Antimicrobial resistance and phylogenetic relatedness of extended-spectrum ß-lactamase (ESBL)-producing Escherichia coli in peridomestic rats (Rattus norvegicus and Rattus tanezumi) linked to city areas and animal farms in Hong Kong.

Extended-spectrum ß-lactamase-producing Escherichia (E.) coli (ESBL-EC) in the clinical setting have emerged as a major threat to public and animal health. Wildlife, including Rattus spp. may serve as reservoirs and spreaders of ESBL-EC in the environment. Peridomestic rats are well adapted to living in proximity to humans and animals in a variety of urban and agricultural environments and may serve as sentinels to identify variations of ESBL-EC within their different habitats. In this study, a set of 221 rats (Rattus norvegicus, R. tanezumi, R. andamanensis, and Niviventer huang) consisting of 104 rats from city areas, 44 from chicken farms, 52 from pig farms, and 21 from stables of horse-riding schools were screened for ESBL-EC. Overall, a total of 134 ESBL-EC were isolated from the caecal samples of 130 (59%) rats. The predominant blaESBL genes were blaCTX-M-14, blaCTX-M-15, blaCTX-M-55, and blaCTX-M-65. Phylogenetic analysis revealed a total of 62 sequence types (STs) and 17 SNP clusters. E. coli ST10 and ST155 were common to ESBL-EC from city areas and chicken farms, and ST44 were found among ESBL-EC from city areas and pig farms. Extra-intestinal pathogenic E. coli (ExPEC) ST69, ST131 and ST1193 were found exclusively among rats from city areas, and avian pathogenic E. coli (APEC) ST177 was restricted to ESBL-EC originating from chicken farms. Phylogenetic analysis showed that the populations of rodent ESBL-EC from city areas, chicken farms and pig farms were genetically different, suggesting a certain degree of partitioning between the human and animal locations. This study contributes to current understanding of ESBL-EC occurring in rats in ecologically diverse locations.

Analysis of Multidrug Resistance in Staphylococcus aureus with a Machine Learning-Generated Antibiogram.

Multidrug resistance (MDR) surveillance consists of reporting MDR prevalence and MDR phenotypes. Detailed knowledge of the specific associations underlying MDR patterns can allow antimicrobial stewardship programs to accurately identify clinically relevant resistance patterns. We applied machine learning and graphical networks to quantify and visualize associations between resistance traits in a set of 1,091 Staphylococcus aureus isolates collected from one New York hospital between 2008 and 2018. Antimicrobial susceptibility testing was performed using reference broth microdilution. The isolates were analyzed by year, methicillin susceptibility, and infection site. Association mining was used to identify resistance patterns that consisted of two or more individual antimicrobial resistance (AMR) traits and quantify the association among the individual resistance traits in each pattern. The resistance patterns captured the majority of the most common MDR phenotypes and reflected previously identified pairwise relationships between AMR traits in S. aureus Associations between ß-lactams and other antimicrobial classes (macrolides, lincosamides, and fluoroquinolones) were common, although the strength of the association among these antimicrobial classes varied by infection site and by methicillin susceptibility. Association mining identified associations between clinically important AMR traits, which could be further investigated for evidence of resistance coselection. For example, in skin and skin structure infections, clindamycin and tetracycline resistance occurred together 1.5 times more often than would be expected if they were independent from one another. Association mining efficiently discovered and quantified associations among resistance traits, allowing these associations to be compared between relevant subsets of isolates to identify and track clinically relevant MDR.

Evolutionary genomic and bacteria GWAS analysis of Mycobacterium avium subsp. paratuberculosis and dairy cattle Johne's disease phenotypes.

