”

“…All data processing and analyses were carried out in RStudio (v. 1.4.1103, RStudio Team, Boston, MA [55]) running R (v. 4.0.3, R Foundation for Statistical Computing, Vienna, Austria [56]). We used the contact package (v. 1.2.6), introduced in Farthing et al [32] to clean point-location data and create contact and social networks. We used the transtat package (v. 0.3.4, available at github.com/ekenah/ transtat [48]) for all pairwise hazard modelling and analysis.…”

“…This allows for contemporaneous observation of pathogen transmission between individuals. Because real-time location tracking methods can be used to generate inter-animal contact networks [28][29][30] and comparisons of observed contact rates can be used to predict sociality within tracked populations [31,32], combining transmission experiments with real-time location data collection provides a powerful framework for gaining inference into sociobehavioural drivers on microparasite transmission.…”

Corrigendum

“…In our paper, “Accounting for space and uncertainty in real‐time location system‐derived contact networks” ( Ecol & Evol 10(11):4702–4715), we presented a procedure for updating spatial threshold definitions to identify proximity‐based contacts from real‐time location data while accounting for real‐time location system accuracy. This is a simulation‐based procedure whereby researchers generate a distribution of n inter‐point distances between in‐contact point pairs, and choose an appropriate contact distance threshold that ensures a desired percentage of true positives are represented in generated contact networks.…”