A big-data spatial, temporal and network analysis of bovine tuberculosis between wildlife (badgers) and cattle

Abstract

Bovine tuberculosis (TB) poses a serious threat for agricultural industry in several countries, it involves potential interactions between wildlife and cattle and creates societal problems in terms of human-wildlife conflict. This study addresses connectedness network analysis, the spatial, and temporal dynamics of TB between cattle in farms and the European badger (Meles meles) using a large dataset generated by a calibrated agent based model. Results showed that infected network connectedness was lower in badgers than in cattle. The contribution of an infected individual to the mean distance of disease spread over time was considerably lower for badger than cattle; badgers mainly spread the disease locally while cattle infected both locally and across longer distances. The majority of badger-induced infections occurred when individual badgers leave their home sett, and this was positively correlated with badger population growth rates. Point pattern analysis indicated aggregation in the spatial pattern of TB prevalence in badger setts across all scales. The spatial distribution of farms that were not TB free was aggregated at different scales than the spatial distribution of infected badgers and became random at larger scales. The spatial cross correlation between infected badger setts and infected farms revealed that generally infected setts and farms do not coexist except at few scales. Temporal autocorrelation detected a two year infection cycle for badgers, while there was both within the year and longer cycles for infected cattle. Temporal cross correlation indicated that infection cycles in badgers and cattle are negatively correlated. The implications of these results for understanding the dynamics of the disease are discussed.

Evaluating Hypotheses of Plant Species Invasions on Mediterranean Islands: Inverse Patterns between Alien and Endemic Species

“…Technological developments in smart sensors, social networks, and digital maps, spatio-temporal data are more available than ever before (Reis et al, 2015;Miyazaki et al, 2016;Niphadkar and Nagendra, 2016) and ecology in the big data era needs to integrate novel methods for their analysis (Moustakas, 2017). The availability of large datasets poses great challenges in data analytics (Moustakas and Evans, 2017) but also increased availability of computing power facilitates the use of computationally-intensive methods for the analysis of such data in ecology (Moustakas and Evans, 2015).…”

Dynamic, spatial models of parasite transmission in wildlife: Their structure, applications and remaining challenges

“…Modelling approaches for rabies have evolved from non‐spatial compartmental models (Anderson, Jackson, May, & Smith, ) to reaction‐diffusion systems (Murray & Seward, ; Murray, Stanley, & Brown, ), network (Smith et al., ), metapopulation (Haydon et al., ) and individual‐based approaches (Tischendorf et al., ). For bTB in badgers ( Meles meles ), approaches have ranged from lattice (Hardstaff, Bulling, Marion, Hutchings, & White, ; White & Harris, ) to individual‐based approaches (Moustakas & Evans, ; Smith, Cheeseman, Clifton Hadley, & Wilkinson, ; Wilkinson, Smith, Delahay, & Cheeseman, ), including recent attempts to derive empirical contact networks between badger and cattle (Böhm, Hutchings, & White, ). In general, however, there is no comprehensive comparison of these various modelling methods, leaving researchers to make relatively ad hoc decisions about when to include spatial structure and how much spatial structure to include when modelling host–pathogen systems in natural populations.…”

Spatial Downscaling of Alien Species Presences Using Machine Learning

“…Recent advances in remote sensing, social networks, and digital technology resulted in the availability of large spatially and temporally explicit datasets (Moustakas, 2017). Ecology, epidemiology, and biogeography need to employ novel methods for big data analytics combing statistics and computer science, as the analysis of such datasets requires advanced methods for compiling the data, their visualization, and their analyses (Moustakas, 2017;Moustakas and Evans, 2017).…”