Mycobacterium avium subsp. paratuberculosis (MAP) is the causative agent of Johne's disease in ruminants, which has important health consequences for dairy cattle. The Regional Dairy Quality Management Alliance (RDQMA) project is a multistate research program involving MAP isolates taken from three intensively studied commercial dairy farms in the northeastern United States, which emphasized longitudinal data collection of both MAP isolates and animal health in three regional dairy herds for a period of about 7 years. This paper reports the results of a pan-GWAS analysis involving 318 MAP isolates and dairy cow Johne's disease phenotypes, taken from these three farms. Based on our highly curated accessory gene count the pan-GWAS analysis identified several MAP genes associated with bovine Johne's disease phenotypes scored from these three farms, with some of the genes having functions suggestive of possible cause/effect relationships to these phenotypes. This paper reports a pan-genomic comparative analysis between MAP and Mycobacterium tuberculosis, assessing functional Gene Ontology category enrichments between these taxa. Finally, we also provide a population genomic perspective on the effectiveness of herd isolation, involving closed dairy farms, in preventing MAP inter-farm cross infection on a micro-geographic scale.IMPORTANCE Mycobacterium avium subsp. paratuberculosis (MAP) is the causative agent of Johne's disease in ruminants, which has important health consequences for dairy cattle, and enormous economic consequences for the dairy industry. Understanding which genes in this bacterium are correlated with key disease phenotypes can lead to functional experiments targeting these genes and ultimately lead to improved control strategies. This study represents a rare example of a prolonged longitudinal study of dairy cattle where the disease was measured and the bacteria were isolated from the same cows. The genome sequences of over 300 MAP isolates were analyzed for genes that were correlated with a wide range of Johne's disease phenotypes. A number of genes were identified that were significantly associated with several aspects of the disease and suggestive of further experimental follow-up.

Comparison of China's and the European Union's Approaches to Antimicrobial Stewardship in the Pork Industry.

Antimicrobial resistance (AMR) is a recognized global public health concern. Although the link between antimicrobial usage in food animals and AMR in humans is established, the detailed interactions are unclear. Antimicrobial stewardship (AMS) in livestock was first implemented in Europe with Sweden as the pioneer in 1986. Despite this head start, AMR is still an ongoing challenge for Europe. The European Union (EU) is an established agriculture producer, the second largest pork producer globally, and one of the largest markets for organic food. China is the global leader in both production and consumption of pork. China's rise in prosperity has led to an increase in its pork demand. Chinese producers commonly use antimicrobials during production for disease treatment and prevention to meet this increased demand. China's rising prosperity together with recent publicized food safety scandals, disease outbreaks in domestic livestock products, and increased AMR awareness have resulted in an increased willingness to pay and demand for organic food by Chinese consumers. Responding to the growing concerns of AMR by consumers and the World Health Organization (WHO), the Chinese government introduced a national pilot program in 2016 to reduce unnecessary antimicrobial use. Compared with China, the EU is a different entity as it is a political union comprising diverse countries and although it may have more experience in AMS, both entities face similar issues with AMR and increasing demand for organic food. Increased interest in organic food has arisen due to concerns about AMR, food safety, outbreaks of bacterial food contamination, and animal welfare. This article aims to compare the different AMS strategies employed by each entity, China and the EU, and how the increased demand for organic produce globally also influences the effort to reduce antimicrobial use in these entities' pork industries.

Modeling the Effect of Tylosin Phosphate on Macrolide-Resistant Enterococci in Feedlots and Reducing Resistance Transmission.

Tylosin phosphate (TYL) is administered to more than 50% of U.S. beef cattle to reduce the incidence of liver abscesses but may increase the risk of macrolide-lincosamide-streptogramin-resistant bacteria disseminating from the feedlot. Limited evidence has been collected to understand how TYL affects the proportion of resistant bacteria in cattle or the feedlot environment. We created a mathematical model to investigate the effects of TYL administration on Enterococcus dynamics and examined preharvest strategies to mitigate the impact of TYL administration on resistance. The model simulated the physiological pharmacokinetics of orally administered TYL and estimated the pharmacodynamic effects of TYL on populations of resistant and susceptible Enterococcus within the cattle large intestine, feedlot pen, water trough, and feed bunk. The model parameters' population distributions were based on the available literature; 1000 Monte Carlo simulations were performed to estimate the likely distribution of outcomes. At the end of the simulated treatment period, the median estimated proportion of macrolide-resistant enterococci was only 1 percentage point higher within treated cattle compared with cattle not fed TYL, in part because the TYL concentrations in the large intestine were substantially lower than the enterococci minimum inhibitory concentrations. However, 25% of the simulated cattle had a >10 percentage point increase in the proportion of resistant enterococci associated with TYL administration, termed the TYL effect. The model predicts withdrawing TYL treatment and moving cattle to an antimicrobial-free terminal pen with a low prevalence of resistant environmental enterococci for as few as 6 days could reduce the TYL effect by up to 14 percentage points. Additional investigation of the importance of this subset of cattle to the overall risk of resistance transmission from feedlots will aid in the interpretation and implementation of resistance mitigation strategies.

