Evandro Konzen (University of Warwick)

Title: Detecting superspreaders in wildlife reservoirs of disease
 

Abstract: To better understand the dynamics of infectious diseases of wildlife, it is crucial to be able to fit dynamic transmission models to observed data in a robust and efficient way, in order to estimate key epidemiological parameters and generate well calibrated predictive information. In practice, epidemiological events are at best only partially observed, and as such it is necessary to infer missing information alongside the model parameters as part of the inference routine, requiring computationally intensive inference algorithms where computational load increases non-linearly with population size and with increased dimensionality of the hidden states.
With this in mind, we implement a recently proposed individual forward filtering backward sampling algorithm to fit a complex individual-based epidemic model to data from a large-scale longitudinal study of bovine tuberculosis in badgers. This data set, from Woodchester Park in south-west England, comprises >2,000 badgers across 34 social groups over a 40-year period. We deal with many complexities typical to endemic wildlife disease systems: incomplete sampling of individuals over time (through capture-mark-recapture events), the use of multiple diagnostic tests, spatial meta-population structures, and non-Markovian demographic aspects such as age-dependent mortality rates (with censoring), all alongside a hidden stochastic compartmental model of disease spread. The method produces full posterior distributions for the parameters, and predictive distributions for the hidden states over time for each individual, and fits in just a few hours on a desktop machine.
We also propose a novel individual-level reproduction number which accounts for major sources of uncertainty of the disease system, and from it provide quantitative evidence for the presence of superspreader badgers in Woodchester Park. The inference framework is very flexible, and could be applied to other individual-level disease systems, and we will discuss future extensions to explore further important epidemiological questions.