The koala, Phascolarctos cinereus, is an iconic Australian wildlife species, but faces rapid decline in South-East Queensland (SEQLD). For conservation planning, estimating koala populations is crucial. Systematic surveys are the most common approach to estimate koala populations, but such surveys are restricted to small geographic areas, they are costly and conducted infrequently. Public interest and participation in the collection of koala sightings is increasing in popularity, but such data is generally not used for population estimation. We used incidental sightings of koalas reported by members of the public from 1997-2013 in SEQLD to estimate the yearly spatio-temporal koala sightings density. For this, a spatio-temporal point process model was developed accounting for observed koala density, spatio-temporal detection bias and clustering. The density of koalas varied throughout the study period due to the heterogeneous nature of koala habitat in SEQLD, with density estimates ranging between 0.005 to 8.9 koalas per km2. The percentage of land areas with very low sightings densities (0-0.25 koalas per km2) remained similar throughout the study period representing in average (SD) 68.3% (0.06) of the total study area. However, land areas with more koalas per km2 showed larger annual variations, with koala mean (SD) densities of 0.25-0.5, 0.5-1, 1-2, 2-5 and > 5 koalas per km2 representing 16.8% (0.21), 13.8% (0.25), 0.7% (0.20), 0.3% (0.13), and 0.2% (0.1) of the study area in South-East Queensland, respectively.We did find that clustering of koala sightings was not prominently different between the mating and non-mating seasons of koalas. While acknowledging the limitations associated sightings data, we developed a statistical model that addressed the spatio-temporal bias associated with observed koala sightings and provided long-term relative koala density estimates for one of the largest koala populations of Australia.

In South Vietnam, live bird markets (LBMs) are key in the value chain of poultry products and spread of avian influenza virus (AIV) although they may not be the sole factor to determine avian influenza (AI) prevalence in the southern part. Therefore, a risk analysis of AIV spread was conducted by including all possible value chain factors. A cross-sectional study was performed in backyard farms, high-biosecurity farms (bio-farms), LBMs, and poultry delivery stations (PDSs) in the four districts of Vinh Long Province in December 2016 and August 2017. A total of 3 597 swab samples were collected from individual poultry at 101 backyard farms, 50 bio-farms, 58 sellers in LBMs, and 17 traders in PDSs and then investigated for AIV isolation. Concurrently, information related to participants and birds was collected and used to identify the fixed and random effects of factors in AIV infection. A total of 274 birds were positive for virus isolation, with a prevalence of 7.6% (95% confidence interval [CI]: 6.8–8.5) at the individual poultry level, and the adjusted prevalence based on the sampling weight was 7.9% (95% CI: 7.6–8.2). The significantly higher prevalence in PDSs (20.7%) and LBMs (14.2%) compared to backyard farms (3.0%) and bio-farms (0.6%) suggested that PDSs are another hot spot for AIV circulation. The high diversity in the seller and trader population characteristics was revealed using multiple-correspondence analysis to analyze the participants’ demographic factors in LBM and PDS. The mixed-effect logistic regression model revealed that keeping duck at the sampling time and the owner’s older age should be risk factors of AIV infection in PDS. Therefore, functional AI control efforts to monitor the PDS system should be emphasized to minimize AIV circulation risk in Vietnam.

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; and (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 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.