Effects of trading networks on the risk of bovine tuberculosis incidents on cattle farms in Great Britain

Abstract

Trading animals between farms and via markets can provide a conduit for spread of infections. By studying trading networks, we might better understand the dynamics of livestock diseases. We constructed ingoing contact chains of cattle farms in Great Britain that were linked by trading, to elucidate potential pathways for the transmission of infection and to evaluate their effect on the risk of a farm experiencing a bovine tuberculosis (bTB) incident. Our findings are consistent with variation in bTB risk associated with region, herd size, disease risk area and history of previous bTB incidents on the root farm and nearby farms. However, we also identified effects of both direct and indirect trading patterns, such that connections to more farms in the England High-Risk Area up to three movements away from the root farm increased the odds of a bTB incident, while connections with more farms in the England Low-Risk Area up to eight movements away decreased the odds. Relative to other risk factors for bTB, trading behaviours are arguably more amenable to change, and consideration of risks associated with indirect trading, as well direct trading, might therefore offer an additional approach to bTB control in Great Britain.

Capturing complex interactions in disease ecology with simplicial sets

“…Spatial and movement networks have been used to identify patterns of landscape connectivity and dispersal (Bodin & Norberg, 2007; Fletcher et al, 2011) that are critical to disease spread between groups and through populations (Schreiber & Lloyd‐Smith, 2009; White et al, 2017a). Movement networks have been especially valuable in the study of livestock infections (Fielding et al, 2020; Kao et al, 2007; Kiss et al, 2006). Meanwhile, ecological networks help capture host‐pathogen (or host–parasite) interactions, elucidating community ecology underlying spillover and thus playing an important role in forecasting zoonotic risk (Albery, Becker, et al, 2021; Carlson et al, 2021; Poisot et al, 2021; Wardeh et al, 2021).…”

Potential Transmission of Zoonotic Tuberculosis Disease from Livestock to Humans

“…Zoonotik tuberkulosis diketahui dapat terjadi pada ternak sapi (Biru et al 2014;Shaheenur Islam et al 2020;), kambing (Higino et al 2011;Rahman et al 2013), domba (Munos-mendoza et al 2015Infanteslorenzo et al 2019) dan babi (Bailey et al2013;Lipiec et al 2019). Tuberkulosis pada sapi dilaporkan terjadi di negara Algeria (Naima et al 2011), Bangladesh (Shaheenur Islam et al 2020, Britania (Fielding et al 2020), Egypt (Hassanain et al 2009, Eritrea (Ghebremariam et al 2018), Ethiopia (Biru et al 2014;Kassa et al 2015;Habitu et al 2019;Dejene et al 2016;Kemal et al 2019), Indonesia (Mahatmi et al 2014;Daulay 2015), Morocco (Yahyaoui-Azami et al 2017, Niger (Boukary et al 2012) Perbedaan angka prevalensi berdasarkan pada level ternak dan kawanan ternak, bergantung pada jumlah sampel ternak serta kondisi peternakan daerah yang digunakan penelitian.…”

Characterization of potential superspreader farms for bovine tuberculosis: A review

“…These very extensive chains of farms aggregated over a period of five years have been associated with an increased risk of M. bovis infection in French cattle herds (Palisson et al., 2016), showing that chain magnitude may be useful in predicting which farms might be more likely to spread chronic infections. British cattle herds with more high‐risk farms in their contact chains aggregated over a three year period were associated with increased odds of bTB incidents (Fielding, McKinley, Delahay, Silk, & McDonald, 2020). In choosing the time period over which to study the network, the independent timescales of the movement network and the pathogen should be considered (Kao, Green, Johnson, & Kiss, 2007).…”

“…Spatial clustering analysis of bTB data from England in 2005 showed only weak evidence for clustering of disease on a county level (Green & Cornell, 2005). However, herd‐level risk factor studies have found that risks of bTB are greater for farms whose neighbours have a history of infection (Fielding et al., 2020; Skuce, Allen, & McDowell, 2012). A study of M. bovis transmission in France, where infection is rare, combined the cattle movement network with a ‘spatial neighbourhood’ based on geographic proximity of farms (Palisson et al., 2016).…”