Disease management at the wildlife-livestock interface: Using whole-genome sequencing to study the role of elk in Mycobacterium bovis transmission in Michigan, USA.

The role of wildlife in the persistence and spread of livestock diseases is difficult to quantify and control. These difficulties are exacerbated when several wildlife species are potentially involved. Bovine tuberculosis (bTB), caused by Mycobacterium bovis, has experienced an ecological shift in Michigan, with spillover from cattle leading to an endemically infected white-tailed deer (deer) population. It has potentially substantial implications for the health and well-being of both wildlife and livestock and incurs a significant economic cost to industry and government. Deer are known to act as a reservoir of infection, with evidence of M. bovis transmission to sympatric elk and cattle populations. However, the role of elk in the circulation of M. bovis is uncertain; they are few in number, but range further than deer, so may enable long distance spread. Combining Whole Genome Sequences (WGS) for M. bovis isolates from exceptionally well-observed populations of elk, deer and cattle with spatiotemporal locations, we use spatial and Bayesian phylogenetic analyses to show strong spatiotemporal admixture of M. bovis isolates. Clustering of bTB in elk and cattle suggests either intraspecies transmission within the two populations, or exposure to a common source. However, there is no support for significant pathogen transfer amongst elk and cattle, and our data are in accordance with existing evidence that interspecies transmission in Michigan is likely only maintained by deer. This study demonstrates the value of whole genome population studies of M. bovis transmission at the wildlife-livestock interface, providing insights into bTB management in an endemic system.

Assessment of the bovine tuberculosis elimination protocol in the United States.

In this study, we analyzed the performance of the USDA's bovine tuberculosis (bTB) elimination protocol in a 1,000-cow closed dairy herd using an agent-based simulation model under different levels of initial bTB infection. We followed the bTB test sensitivity and specificity values used by the USDA in its model assessment. We estimated the net present value over a 20-yr horizon for a bTB-free milking herd and for bTB-infected herds following the USDA protocol. We estimated the expected time to identify the infection in the herd once it is introduced, its elimination time, the reproductive number (R0), and effective reproduction number (Re) under the USDA protocol. The optimal number of consecutive negative whole-herd tests (WHT) needed to declare a herd bTB-free with a 95% confidence under different bTB prevalence levels was derived. Our results support the minimum number of consecutive negative WHT required by the USDA protocol to declare a herd bTB-free; however, the number of consecutive negative WHT needed to eliminate bTB in a herd depends on the sensitivity and specificity of the tests. The robustness of the protocol was analyzed under conservative bTB test parameters from the literature. The cost of implementing the USDA protocol when 1 infected heifer is introduced in a 1,000-cow dairy herd is about $1,523,161. The average time until detection and the time required to eliminate bTB-infected animals from the herd, after 1 occult animal is introduced in the herd, were 735 and 119 d, respectively.

Postharvest Supply Chain with Microbial Travelers: a Farm-to-Retail Microbial Simulation and Visualization Framework.

