Within-group contact of cattle in dairy barns and the implications for disease transmission

Inferring transmission routes for foot-and-mouth disease virus within a cattle herd using approximate Bayesian computation

To control an outbreak of an infectious disease it is essential to understand the different routes of transmission and how they contribute to the overall spread of the pathogen. With this information, policy makers can choose the most efficient methods of detection and control during an outbreak. Here we assess the contributions of direct contact and environmental contamination to the transmission of foot-and-mouth disease virus (FMDV) in a cattle herd using an individual-based model that includes both routes. Model parameters are inferred using approximate Bayesian computation with sequential Monte Carlo sampling (ABC-SMC) applied to data from transmission experiments and the 2007 epidemic in Great Britain. This demonstrates that the parameters derived from transmission experiments are applicable to outbreaks in the field, at least for closely related strains. Under the assumptions made in the model we show that environmental transmission likely contributes a majority of infections within a herd during an outbreak, although there is a lot of variation between simulated outbreaks. The accumulation of environmental contamination not only causes infections within a farm, but also has the potential to spread between farms via fomites. We also demonstrate the importance and effectiveness of rapid detection of infected farms in reducing transmission between farms, whether via direct contact or the environment.

Quantifying the relationship between within-host dynamics and transmission for viral diseases of livestock

Understanding the population dynamics of an infectious disease requires linking within-host dynamics and between-host transmission in a quantitative manner, but this is seldom done in practice. Here a simple phenomenological model for viral dynamics within a host is linked to between-host transmission by assuming that the probability of transmission is related to log viral titre. Data from transmission experiments for two viral diseases of livestock, foot-and-mouth disease virus in cattle and swine influenza virus in pigs, are used to parametrize the model and, importantly, test the underlying assumptions. The model allows the relationship between within-host parameters and transmission to be determined explicitly through their influence on the reproduction number and generation time. Furthermore, these critical within-host parameters (time and level of peak titre, viral growth and clearance rates) can be computed from more complex within-host models, raising the possibility of assessing the impact of within-host processes on between-host transmission in a more detailed quantitative manner.

A review targeting veterinary antibiotics removal from livestock manure management systems and future outlook

Veterinary antibiotics (VAs) contamination has been considered as a worldwide environmental and health concern in recent decades. This paper reviewed the variability of contents of VAs and their release from the animal breeding industry into the surrounding environment along with the performance of the manure treatment technologies. The data collected revealed that VAs were mostly excreted in animal feces and observed in manure, soil, water, and sediment. The findings illustrate the disparity of VAs in excretion rates, consumption, and their residues in the environment with relatively high distribution for tetracyclines, fluoroquinolones, and sulfonamides. Anaerobic digestion has a capacity to remove of 73% VAs while manure composting and constructed wetlands can remove 84.7%, and 90% VAs. Due to the profound effect of antibiotics on the environment, further research and intensive management strategies for livestock manure need to be designed to improve the removal efficiency and manure management technologies.

On-farm screw press and rotary drum treatment of dairy manure-associated antibiotic residues and resistance

An on-farm solid-liquid separator (SLS) and rotary drum composter (RD) manure treatment system was monitored for its impact on antibiotic residues and antibiotic resistance genes (ARGs). Administered antibiotics were tracked, and treatment system mass flows were quantified. Total amounts of antibiotic residues and ARGs were calculated from measured concentrations and mass flows. Only oxytetracycline (OTC) and sulfadimethoxine (SDM) were detected in the manure treatment system influent. No β-lactams were measured despite comprising ∼25% of the antibiotics administered. Nearly 80% of OTC and >90% of SDM partitioned into SLS liquid effluent (SL). The RD reduced the mass of OTC remaining in the SLS solid effluent (SS) significantly by 50%, whereas the mass of SDM appeared to increase after RD treatment. All four ARGs tested were detected in influent, with >70% of the sul1, bla_OXA-1 , and intI1 genes (normalized by the 16S ribosomal RNA gene) partitioning into the SL. In contrast, about eight times more normalized tetO gene copies partitioned into the SS than in the SL. All ARGs remaining in the SS were significantly reduced by the RD treatment, with a noteworthy 98% reduction in normalized tetO gene copies. This study provides insight into on-farm levels of antibiotic residues and ARGs in dairy manure, their partitioning during SLS treatment, and their fate after a high-temperature RD treatment reaching 72.2 ± 0.18 °C near the outlet. It also notes the importance of mass-flow standardization of data, and the need to work towards standardization of manure system sampling protocols for antibiotic residues and ARGs.

Replicating disease spread in empirical cattle networks by adjusting the probability of infection in random networks

Comparisons between mass-action or "random" network models and empirical networks have produced mixed results. Here we seek to discover whether a simulated disease spread through randomly constructed networks can be coerced to model the spread in empirical networks by altering a single disease parameter - the probability of infection. A stochastic model for disease spread through herds of cattle is utilised to model the passage of an SEIR (susceptible-latent-infected-resistant) through five networks. The first network is an empirical network of recorded contacts, from four datasets available, and the other four networks are constructed from randomly distributed contacts based on increasing amounts of information from the recorded network. A numerical study on adjusting the value of the probability of infection was conducted for the four random network models. We found that relative percentage reductions in the probability of infection, between 5.6% and 39.4% in the random network models, produced results that most closely mirrored the results from the empirical contact networks. In all cases tested, to reduce the differences between the two models, required a reduction in the probability of infection in the random network.

Bovine tuberculosis: within-herd transmission models to support and direct the decision-making process

Use of mathematical models to study the transmission dynamics of infectious diseases is becoming increasingly common in veterinary sciences. However, modeling chronic infectious diseases such as bovine tuberculosis (bTB) is particularly challenging due to the substantial uncertainty associated with the epidemiology of the disease. Here, the methodological approaches used to model bTB and published in the peer-reviewed literature in the last decades were reviewed with a focus on the impact that the models' assumptions may have had on their results, such as the assumption of density vs. frequency-dependent transmission, the existence of non-infectious and non-detectable stages, and the effect of extrinsic sources of infection (usually associated with wildlife reservoirs). Although all studies suggested a relatively low rate of within-herd transmission of bTB when test-and-cull programs are in place, differences in the estimated length of the infection stages, sensitivity and specificity of the tests used and probable type of transmission (density or frequency dependent) were observed. Additional improvements, such as exploring the usefulness of contact-networks instead of assuming homogeneous mixing of animals, may help to build better models that can help to design, evaluate and monitor control and eradication strategies against bTB.