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Forecasting for outbreaks of vector-borne diseases: a data assimilation approach

Research output: Contribution to conference - Without ISBN/ISSN Conference paper

Published
Publication date15/04/2016
<mark>Original language</mark>English

Abstract

In August 2012, the first case of a novel strain of /Theileria orientalis/ (Ikeda) was discovered in a dairy herd near Auckland, New Zealand. The strain was unusually pathogenic, causing haemolytic anaemia in up to 35% of animals within an infected herd. In the ensuing months, more cases were discovered in a pattern that suggested wave-like spread down New Zealand’s North Island. Theileria orientalis is a blood-borne parasite of cattle, which is transmitted by the tick vector /Haemaphysalis longicornis/. This tick was known to exist in New Zealand, but although its behaviour and life cycle were known from laboratory experiments surprisingly little was known about its country-wide distribution. Predicting the spread of /T. orientalis/ (Ikeda) for management and economic purposes was therefore complicated by not knowing which areas of the country would be conducive to transmission, if an infected cow happened to be imported via transportation. The approach to prediction presented here uses a Bayesian probability model of dynamical disease spread, in combination with a separable discrete-space, continuous-time spatial model of tick abundance. This joint model allows inference on tick abundance by combining information from independent disease screening, expert opinion, and the occurrence of theileriosis cases. A fast GPU-based implementation was used to provide timely predictions for the outbreak, with the predictive distribution used to provide evidence for policy decisions.

Bibliographic note

Presented at Gregynog 2016 conference.