The frequency and distance of movements of pigs and semen between commercial and non-commercial piggeries in New Zealand

Isolates, Antimicrobial Susceptibility Profiles and Multidrug Resistance of Bacteria Cultured from Pig Submissions in New Zealand

Data on the scope of bacterial pathogens present and the frequency of antimicrobial resistance (AMR) in New Zealand's pigs are limited. This study describes bacterial isolates, antimicrobial susceptibility data, and multidrug resistance (MDR; resistance to ≥3 antimicrobial classes) from New Zealand pig submissions. Porcine test data from June 2003 to February 2016 were obtained from commercial veterinary pathology laboratory records. In total, 470/477 unique submissions resulted in bacterial growth, yielding 779 isolates. Sample type was recorded for 360/477 (75.5%); lung (79/360; 21.9%), faecal (61/360; 16.9%) and intestinal (45/360; 12.5%) were most common. The most common isolates were Escherichia coli (186/779, 23.9%), Actinobacillus pleuropneumoniae (43/779; 5.5%), Streptococcus suis (43/779; 5.5%), unidentified Campylobacter spp. (38/779; 4.9%), alpha haemolytic Streptococci (32/779; 4.1%), coagulase negative Staphylococcus spp. (26/779; 3.3%), and Pasteurella multocida (25/779; 3.2%). Susceptibility results were available for 141/779 (18.1%) isolates from 62/470 (13.2%) submissions. Most were susceptible to trimethoprim-sulphonamide (75/81; 92.6%), but fewer were susceptible to penicillin (37/77; 48.1%), tilmicosin (18/43; 41.9%), or tetracyclines (41/114; 36.0%). No susceptibility data were available for Salmonella spp., Campylobacter spp., or Yersinia spp. isolates. MDR was present in 60/141 (42.6%) isolates. More data on sample submission drivers, antimicrobial drug use, and susceptibilities of important porcine bacterial isolates are required to inform guidelines for prudent antimicrobial use, to reduce their prevalence, human transmission, and to minimise AMR and MDR.

Early Decision Indicators for Foot-and-Mouth Disease Outbreaks in Non-Endemic Countries

Disease managers face many challenges when deciding on the most effective control strategy to manage an outbreak of foot-and-mouth disease (FMD). Decisions have to be made under conditions of uncertainty and where the situation is continually evolving. In addition, resources for control are often limited. A modeling study was carried out to identify characteristics measurable during the early phase of a FMD outbreak that might be useful as predictors of the total number of infected places, outbreak duration, and the total area under control (AUC). The study involved two modeling platforms in two countries (Australia and New Zealand) and encompassed a large number of incursion scenarios. Linear regression, classification and regression tree, and boosted regression tree analyses were used to quantify the predictive value of a set of parameters on three outcome variables of interest: the total number of infected places, outbreak duration, and the total AUC. The number of infected premises (IPs), number of pending culls, AUC, estimated dissemination ratio, and cattle density around the index herd at days 7, 14, and 21 following first detection were associated with each of the outcome variables. Regression models for the size of the AUC had the highest predictive value (R² = 0.51-0.9) followed by the number of IPs (R² = 0.3-0.75) and outbreak duration (R² = 0.28-0.57). Predictability improved at later time points in the outbreak. Predictive regression models using various cut-points at day 14 to define small and large outbreaks had positive predictive values of 0.85-0.98 and negative predictive values of 0.52-0.91, with 79-97% of outbreaks correctly classified. On the strict assumption that each of the simulation models used in this study provide a realistic indication of the spread of FMD in animal populations. Our conclusion is that relatively simple metrics available early in a control program can be used to indicate the likely magnitude of an FMD outbreak under Australian and New Zealand conditions.

How commercial and non-commercial swine producers move pigs in Scotland: a detailed descriptive analysis

BACKGROUND

The impact of non-commercial producers on disease spread via livestock movement is related to their level of interaction with other commercial actors within the industry. Although understanding these relationships is crucial in order to identify likely routes of disease incursion and transmission prior to disease detection, there has been little research in this area due to the difficulties of capturing movements of small producers with sufficient resolution. Here, we used the Scottish Livestock Electronic Identification and Traceability (ScotEID) database to describe the movement patterns of different pig production systems which may affect the risk of disease spread within the swine industry. In particular, we focused on the role of small pig producers.

RESULTS

Between January 2012 and May 2013, 23,169 batches of pigs were recorded moving animals between 2382 known unique premises. Although the majority of movements (61%) were to a slaughterhouse, the non-commercial and the commercial sectors of the Scottish swine industry coexist, with on- and off-movement of animals occurring relatively frequently. For instance, 13% and 4% of non-slaughter movements from professional producers were sent to a non-assured commercial producer or to a small producer, respectively; whereas 43% and 22% of movements from non-assured commercial farms were sent to a professional or a small producer, respectively. We further identified differences between producer types in several animal movement characteristics which are known to increase the risk of disease spread. Particularly, the distance travelled and the use of haulage were found to be significantly different between producers.

CONCLUSIONS

These results showed that commercial producers are not isolated from the non-commercial sector of the Scottish swine industry and may frequently interact, either directly or indirectly. The observed patterns in the frequency of movements, the type of producers involved, the distance travelled and the use of haulage companies provide insights into the structure of the Scottish swine industry, but also highlight different features that may increase the risk of infectious diseases spread in both Scotland and the UK. Such knowledge is critical for developing more robust biosecurity and surveillance plans and better preparing Scotland against incursions of emerging swine diseases.