Ascertainment and near-term forecasting of tick paralysis admissions

Tick paralysis is a leading cause of emergency veterinary admissions for Australian companion animals, often resulting in death if left untreated. Availability of timely information on periods of increased risk can help modulate behaviours that reduce exposures to ticks and improve awareness of owners for the need of preventative ectoparasite treatment. However, detection of trends in risk is hampered by the lack of clearly annotated historical records of tick paralysis. Natural Language Processing (NLP) of clinical records is required to first ascertain historical cases. Here we describe a platform to perform NLP on VetCompass Australia’s veterinary clinical records to accurately identify historical cases of canine tick paralysis where we make use of combine bespoke spellchecking and tokenization routines with a domain-expertise inspired clinical dictionary to identify important terms in free text indicative of a tick paralysis diagnosis. Resulting time series of tick paralysis cases are then analysed using Dynamic Generalised Additive Models to jointly estimate nonlinear distributed lag effects of environmental predictors and dynamic latent temporal processes that facilitate probabilistic near-term forecasts of tick paralysis risk. Our models forecast tick paralysis cases with exceptional accuracy while preserving epidemiological interpretability. We have designed an interactive online dashboard to showcase our data and modelling results so that we can refine the way we present probabilistic predictions to meet end-user requirements. We expect our data acquisition / modelling pipeline to act as a platform for developing early warning systems that can notify clinicians and pet owners about heightened risks of environmentally driven veterinary conditions.

August 16, 2022

2:14 PM – 2:20 PM

Online