UK - The largest simulation to date of the numbers of cattle and badgers infected with tuberculosis (TB) casts serious doubts about the extent to which badgers cause TB in cattle, according to research from Queen Mary University of London (QMUL).
Using a mathematical model that combines a huge number of cattle and badgers that have TB, the researchers were able to quantify the relationship between the two animals and use a big data approach to show that the route of infection for cattle is from other cattle rather than from other species. Reciprocally, badgers are mainly infected by other infected badgers.
“If badgers are causing TB in cattle, we would see a similar pattern of infection in both species, however our analysis reveals that this isn’t the case and could have implications for a strategy to vaccinate badgers, as an efficient control strategy if policymakers were to pursue this option,” said lead author Dr Aristides Moustakas from QMUL’s School of Biological and Chemical Sciences.
The modelling exercise in this new research paper follows over a million cattle and 50,000 badgers over different scenarios to understand how the movement of one species affects the transmission of TB in the other.
Dr Moustakas adds: “There is little geographical overlap between farms with infected cattle and setts with infected badgers, and cycles of infections between the two species are not synchronised. Also, the spatial aggregation pattern of TB in cattle and badgers is different – in badgers, we find that the disease is found in clusters whereas in cattle the disease is much more random and dispersed.”
The research suggests that an efficient way to vaccinate badgers might be to follow the spatial pattern of TB infections, for example by identifying the hotspots where the disease is concentrated. This targeted approach would save labour and costs to control the spread of the disease.
A big-data spatial, temporal and network analysis of bovine tuberculosis between wildlife (badgers) and cattle’ by A. Moustakas and M.R. Evans will be published in the journal Stochastic Environmental Research and Risk Assessment on Tuesday 27 September 2016.
TheCattleSite News Desk