Australian Bat Lyssavirus: Analysis of National Bat Surveillance Data from 2010 to 2016.

Australian bat lyssavirus (ABLV) was first described in 1996 and has been regularly detected in Australian bats since that time. While the virus does not cause population level impacts in bats and has minimal impacts on domestic animals, it does pose a public health risk. For this reason, bats are monitored for ABLV and a national dataset is collated and maintained by Wildlife Health Australia. The 2010-2016 dataset was analysed using logistic regression and time-series analysis to identify predictors of infection status in bats and the factors associated with human exposure to bats. In common with previous passive surveillance studies, we found that little red flying-foxes (Pteropus scapulatus) are more likely than other species to be infected with ABLV. In the four Australian mainland species of flying-fox, there are seasonal differences in infection risk that may be associated with reproductive cycles, with summer and autumn the seasons of greatest risk. The risk of human contact was also seasonal, with lower risk in winter. In line with other studies, we found that the circumstances in which the bat is encountered, such as exhibiting abnormal behaviour or being grounded, are risk factors for ABLV infection and human contact and should continue be key components of public health messaging. We also found evidence of biased recording of some types of information, which made interpretation of some findings more challenging. Strengthening of "One Health" linkages between public health and animal health services at the operational level could help overcome these biases in future, and greater harmonisation nationally would increase the value of the dataset.

Autoregressive Models Applied to Time-Series Data in Veterinary Science.

A time-series is any set of N time-ordered observations of a process. In veterinary epidemiology, our focus is generally on disease occurrence (the "process") over time, but animal production, welfare or other traits might also be of interest. A common source of time-series datasets are animal disease monitoring and surveillance systems. Here, we scan the application of methods to analyse time-series data in the peer-reviewed, published literature. Based on this literature scan we focus on autocorrelation and illustrate the recommended steps using ARIMA (Autoregressive Integrated Moving Average Models) methods via analysis of a time-series of canine parvovirus (CPV) events in a pet dog population in Australia, 2009 to 2015. We conclude by identifying the barriers to the application of ARIMA methods in veterinary epidemiology and suggest some possible solutions. In the literature scan the selected 37 studies focused mostly on infectious and parasitic diseases, predominantly for analytical, rather than descriptive or predictive, purposes. Trends and seasonality were investigated, and autocorrelation analyzed, in most studies, most commonly using R software. An approach to analyzing autocorrelation using ARIMA methods was then illustrated using a time-series (week and month units) of CPV events in a pet dog population in Australia, reported to a national companion animal disease surveillance system. This time-series was derived by summing veterinarian reports of confirmed CPV diagnoses. We present data analysis output generated via the R statistical environment, and make this code available for the reader to apply to this or other time-series datasets. We also illustrate prediction of CPV events by rainfall as a covariate. Time-series analysis using ARIMA methods to understand and explore autocorrelation appears to be relatively uncommon in veterinary epidemiology. Some of the reasons might include limited availability of data of sufficient time unit length, lack of familiarity with analytical methods and available software, and how to best use the information generated. We recommend that wherever feasible, such time-series data be made available both for analysis and for methods development.