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

Abstract

Enteric microparasites like Escherichia coli use multiple transmission pathways to propagate within and between host populations. Characterizing the relative transmission risk attributable to host social relationships and direct physical contact between individuals is paramount for understanding how microparasites like E. coli spread within affected communities and estimating colonization rates. To measure these effects, we carried out commensal E. coli transmission experiments in two cattle ( Bos taurus ) herds, wherein all individuals were equipped with real-time location tracking devices. Following transmission experiments in this model system, we derived temporally dynamic social and contact networks from location data. Estimated social affiliations and dyadic contact frequencies during transmission experiments informed pairwise accelerated failure time models that we used to quantify effects of these sociobehavioural variables on weekly E. coli colonization risk in these populations. We found that sociobehavioural variables alone were ultimately poor predictors of E. coli colonization in feedlot cattle, but can have significant effects on colonization hazard rates ( p ≤ 0.05). We show, however, that observed effects were not consistent between similar populations. This work demonstrates that transmission experiments can be combined with real-time location data collection and processing procedures to create an effective framework for quantifying sociobehavioural effects on microparasite transmission.

A wrap-around movement path randomization method to distinguish social and spatial drivers of animal interactions

“…Spiegel et al (2016) conducted a thorough analysis of the effect of the time-window size on the performance of the path shuffling approach. Here we shuffle paths over the entire tracking period, because this is how path shuffling method has been implemented in most cases (Jones et al 2020;Tichon et al 2020;Farthing et al 2021) and because even the smallest time-shift ranges of the wrap-around method allow for the possibility of interchanging trajectory segments at either end of the tracking period. Future work could examine how the time window for path shuffling and the range of the wrap-around method correspond to each other.…”

“…Path shuffling also allows for identifying whether the locations of encounters differ from those expected by chance (Spiegel et al 2018). The path shuffling method has been implemented to understand patterns and drivers of social interactions in cows (Farthing et al 2021), hyenas (Tichon et al 2020, colonial seabirds (Jones et al 2020), caribou (Peignier et al 2019), and sleepy lizards (Spiegel et al 2018). It has been integrated into the widely-used R package `spatsoc` (Robitaille et al 2019), and a modified version also appears in the `contact`R package (Farthing et al 2020).…”