We describe results from a panel study in which pigs from a 17-sow African swine fever (ASF) positive herd in Thái Bình province, Vietnam, were followed over time to record the date of onset of ASF signs and the date of death from ASF. Our objectives were to (1) fit a susceptible-exposed-infectious-removed disease model to the data with transmission coefficients estimated using approximate Bayesian computation; (2) provide commentary on how a model of this type might be used to provide decision support for disease control authorities. For the outbreak in this herd, the median of the average latent period was 10 days (95% HPD (highest posterior density interval): 2 to 19 days), and the median of the average duration of infectiousness was 3 days (95% HPD: 2 to 4 days). The estimated median for the transmission coefficient was 3.3 (95% HPD: 0.4 to 8.9) infectious contacts per ASF-infectious pig per day. The estimated median for the basic reproductive number, R₀, was 10 (95% HPD: 1.1 to 30). Our estimates of the basic reproductive number R₀ were greater than estimates of R₀ for ASF reported previously. The results presented in this study may be used to estimate the number of pigs expected to be showing clinical signs at a given number of days following an estimated incursion date. This will allow sample size calculations, with or without adjustment to account for less than perfect sensitivity of clinical examination, to be used to determine the appropriate number of pigs to examine to detect at least one with the disease. A second use of the results of this study would be to inform the equation-based within-herd spread components of stochastic agent-based and hybrid simulation models of ASF.

Background: The 2019/2020 Australian bushfires were the largest bushfire event in modern Australian history. While actions to mitigate risk to homes from bushfires are well reported, there is very little research reported on the impacts of bushfires on livestock. With an increasing incidence of bushfires predicted, there is an urgent need to identify how farmers can best protect their livestock.Objectives: Compare bushfire affected farms with and without injured livestock to identify associations between risk factors and bushfire injury. Infer management approaches that can be used to reduce bushfire injury in livestock.Method: A case‐control study using a structured interview questionnaire, delivered in late 2020 to cattle and sheep farmers in south‐eastern Australia (New South Wales and Victoria) whose farmland was burnt in the 2019/2020 Australian bushfires. Case farms were farms with bushfires injured or killed livestock. Control farms were farms that had no bushfire injured livestock but that still had fire present on the farm. Interview responses were summarised and information theoretical approaches were used to identify potential risk factors for livestock bushfire injury and protective actions that could inform future fire‐preparation recommendations.Results and discussion: Of 46 farms in the case‐control study, 21 (46%) reported bushfire injured or killed livestock. Apparent protective factors identified included: preparation (having a bushfire plan and more than two farm bushfire fighting units), backburning and receiving assistance from fire authorities. Combined beef and sheep grazing enterprises appeared to have an increased risk of bushfire injury to livestock.

A factor constraining the elimination of dog-mediated human rabies is limited information on the size and spatial distribution of free-roaming dog populations (FRDPs). The aim of this study was to develop a statistical model to predict the size of free-roaming dog populations and the spatial distribution of free-roaming dogs in urban areas of Nepal, based on real-world dog census data from the Himalayan Animal Rescue Trust (HART) and Animal Nepal. Candidate explanatory variables included proximity to roads, building density, specific building types, human population density and normalised difference vegetation index (NDVI). A multivariable Poisson point process model was developed to estimate dog population size in four study locations in urban Nepal, with building density and distance from nearest retail food establishment or lodgings as explanatory variables. The proposed model accurately predicted, within a 95 % confidence interval, the surveyed FRDP size and spatial distribution for all four study locations. This model is proposed for further testing and refinement in other locations as a decision-support tool alongside observational dog population size estimates, to inform dog health and public health initiatives including rabies elimination efforts to support the 'zero by 30' global mission.