Rewiring cattle movements to limit infection spread

Abstract

AbstractThe cattle tracing databases set up over the past decades in Europe have become major resources for representing demographic processes of livestock and assessing potential risk of infections spreading by trade. The herds registered in these databases are parts of a network of commercial movements, which can be altered to lower the risk of disease transmission. In this study, we developed an algorithm aimed at reducing the number of infected animals and herds, by rewiring specific movements responsible for trade flows from high-to low-prevalence herds. The algorithm was coupled with a generic computational model describing infection spread within and between herds, based on data extracted from the French cattle movement tracing database (BDNI). This model was used to simulate a wide array of infections, with either a recent outbreak (epidemic) or an outbreak that occurred five years earlier (endemic), on which the performances of the rewiring algorithm were explored. Results highlighted the effectiveness of rewiring in containing infections to a limited number of herds for all scenarios, but especially if the outbreak was recent and if the estimation of disease prevalence was frequent. Further analysis revealed that the key parameters of the algorithm affecting infection outcome varied with the infection parameters. Allowing any animal movement from high to low-prevalence herds reduced the effectiveness of the algorithm in epidemic settings, while frequent and fine-grained prevalence assessments improved the impact of the algorithm in endemic settings. Overall, our approach, which focuses on a few commercial movements, has led to substantial improvements in the control of a targeted disease, although changes in the network structure should be monitored for potential vulnerabilities to other diseases. Due to its generality, the developed rewiring algorithm could be applied to any network of controlled individual movements liable to spread disease.