Fresh produce supply chains present variable and diverse conditions that are relevant to food quality and safety because they may favor microbial growth and survival following contamination. This study presents the development of a simulation and visualization framework to model microbial dynamics on fresh produce moving through postharvest supply chain processes. The postharvest supply chain with microbial travelers (PSCMT) tool provides a modular process modeling approach and graphical user interface to visualize microbial populations and evaluate practices specific to any fresh produce supply chain. The resulting modeling tool was validated with empirical data from an observed tomato supply chain from Mexico to the United States, including the packinghouse, distribution center, and supermarket locations, as an illustrative case study. Due to data limitations, a model-fitting exercise was conducted to demonstrate the calibration of model parameter ranges for microbial indicator populations, i.e., mesophilic aerobic microorganisms (quantified by aerobic plate count and here termed APC) and total coliforms (TC). Exploration and analysis of the parameter space refined appropriate parameter ranges and revealed influential parameters for supermarket indicator microorganism levels on tomatoes. Partial rank correlation coefficient analysis determined that APC levels in supermarkets were most influenced by removal due to spray water washing and microbial growth on the tomato surface at postharvest locations, while TC levels were most influenced by growth on the tomato surface at postharvest locations. Overall, this detailed mechanistic dynamic model of microbial behavior is a unique modeling tool that complements empirical data and visualizes how postharvest supply chain practices influence the fate of microbial contamination on fresh produce.IMPORTANCE Preventing the contamination of fresh produce with foodborne pathogens present in the environment during production and postharvest handling is an important food safety goal. Since studying foodborne pathogens in the environment is a complex and costly endeavor, computer simulation models can help to understand and visualize microorganism behavior resulting from supply chain activities. The postharvest supply chain with microbial travelers (PSCMT) model, presented here, provides a unique tool for postharvest supply chain simulations to evaluate microbial contamination. The tool was validated through modeling an observed tomato supply chain. Visualization of dynamic contamination levels from harvest to the supermarket and analysis of the model parameters highlighted critical points where intervention may prevent microbial levels sufficient to cause foodborne illness. The PSCMT model framework and simulation results support ongoing postharvest research and interventions to improve understanding and control of fresh produce contamination.

Expanding behavior pattern sensitivity analysis with model selection and survival analysis.

BACKGROUND: Sensitivity analysis is an essential step in mathematical modeling because it identifies parameters with a strong influence on model output, due to natural variation or uncertainty in the parameter values. Recently behavior pattern sensitivity analysis has been suggested as a method for sensitivity analyses on models with more than one mode of output behavior. The model output is classified by behavior mode and several behavior pattern measures, defined by the researcher, are calculated for each behavior mode. Significant associations between model inputs and outputs are identified by building linear regression models with the model parameters as independent variables and the behavior pattern measures as the dependent variables. We applied the behavior pattern sensitivity analysis to a mathematical model of tetracycline-resistant enteric bacteria in beef cattle administered chlortetracycline orally. The model included 29 parameters related to bacterial population dynamics, chlortetracycline pharmacokinetics and pharmacodynamics. The prevalence of enteric resistance during and after chlortetracycline administration was the model output. Cox proportional hazard models were used when linear regression assumptions were not met. RESULTS: We have expanded the behavior pattern sensitivity analysis procedure by incorporating model selection techniques to produce parsimonious linear regression models that efficiently prioritize input parameters. We also demonstrate how to address common violations of linear regression model assumptions. Finally, we explore the semi-parametric Cox proportional hazards model as an alternative to linear regression for situations with censored data. In the example mathematical model, the resistant bacteria exhibited three behaviors during the simulation period: (1) increasing, (2) decreasing, and (3) increasing during antimicrobial therapy and decreasing after therapy ceases. The behavior pattern sensitivity analysis identified bacterial population parameters as high importance in determining the trajectory of the resistant bacteria population. CONCLUSIONS: Interventions aimed at the enteric bacterial population ecology, such as diet changes, may be effective at reducing the prevalence of tetracycline-resistant enteric bacteria in beef cattle. Behavior pattern sensitivity analysis is a useful and flexible tool for conducting a sensitivity analysis on models with varied output behavior, enabling prioritization of input parameters via regression model selection techniques. Cox proportional hazard models are an alternative to linear regression when behavior pattern measures are censored or linear regression assumptions cannot be met.

Transmission dynamics of intramammary infections caused by Corynebacterium species.

The development of reliable models for transmission of intramammary infections (IMI) is the subject of extensive research. Such models are useful to enhance the identification and understanding of factors that affect pathogen-specific IMI dynamics. Longitudinal transmission models are valuable for predicting infection outbreak risks, quantifying the effectiveness of response tactics, and performing response planning. In this work, we focused on modeling Corynebacterium spp. by using a compartmental model. Previous investigations have considered modeling the transmission dynamics of several bacterial pathogens, but not Corynebacterium spp. We established a Corynebacterium spp. Susceptible-Infectious-Susceptible (SIS) model. We simulated the model numerically by using parameters that we estimated by a generalized linear model approach, using month of study as the time variable. The data, from which the parameters of the model were estimated, were obtained in a field trial conducted in 2 US dairy herds. Altogether, 786 cows were sampled at least once during the 13-mo study period. The total number of quarter milk cultures and cases of IMI caused by Corynebacterium spp. were 11,744 and 556, respectively, in farm A; the corresponding figures for farm B were 11,804 and 179. Our modeling study included only transmission from persistent IMI caused by Corynebacterium spp. within the lactation pens. The rate of new infections was significantly related to preexisting IMI in both farms, underscoring the importance of preexisting Corynebacterium spp. IMI for the transmission of Corynebacterium spp. within lactation pens. The estimated basic reproduction numbers (R0) in the 2 farms were 1.18 and 0.98, respectively. The nonsignificant disparity in R0 was associated with significant differences in cure rates between farms.

