A quantitative risk assessment for the incursion of lumpy skin disease virus into Australia via long-distance windborne dispersal of arthropod vectors.

Lumpy skin disease (LSD) is an infectious disease of cattle and water buffalo caused by lumpy skin disease virus (LSDV). It is primarily transmitted mechanically by biting insects. LSDV has spread from Africa to the Middle-East, the Balkans, Caucasus, Russia, Kazakhstan, China, Asia and India, suggesting that a wide variety of arthropod vectors are capable of mechanical transmission. In 2022, LSD was detected in Indonesia, heightening awareness for Australia's livestock industries. To better understand the risk of LSDV incursion to Australia we undertook a quantitative risk assessment (QRA) looking at windborne dispersal of arthropod vectors, assuming a hypothetical situation where LSD is endemic in south-east Asia and Papua New Guinea. We estimated the risk of LSDV incursion to be low, with a median incursion rate of one incursion every 403 years, based on a model where several infectious insects (i.e. a 'small batch' of 3-5) must bite a single bovine to transmit infection. The incursion risk increases substantially to one incursion every 7-8 years if a bite from a single insect is sufficient for transmission. The risk becomes negligible (one incursion every 20,706 years) if bites from many insects (i.e. a 'large batch' of 30-50 insects) are necessary. Critically, several of our parameter estimates were highly uncertain during sensitivity analyses. Thus, a key outcome of this QRA was to better prioritise surveillance activities and to understand the key research gaps associated with LSDV in the Australasian context. The current literature shows that multiple vectors are required for successful bovine-to-vector transmission of LSDV, suggesting that our estimate of one outbreak every 403 years more accurately represents the risk to Australia; however, the role of single insects in transmission has not yet been evaluated. Similarly, attempts to transmit LSDV between bovines by Culicoides have not been successful, although midges were the highest risk vector category in our model due to the high vector-to-host ratio for midges compared to other vector categories. Our findings provide further insight into the risk of LSD to Australian cattle industries and identify the Tiwi Islands and areas east of Darwin as priority regions for LSDV surveillance, especially between December and March.

New approaches to aquatic and terrestrial animal surveillance: The potential for people and technology to transform epidemiology.

Epidemiology provides insights about causes of diseases and how to control them, and is powered by surveillance information. Animal health surveillance systems typically have been designed to meet high-level government informational needs, and any incentives for those who generate data (such as animal owners and animal health workers) to report surveillance information are sometimes outweighed by the negative consequences of reporting; underreporting is a serious constraint. This problem can persist even when modern advances in information and communications technology (ICT) are incorporated into the structure and operation of surveillance systems, although some problems typical of paper-based systems (including timeliness of reporting and response, accuracy of data entry, and level of detail recorded) are reduced. On occasions, however, additional problems including sustainability arise. We describe two examples of a philosophical approach and ICT platform for the development of powerful and sustainable health information systems that are people-centred and do not exhibit these typical problems. iSIKHNAS is Indonesia's integrated animal health information system, and PIISAC is a sustainable secure research platform based on full production data from participating commercial Chilean aquaculture companies. Epidemiologists working with these systems are faced with interesting new challenges, including the need to develop skills in extracting appropriate surveillance outcomes from large volumes of continually-streaming data.