Fetuses, Aborted

C. psittaci LAMP assay was evaluated using: (1) 12 DNA samples extracted from previously characterised C. psittaci isolates (10 human, two parrot and one equine) (Table S1); (2) DNA extracted from 21 placental, foetal, nasal, lung and rectal swabs, and 1 each placental and foetal tissue sample taken from 20 equine hosts; and (3) three pigeon liver DNA extracts (Table S2). All samples were collected and submitted as part of routine diagnostic testing by field or district veterinarians to the State Veterinary Diagnostic Laboratory (SVDL), Elizabeth Macarthur Agricultural Institute (EMAI), Menangle, NSW, Australia, and as such do not require special animal ethics approval. DNA extracts from these samples were kindly provided by Dr. Cheryl Jenkins, and Dr. James Branley. The use of these swabs was considered by the University of The Sunshine Coast (USC) Animal Ethics Committee and the need for further ethics consideration was waived under exemption AN/E/17/19.
C. pecorum LAMP was evaluated using a: (1) 18 DNA samples extracted from previously characterised koala (n = 7), sheep (n = 4), cattle (n = 4) and pig C. pecorum (n = 3) cultures (Table S1); (2) 16 sheep and 13 cattle ocular, rectal, and tissue swab DNA samples; and (3) 34 ocular and urogenital (UGT) koala swab DNA samples (Table S3), all available in our collection. The use of these swabs, also collected by qualified veterinarians as a part of routine diagnostic testing, was considered and approved for exemption by the University of The Sunshine Coast (USC) Animal Ethics Committee (AN/E/14/01 and AN/E/14/31).
We also evaluated the specificity of the assays against DNA samples extracted from previously characterised (i) chlamydial isolates (koala C. pneumoniae LPColN, C. abortus S26/3, C. suis S45, C. trachomatis serovar D, C. murridarum Nigg, C. caviae GPIC) and uncultured Chlamydiales (Fritschea spp.); (ii) Gram negative Escherichia coli and Prevotella bivia; Gram positive Fusobacterium nucleatum, Staphylococcus epidermidis, S. aureus, Streptococcus spp., and Enterococcus faecalis; and (iii) commercially available human gDNA (Promega, Alexandria, NSW 2015), all available in our laboratory (Table S1).
In order to evaluate rapid swab processing, 18 ocular, cloacal and UGT (14 dry and four RNA-Later) clinical swabs taken from 14 koalas with presumptive chlamydiosis were used for testing without DNA extraction. Briefly, RNA-Later and dry swabs with added 500 µL TE buffer were vortexed vigorously for 5 min. 300 µL aliquots were then heated to 98 °C for 15 min to lyse DNA, following LAMP testing. The use of these swabs, collected as a part of routine diagnostic testing, is also under Animal Ethics approval exemption (AN/E/14/01). An aliquot of 50 µL of the swab suspension was used for LAMP and qPCR assays, while from the remaining volume of the swab suspension was used for DNA extraction, in order to compare swab suspension and its paired extracted DNA as a template in the assays.

A dynamic age-structured stochastic compartmental model incorporating dairy herd demographics was written in R [22 ] to capture transmission of B. abortus within and between dairy herds. As the model is used to capture dynamics in dairy herds typical of Punjab State of India, large ruminants (cows/buffalo) are the only species considered as very few farms or households in this area keep small ruminants (2%) [13 (link)]. In addition, B. abortus is the only species to be isolated from large ruminants here [13 (link)]. Cows and buffalo are assumed to mix homogeneously within the herd as management practices in Punjab are very similar and farms often keep a mixture of both species. Transition between compartments follows a Poisson process using the event-driven Gillespie stochastic simulation algorithm (SSA) implemented using the R package GillespieSSA [23 ]. The model tracks the numbers of livestock in each state; susceptible (S_i), infected (E_i), vaccinated (V_i) (all age groups) and infectious (I_i; adults only), as well as vaccine doses and the number of animals ‘removed’ from the dairy herds when implementing test and removal strategies (figure 1 and electronic supplementary material).
Figure 1.

Model schematic showing the transition between different compartments, where NS(t)=∑ j=3 j=11Sj(t), NE(t)=∑ j=3 j=11Ej(t) and NV(t)=∑ j=3 j=11Vj(t) denote, respectively, the total number of susceptible, exposed and vaccinated adults in the herd; λ, θ, α₁ and ω denote, respectively, the calving rate, probability of vertical transmission, removal rate of newborns and probability a calf is vaccinated and becomes immune; γ is the transition rate between age groups (1 year); β is the effective contact rate; ε_j, P, φ, μ and α_j are the number of new purchases (ε_j, j = 4), the probability that a purchased animal is infected (P) and vaccinated (φ), the rate of loss of infectiousness (μ) and the rate of removal of adults (α_j).

Scenarios combining calfhood vaccination with testing of all animals (cows and buffalo of all age groups using the Rose Bengal test) and immediate removal of test positives were also simulated. Test and removal is assumed to occur at a single point in time, at the inception of the control programme (as proposed in the Brucella free village programme). There is no differentiate infected from vaccinated animals (DIVA) test available for brucellosis, therefore this strategy was not combined with vaccination at the inception of the programme. The number of true test positive animals is sampled from a Bernoulli distribution with the number of trials equal to E_j+ I_j and the probability of testing positive given by the test sensitivity, Se. False positive animals are also sampled from a Bernoulli distribution with number of trials equal to S_j and the probability of giving a false positive result equal to 1 − Sp. Scenario B was not simulated with test and removal as it was not considered an effective use of resources compared with Scenario C (see Results).
Targets for control were set using prevalence (percentage of infected animals in a village), as this is measurable via surveillance. Therefore, we define ‘control’ as animal-level prevalence below 1%, as indicative of a nominally low level of infection whereby Punjab could consider moving towards elimination. The ‘probability of control’ is the percentage of simulations with prevalence less than 1%. Predicted (cumulative) incidence, the percentage of animals infected with B. abortus per year, is also presented.