Bayesian data assimilation provides rapid decision support for vector-borne diseases

TL;DR

This paper introduces a Bayesian data assimilation method that uses indirect vector activity observations to rapidly predict and update the spread of vector-borne diseases, even with limited vector data.

Contribution

The paper presents a novel Bayesian approach that integrates indirect vector data into epidemic models for real-time outbreak prediction and uncertainty quantification.

Findings

Provides quantitative epidemic spread forecasts with uncertainty estimates.

Learns sequentially as the epidemic unfolds, updating predictions over time.

Demonstrates effectiveness on T. orientalis outbreak in New Zealand cattle.

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. Whereas host population data is typically available, for novel disease introductions there is a high chance of the pathogen utilising a vector for which data is 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 provides evidence for changing epidemic dynamics through time. Our approach therefore provides a significant advance in rapid decision support for novel vector-borne disease outbreaks.