title: Bayesian data assimilation provides rapid decision support for vector-borne diseases.
abstract: Predicting the spread of vector-borne diseases in response to incursions requires knowledge of both host and vector demographics in advance of an outbreak. Although host population data are typically available, for novel disease introductions there is a high chance of the pathogen using a vector for which data are unavailable. This presents a barrier to estimating the parameters of dynamical models representing host-vector-pathogen interaction, and hence limits their ability to provide quantitative risk forecasts. The Theileria orientalis (Ikeda) outbreak in New Zealand cattle demonstrates this problem: even though the vector has received extensive laboratory study, a high degree of uncertainty persists over its national demographic distribution. Addressing this, we develop a Bayesian data assimilation approach whereby indirect observations of vector activity inform a seasonal spatio-temporal risk surface within a stochastic epidemic model. We provide quantitative predictions for the future spread of the epidemic, quantifying uncertainty in the model parameters, case infection times and the disease status of undetected infections. Importantly, we demonstrate how our model learns sequentially as the epidemic unfolds and provide evidence for changing epidemic dynamics through time. Our approach therefore provides a significant advance in rapid decision support for novel vector-borne disease outbreaks.
authors.affiliation: CHICAS, Lancaster University, Bailrigg, Lancaster LA1 4YG, UK c.jewell@lancaster.ac.uk.
authors.forename: Chris P
authors.initials: CP
authors.lastname: Jewell
authors.affiliation: Institute of Fundamental Sciences, Massey University, Private Bag 11222, Palmerston, North 4442, New Zealand.
authors.forename: Richard G
authors.initials: RG
authors.lastname: Brown
mesh_terms: D000818:Animals; D001096:Arachnid Vectors; D001499:Bayes Theorem; D002417:Cattle; D004196:Disease Outbreaks; D008954:Models, Biological; D009520:New Zealand / Q000453:epidemiology; D012307:Risk Factors; D012621:Seasons; D016794:Theileria; D013801:Theileriasis / Q000453:epidemiology / Q000635:transmission
keywords: Bayesian inference; Markov-chain Monte Carlo; risk forecasting; seasonal epidemic; vector-borne disease