Bacterial biofilms are structured communities of bacterial cells enclosed in a self-produced polymer matrix that is attached to a surface. Biofilms protect and allow bacteria to survive and thrive in hostile environments. Bacteria within biofilms can withstand host immune responses, and are much less susceptible to antibiotics and disinfectants when compared to their planktonic counterparts. The ability to form biofilms is now considered an attribute of many microorganisms. Diseases associated with biofilms require novel methods for their prevention, diagnosis and treatment; this is largely due to the properties of biofilms. Furthermore, the presence of biofilms on surfaces found at farms, slaughterhouses or food processing plants will have an impact on the efficacy of disinfection protocols. Surprisingly, biofilm formation by bacterial pathogens of veterinary or zoonotic importance has received relatively little attention. The objective of this brief Review article is to bring awareness about the importance of biofilms to animal health stakeholders.

Objective-To use mathematical modeling to assess the effectiveness of control strategies for porcine reproductive and respiratory syndrome (PRRS) virus on a swine farm. Sample-A hypothetical small, medium, or large farrow-to-weaning swine farm in the Midwestern United States. Procedures-Stochastic models were formulated to simulate an outbreak of PRRS on a farm. Control strategies assessed in those models included none (baseline) and various combinations of mass immunization, herd closure, and gilt acclimatization. Nine different models resulting from the combination of low, moderate, or high PRRS virus virulence and small, medium, or large herd size were simulated. A stabilized status, the outcome of interest, was defined as the absence of positive PCR assay results for PRRS virus in 3-week-old piglets. For each scenario, the percentage of simulations with a stabilized status was used as a proxy for the probability of disease control. Results-Increasing PRRS virus virulence and herd size were negatively associated with the probability of achieving a stabilized status. Repeated mass immunization with herd closure or gilt acclimitization was a better alternative than was single mass immunization for disease control within a farm. Conclusions and Clinical Relevance-Repeated mass immunization with a PRRS modified-live virus vaccine with herd closure or gilt acclimitization was the scenario most likely to achieve a stabilized status. Estimation of the cost of various PRRS control strategies is necessary.

A study of Ontario swine farms positive for Porcine reproductive and respiratory syndrome virus (PRRSV) tested the association between genetic similarity of the virus and similarity of clinical signs reported by the herd owner. Herds were included if a positive result of polymerase chain reaction for PRRSV at the Animal Health Laboratory at the University of Guelph, Guelph, Ontario, was found between September 2004 and August 2007. Nucleotide-sequence similarity and clinical similarity, as determined from a telephone survey, were calculated for all pairs of herds. The Mantel test indicated that clinical similarity and sequence similarity were weakly correlated for most clinical signs. The generalized additive model indicated that virus homology with 2 vaccine viruses affected the association between sequence similarity and clinical similarity. When the data for herds with vaccine-like virus were removed from the dataset there was a significant association between virus similarity and similarity of the reported presence of abortion, stillbirth, preweaning mortality, and sow/boar mortality. Ownership similarity was also found to be associated with virus similarity and with similarity of the reported presence of sows being off-feed, nursery respiratory disease, nursery mortality, finisher respiratory disease, and finisher mortality. These results indicate that clinical signs of PRRS are associated with PRRSV genotype and that herd ownership is associated with both of these.

The objective of this study was to estimate the population size of Canadian poultry farms in 3 subpopulations (British Columbia, Ontario, and Other) by poultry category. We used data for 2008 to 2011 from the Canadian Notifiable Avian Influenza (NAI) Surveillance System (CanNAISS). Log-linear capture-recapture models were applied to estimate the number of commercial chicken and turkey farms. The estimated size of farm populations was validated by comparing sizes to data provided by the Canadian poultry industry in 2007, which were assumed to be complete and exhaustive. Our results showed that the log-linear modelling approach was an appropriate tool to estimate the population size of Canadian commercial chicken and turkey farms. The 2007 farm population size for each poultry category was included in the 95% confidence intervals of the farm population size estimates. Log-linear capture-recapture modelling might be useful for estimating the number of farms using surveillance data when no comprehensive registry exists.