Abstract
Understanding the influence of non-susceptible hosts on vector-borne disease transmission is an important epidemiological problem. However, investigation of its impact can be complicated by uncertainty in the location of the hosts. Estimating the risk of transmission of African horse sickness (AHS) in Great Britain (GB), a virus transmitted by Culicoides biting midges, provides an insightful example because: (i) the patterns of risk are expected to be influenced by the presence of non-susceptible vertebrate hosts (cattle and sheep) and (ii) incomplete information on the spatial distribution of horses is available because the GB National Equine Database records owner, rather than horse, locations. Here, we combine land-use data with available horse owner distributions and, using a Bayesian approach, infer a realistic distribution for the location of horses. We estimate the risk of an outbreak of AHS in GB, using the basic reproduction number (R0), and demonstrate that mapping owner addresses as a proxy for horse location significantly underestimates the risk. We clarify the role of non-susceptible vertebrate hosts by showing that the risk of disease in the presence of many hosts (susceptible and non-susceptible) can be ultimately reduced to two fundamental factors: first, the abundance of vectors and how this depends on host density, and, second, the differential feeding preference of vectors among animal species.
Highlights
A large body of ecological and epidemiological studies has highlighted the profound effects of spatial distributions of living organisms on population and disease dynamics
It is essential to disentangle these processes when assessing the risk of a disease. We address these issues by developing a credible 2 distribution of horses in Great Britain (GB) that can be used to re-assess, in the light of current knowledge, the risk of African horse sickness virus (AHSV) spread in GB and the efficacy of potential control measures
Re-distribution of the National Equine Database (NED) data according to the algorithm developed here appears to correct this source of bias with the re-distributed horse population more evenly spread out towards rural areas and the exceptionally high densities in urban settlements being removed
Summary
A large body of ecological and epidemiological studies has highlighted the profound effects of spatial distributions of living organisms on population and disease dynamics (see [1], and references therein) This issue has raised considerable interest outside the scientific community; inaccurate knowledge of spatial host distribution is regarded as a central problem for health authorities, especially in the presence of a sudden outbreak of disease when control measures need to be quickly implemented. As such information is often only partially available, developing mathematical tools that overcome the limited predictive capacity due to uncertainty in host distribution is a key scientific goal [2 –4]. The approach relies on an artificial parametrization that incorporates the
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