An agent-based model evaluation of economic control strategies for paratuberculosis in a dairy herd.

This paper uses an agent-based simulation model to estimate the costs associated with Mycobacterium avium ssp. paratuberculosis (MAP), or Johne's disease, in a milking herd, and to determine the net benefits of implementing various control strategies. The net present value (NPV) of a 1,000-cow milking herd is calculated over 20 yr, parametrized to a representative US commercial herd. The revenues of the herd are generated from sales of milk and culled animals. The costs include all variable and fixed costs necessary to operate a representative 1,000-cow milking herd. We estimate the NPV of the herd with no MAP infection, under an expected endemic infection distribution with no controls, and under an expected endemic infection distribution with various controls. The initial number of cows in a herd with an endemic MAP infection is distributed as 75% susceptible, 13% latent, 9% low MAP shedding, and 3% high MAP shedding. Control strategies include testing using ELISA and fecal culture tests and culling of cows that test positive, and culling based on observable milk production decrease. Results show that culling cows based on test results does not increase the herd's NPV and in most cases decreases NPV due to test costs as well as false positives and negatives with their associated costs (e.g., culling healthy cows and keeping infected cows). Culling consistently low producing cows when MAP is believed to be present in the herd produces higher NPV over the strategy of testing and culling MAP infected animals, and over the case of no MAP control.

Associations of the first occurrence of pathogen-specific clinical mastitis with milk yield and milk composition in dairy cows.

The aim of this study was to estimate the associations of the first occurrence of pathogen-specific clinical mastitis (CM) with milk yield and milk composition (somatic cell count (SCC), lactose, fat, protein content in milk and milk urea nitrogen (MUN)). We studied 3149 dairy cows in 31 Hokkaido dairy farms in Japan. Five pathogen groups were studied: Streptococcus spp.; Staphylococcus aureus (S. aureus); coagulase-negative staphylococci (CNS); coliforms; and fungi. Test-day milk data and clinical records were collected from June 2011 until February 2014. Mixed models with an autoregressive correlation structure were fitted to quantify the effects of CM and several other control variables (herd, calving season, parity, week of lactation, and other diseases). Primipara (first lactation) and multipara (second and later lactations) were analysed separately. All pathogens, particularly S. aureus and fungi, were associated with significant milk losses in multipara. In this study, S. aureus and CNS infections were not associated with significant milk loss in primipara. All pathogens, in particular S. aureus and fungi, significantly increased SCC in both parity groups. All pathogens, especially CNS (in primipara) and S. aureus (in multipara), decreased lactose content. All pathogen groups except for fungi were associated with significant changes in fat, protein and MUN. Some pathogens such as Streptococcus spp. and coliforms seemed to be associated with long-term fat, protein and MUN changes. These findings provide estimates that could be used to calculate precise costs of CM, and also provide better indicators of pathogen-specific mastitis.

Optimally achieving milk bulk tank somatic cell count thresholds.

High somatic cell count in milk leads to reduced shelf life in fluid milk and lower processed yields in manufactured dairy products. As a result, farmers are often penalized for high bulk tank somatic cell count or paid a premium for low bulk tank somatic cell count. Many countries also require all milk from a farm to be lower than a specified regulated somatic cell count. Thus, farms often cull cows that have high somatic cell count to meet somatic cell count thresholds. Rather than naïvely cull the highest somatic cell count cows, a mathematical programming model was developed that determines the cows to be culled from the herd by maximizing the net present value of the herd, subject to meeting any specified bulk tank somatic cell count level. The model was applied to test-day cows on 2 New York State dairy farms. Results showed that the net present value of the herd was increased by using the model to meet the somatic cell count restriction compared with naïvely culling the highest somatic cell count cows. Implementation of the model would be straightforward in dairy management decision software.

