Post‐outbreak African horse sickness surveillance: A scenario tree evaluation in South Africa’s controlled area

Abstract

An African horse sickness (AHS) outbreak occurred in March and April 2016 in the controlled area of South Africa. This extended an existing trade suspension of live equids from South Africa to the European Union. In the post‐outbreak period ongoing passive and active surveillance, the latter in the form of monthly sentinel surveillance and a stand‐alone freedom from disease survey in March 2017, took place. We describe a stochastic scenario tree analysis of these surveillance components for 24 months, starting July 2016, in three distinct geographic areas of the controlled area. Given that AHS was not detected, the probability of being free from AHS was between 98.3% and 99.8% assuming that, if it were present, it would have a prevalence of at least one infected animal in 1% of herds. This high level of freedom probability had been attained in all three areas within the first 9 months of the 2‐year period. The primary driver of surveillance outcomes was the passive surveillance component. Active surveillance components contributed minimally (<0.2%) to the final probability of freedom. Sensitivity analysis showed that the probability of infected horses showing clinical signs was an important parameter influencing the system surveillance sensitivity. The monthly probability of disease introduction needed to be increased to 20% and greater to decrease the overall probability of freedom to below 90%. Current global standards require a 2‐year post‐incursion period of AHS freedom before re‐evaluation of free zone status. Our findings show that the length of this period could be decreased if adequately sensitive surveillance is performed. In order to comply with international standards, active surveillance will remain a component of AHS surveillance in South Africa. Passive surveillance, however, can provide substantial evidence supporting AHS freedom status declarations, and further investment in this surveillance activity would be beneficial.

An entry risk assessment of African horse sickness virus into the controlled area of South Africa through the legal movement of equids

“…The subclinical rate of infection ( P subclin ) for AHS cases influences both the probability of infection (since reported cases in the endemic area are generally through passive surveillance) and the probability that infected horses will be detected by private veterinarians during pre-movement health certification ( P detect ). P subclin was based on the clinical case proportions observed in randomly selected outbreaks in the AHS controlled area where sub-clinical cases of the disease have been previously described (2011, 2014 and 2016 [ 17 ]). In short, a Beta ( subclin i +1, n i − subclin i +1) distribution was used using an uninformed prior Bayesian estimate, where subclin subclinical cases were observed in outbreak i of a total of n outbreak cases.…”