A study led by Senior Assistant Professor Aristides Moustakas, a researcher from the Institute for Applied Data Analytics, provides insight into understanding the spread of bovine Tuberculosis using data analytics for disease control strategies.

Can rare, extreme (abrupt) events synchronize geographically distinct populations?

Abrupt or extreme events are generally hard to study as by definition they do not occur frequently. In the Arctic, extreme weather events synchronized population fluctuations across herbivores and caused lagged correlations with their predator, the arctic fox. Synchrony after abrupt events has been reported also in climate sciences. Do abrupt events synchronize the spread of diseases? Can small amendments in the control strategies of diseases produce chaotic patterns and synchronize infected populations? This is of key importance for their eradication and control as disease synchronization is potentially pandemic.

Routine testing for the harmful pathogen Bovine Tuberculosis (bTB) was suspended briefly during the foot and mouth disease epidemic of 2001 in Great Britain. We utilize bTB incidence data to demonstrate how the short-lasting abrupt lapse in management can alter epidemiological parameters, including the rate of new infections and duration of infection cycles.

We show that the changes in epidemiological parameters during the short-lasting unmanaged time while testing was suspended, can increase new infections markedly, can have long-lasting effects, and generate longer-term temporal infection cycles. Infection cycles shifted from annual to 4-year after testing interruption. Spatial synchrony of new infections between different GB regions after the interruption of cattle testing increased. These effects persisted for over 15 years after the abrupt testing interruption. After annual testing was introduced in some GB regions, new infections have become more de-synchronised, a result also confirmed by a stochastic model. This study shows that amendments in the epidemiological parameters lead to chaotic patterns and that abrupt events synchronise disease dynamics.

Reference:

Moustakas, A., Evans, M.R., Daliakopoulos, I.N. and Markonis, Y. (2018) Abrupt events and population synchrony in the dynamics of Bovine Tuberculosis. Nature Communications, 9: 2821

Techniques employed:

We have employed the latest state-of-the art techniques in data analytics applied to one of the most timely problems: the spread of bovine Tuberculosis disease in GB using publicly available epidemiological incidence data. Our analysis includes spatio-temporal statistics, wavelets, network analysis, and Bayesian time series intervention analysis to assign predictive causality between the data and the outputs of a stochastic simulation model (agent based model).