Monitoring Antimicrobial Resistance in the Food Supply Chain and Its Implications for FDA Policy Initiatives.

In response to concerning increases in antimicrobial resistance (AMR), the Food and Drug Administration (FDA) has decided to increase veterinary oversight requirements for antimicrobials and restrict their use in growth promotion. Given the high stakes of this policy for the food supply, economy, and human and veterinary health, it is important to rigorously assess the effects of this policy. We have undertaken a detailed analysis of data provided by the National Antimicrobial Resistance Monitoring System (NARMS). We examined the trends in both AMR proportion and MIC between 2004 and 2012 at slaughter and retail stages. We investigated the makeup of variation in these data and estimated the sample and effect size requirements necessary to distinguish an effect of the policy change. Finally, we applied our approach to take a detailed look at the 2005 withdrawal of approval for the fluoroquinolone enrofloxacin in poultry water. Slaughter and retail showed similar trends. Both AMR proportion and MIC were valuable in assessing AMR, capturing different information. Most variation was within years, not between years, and accounting for geographic location explained little additional variation. At current rates of data collection, a 1-fold change in MIC should be detectable in 5 years and a 6% decrease in percent resistance could be detected in 6 years following establishment of a new resistance rate. Analysis of the enrofloxacin policy change showed the complexities of the AMR policy with no statistically significant change in resistance of both Campylobacter jejuni and Campylobacter coli to ciprofloxacin, another second-generation fluoroquinolone.

Analysis of Mycobacterium avium subsp. paratuberculosis mutant libraries reveals loci-dependent transposition biases and strategies for novel mutant discovery.

Mycobacterium avium subsp. paratuberculosis (MAP), the aetiological agent of Johne's disease, is one of the most important bacterial pathogens in ruminants. A thorough understanding of MAP pathogenesis is needed to develop new vaccines and diagnostic tests. The generation of comprehensive random transposon mutant libraries is a fundamental genetic technology to determine the role of genes in physiology and pathogenesis. In this study, whole MAP genome analysis compared the insertion sites for the mycobacterial transposon Tn5367 derived from the Mycobacterium smegmatis insertion sequence IS1096 and the mariner transposon MycoMarT7 carrying the Himar1 transposase. We determined that only MycoMarT7 provides a random representation of insertions in 99 % of all MAP genes. Analysis of the MAP K-10 genome indicated that 710 of all ORFs do not possess IS1096 recognition sites, while only 37 do not have the recognition site for MycoMarT7. Thus, a significant number of MAP genes remain underrepresented in insertion libraries from IS1096-derived transposons. Analysis of MycoMarT7 and Tn5367 mutants showed that Tn5367 has a predilection to insert within intergenic regions, suggesting that MycoMarT7 is the more adequate for generating a comprehensive library. However, we uncovered the novel finding that both transposons have loci-dependent biases, with Tn5367 being the most skewed. These loci-dependent transposition biases led to an underestimation of the number of independent mutants required to generate a comprehensive mutant library, leading to an overestimation of essential genes. Herein, we also demonstrated a useful platform for gene discovery and analysis by isolating three novel mutants for each transposon.

Validation of a Previously Developed Geospatial Model That Predicts the Prevalence of Listeria monocytogenes in New York State Produce Fields.

Technological advancements, particularly in the field of geographic information systems (GIS), have made it possible to predict the likelihood of foodborne pathogen contamination in produce production environments using geospatial models. Yet, few studies have examined the validity and robustness of such models. This study was performed to test and refine the rules associated with a previously developed geospatial model that predicts the prevalence of Listeria monocytogenes in produce farms in New York State (NYS). Produce fields for each of four enrolled produce farms were categorized into areas of high or low predicted L. monocytogenes prevalence using rules based on a field's available water storage (AWS) and its proximity to water, impervious cover, and pastures. Drag swabs (n = 1,056) were collected from plots assigned to each risk category. Logistic regression, which tested the ability of each rule to accurately predict the prevalence of L. monocytogenes, validated the rules based on water and pasture. Samples collected near water (odds ratio [OR], 3.0) and pasture (OR, 2.9) showed a significantly increased likelihood of L. monocytogenes isolation compared to that for samples collected far from water and pasture. Generalized linear mixed models identified additional land cover factors associated with an increased likelihood of L. monocytogenes isolation, such as proximity to wetlands. These findings validated a subset of previously developed rules that predict L. monocytogenes prevalence in produce production environments. This suggests that GIS and geospatial models can be used to accurately predict L. monocytogenes prevalence on farms and can be used prospectively to minimize the risk of preharvest contamination of produce.

