A Comparison of Dynamics in Two Models for the Spread of a Vector-Borne Disease

Quantification of within- and between-farm dispersal of Culicoides biting midges using an immunomarking technique

Culicoides biting midges (Diptera, Ceratopogonidae) are vectors of arboviruses that cause significant economic and welfare impact. Local‐scale spread of Culicoides‐borne arboviruses is largely determined by the between‐farm movement of infected Culicoides.

Study of the dispersal behaviour of Culicoides by capture–mark–recapture (CMR) is problematic due to the likelihood of mortality and changes in behaviour upon capture caused by the small size and fragility of these insects, evidenced by low recapture rates. To counter the problem of using CMR with Culicoides, this study utilised an ovalbumin immunomarking technique to quantify the within‐ and between‐farm dispersal of Culicoides in southern England.

Both within‐ and between‐farm dispersal of Culicoides was observed. Of the 9058 Culicoides collected over 22 nights of trapping, 600 ovalbumin‐positive Culicoides, of 12 species including those implicated as arbovirus vectors, were collected with a maximum dispersal distance of 3125 m.

This study provides the first species‐level data on the between‐farm dispersal of potential bluetongue, Schmallenberg and African horse sickness virus vectors in northern Europe. High‐resolution meteorological data determined upwind and downwind flight by Culicoides had occurred. Cumulative collection and meteorological data suggest 15·6% of flights over 1 km were upwind of the treatment area and 84·4% downwind.

Synthesis and applications. The use of immunomarking eliminates the potential adverse effects on survival and behaviour of insect collection prior to marking, substantially improving the resolution and accuracy of estimates of the dispersal potential of small and delicate vector species such as Culicoides. Using this technique, quantification of the range of Culicoides dispersal with regard to meteorological conditions including wind direction will enable improved, data‐driven modelling of the spread of Culicoides‐borne arboviruses and will inform policy response to incursions and outbreaks.

The use of immunomarking eliminates the potential adverse effects on survival and behaviour of insect collection prior to marking, substantially improving the resolution and accuracy of estimates of the dispersal potential of small and delicate vector species such as Culicoides. Using this technique, quantification of the range of Culicoides dispersal with regard to meteorological conditions including wind direction will enable improved, data‐driven modelling of the spread of Culicoides‐borne arboviruses and will inform policy response to incursions and outbreaks.

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

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.