African swine fever: a New Zealand perspective on epidemiological risk factors for its occurrence

Epidemiological Characterization of African Swine Fever Dynamics in Ukraine, 2012-2023

African swine fever (ASF) is a viral disease, endemic to Africa, that causes high mortality when introduced into domestic pig populations. Since the emergence of p72-genotype II African swine fever virus (ASFV) in Georgia in 2007, an ASF epidemic has been spreading across Europe and many countries in Asia. The epidemic first reached Ukraine in 2012. To better understand the dynamics of spread of ASF in Ukraine, we analyzed spatial and temporal outbreak data reported in Ukraine between 2012 and mid-2023. The highest numbers of outbreaks were reported in 2017 (N = 163) and 2018 (N = 145), with overall peak numbers of ASF outbreaks reported in August (domestic pigs) and January (wild boars). While cases were reported from most of Ukraine, we found a directional spread from the eastern and northern borders towards the western and southern regions of Ukraine. Many of the early outbreaks (before 2016) were adjacent to the border, which is again true for more recent outbreaks in wild boar, but not for recent outbreaks in domestic pigs. Outbreaks prior to 2016 also occurred predominantly in areas with a below average domestic pig density. This new analysis suggests that wild boars may have played an important role in the introduction and early spread of ASF in Ukraine. However, in later years, the dynamic suggests human activity as the predominant driver of spread and a separation of ASF epizootics between domestic pigs and in wild boars. The decline in outbreaks since 2019 suggests that the implemented mitigation strategies are effective, even though long-term control or eradication remain challenging and will require continued intensive surveillance of ASF outbreak patterns.

Fuzzy model for quantitative assessment of the epidemic risk of African Swine Fever within Australia

African Swine Fever (ASF) has spread rapidly across different continents since 2007 and caused huge biosecurity threats and economic losses. Establishing an effective risk assessment model is of great importance for ASF prevention, especially for those ASF-free countries such as Australia. With a vast territory and an economy heavily relying on primary industry, Australia faces a threat from the spread of ASF. Although ordinary quarantine measures have been well-performed throughout Australia, there is still a need to develop an effective risk assessment model to understand the spread of ASF due to the strong transmission ability of ASF. In this paper, via a comprehensive literature review, and analyzing the transmission factors of ASF, we provide a fuzzy model to assess the epidemic risk of Australian states and territories, under the assumption that ASF has entered Australia. As demonstrated in this work, although the pandemic risk of ASF in Australia is relatively low, there is a risk of irregular and scattered outbreaks, with Victoria (VIC) and New South Wales (NSW) - Australia Capital Territory (NSW-ACT) showed the highest risk. The reliability of this model was also systematically tested by a conjoint analysis model. To our knowledge, this is the first study to comprehensively analyze the ASF epidemic risk in a country using fuzzy modeling. This work can provide an understanding of the risk ASF transmission within Australia based on the fuzzy modeling, the same methodology can also provide insights and useful information for the establishment of fuzzy models to perform the ASF risk assessment for other countries.

Development and Validation of a Bioinformatic Workflow for the Rapid Detection of Viruses in Biosecurity

The field of biosecurity has greatly benefited from the widespread adoption of high-throughput sequencing technologies, for its ability to deeply query plant and animal samples for pathogens for which no tests exist. However, the bioinformatics analysis tools designed for rapid analysis of these sequencing datasets are not developed with this application in mind, limiting the ability of diagnosticians to standardise their workflows using published tool kits. We sought to assess previously published bioinformatic tools for their ability to identify plant- and animal-infecting viruses while distinguishing from the host genetic material. We discovered that many of the current generation of virus-detection pipelines are not adequate for this task, being outperformed by more generic classification tools. We created synthetic MinION and HiSeq libraries simulating plant and animal infections of economically important viruses and assessed a series of tools for their suitability for rapid and accurate detection of infection, and further tested the top performing tools against the VIROMOCK Challenge dataset to ensure that our findings were reproducible when compared with international standards. Our work demonstrated that several methods provide sensitive and specific detection of agriculturally important viruses in a timely manner and provides a key piece of ground truthing for method development in this space.