A scenario model to support freedom from African swine fever virus in Western Canada populated with data from Canada West Swine Health Intelligence Network and CanSpotASF

Abstract

African swine fewer (ASF) is a serious disease present in Africa, Eurasia, and the Caribbean but not in continental North America. CanSpotASF describes the ASF surveillance in Canada. It enhanced the passive surveillance by adding new surveillance components one by one. The first enhancement was “the risk-based early detection testing (rule-out testing)” where cases with eligible conditions were tested for ASF virus (ASFv). The rule-out testing started in 2020 and we have over 3 years data in the Canada West Swine Health Intelligence Network (CWSHIN) data repository on ASFv rule-out testing. The objectives were to develop and assess a scenario tree model (STM) for ASFv freedom to support the claim that commercial swine in Western Canada were free from ASFv; to assess if the CWSHIN repository data may be useful in the STM; and to assess if the rule-out testing had contributed to more evidence of freedom from ASFv than we would have had with passive surveillance alone. To compare and assess different scenarios we used the model’s ability to accumulate evidence of freedom from the July quarter in 2020 to the July quarter in 2023 (13 quarters). We concluded that a method to assess the ability of a scenario and model to accumulate evidence of freedom, based on increasing probability of freedom that approached an equilibrium and a system sensitivity of at least 0.2–0.3, was useful. The CWSHIN data repository was critical to populate the model because it included both number of clinical assessments by herd veterinarians (from quarterly surveys); routine diagnostic data (pathology examinations) and ASF testing results from laboratories. The take home message was that the probability that a herd veterinarian will report a suspicion of ASF to the Canadian Food Inspection Agency (CFIA) (seVet) affected the outcomes of the STM. If the probability that a herd veterinarian reports to CFIA is low (seVet=0.01), then pathology examination and CanSpotASF will improve the STM outcomes. If the probability that a herd veterinarian reports to CFIA is high (seVet is 0.7 or higher), then there is no need for additional pathology and CanSpotASF testing. We expect the actual probability of reporting and subsequent value of additional pathology and testing to between these two scenarios.