Modeling of Mycobacterium avium subsp. paratuberculosis dynamics in a dairy herd: An individual based approach.

In the dairy industry, Johne's disease (JD), caused by Mycobacterium avium subsp. paratuberculosis (MAP) is one of the major investigated diseases. To date, researchers have suggested some control strategies for JD, such as test-and-cull based herd management, isolated calf rearing management, and vaccinations. Due to the slow progressing nature of MAP, tests with low diagnostic test sensitivity and specificity, and economic limitations, implementing these strategies has not resulted in elimination of MAP from farms. To date, no study has integrated detailed dairy herd dynamics with different MAP transmission routes. We have developed an individual-based dairy herd model by incorporating basic herd dynamics in a closed herd environment where no new animals have been bought from outside. The model considered three age groups of animals: calves, heifers and adults. It includes sequential life events of a dairy animal and such key dynamic processes of the dairy herd as lactation cycle, calving, voluntary waiting period, insemination, pregnancy, dry-off period and calf and heifer rearing. After initially validating that the model reproduced typical herd dynamics, it was extended by incorporating MAP infection dynamics, where each individual adult animal belonged to one of four infection compartments: susceptible, latent, low shedding and high shedding. The model includes two disease transmission routes: horizontal transmission (i.e., fecal-oral) and vertical transmission (i.e., in utero infection). The results confirm that this model can simulate a realistic dairy herd and that inclusion of the above-mentioned dynamic processes provides useful information about individual infected animals to farmers. Access to the individual animal information offers more validity to assessment of appropriate control strategies for an endemically MAP infected herd. This model can serve as an accurate and novel tool not only to better understand MAP dynamics, but is also valuable as an individual based system of a typical dairy herd that can be applied to other research questions.

Evidence of no protection for a recurrent case of pathogen specific clinical mastitis from a previous case.

The objective of this study was to determine whether the occurrence of a previous case of pathogen-specific clinical mastitis (CM) protects Holstein dairy cows against a recurrent case. Pathogens studied were Escherichia coli, Staphylococcus aureus, Staphylococcus spp., Streptococcus spp., Klebsiella spp., and Trueperella pyogenes. A total of 40 864 lactations (17,265 primiparous and 23,599 multiparous) from 19 835 cows from 5 large, high milk producing New York State dairy herds were analysed. We estimated the effects of parity, calving diseases, milk yield, current season and number of CM cases in the previous lactation on the risk of a first CM case using generalised linear mixed models with a log link and Poisson error distribution. The aforementioned risk factors and the occurrence of previous cases of pathogen-specific CM within the current lactation were evaluated as risks for second and third cases of pathogen-specific CM. Cows with more CM cases in the previous lactation were at greater risk of pathogen-specific CM in the current lactation. Multiparous cows were at greater risk of a second CM case if they had suffered from a first CM case that was caused by the same pathogen as the second case. In contrast, a second CM case generally put cows at greater risk of a third case, irrespective of whether the third case was caused by the same or a different pathogen. Our results showed that a previous case of pathogen specific CM does not generally protect against a recurrent case.

The value of pathogen information in treating clinical mastitis.

The objective of this study was to determine the economic value of obtaining timely and more accurate clinical mastitis (CM) test results for optimal treatment of cows. Typically CM is first identified when the farmer observes recognisable outward signs. Further information of whether the pathogen causing CM is Gram-positive, Gram-negative or other (including no growth) can be determined by using on-farm culture methods. The most detailed level of information for mastitis diagnostics is obtainable by sending milk samples for culture to an external laboratory. Knowing the exact pathogen permits the treatment method to be specifically targeted to the causation pathogen, resulting in less discarded milk. The disadvantages are the additional waiting time to receive test results, which delays treating cows, and the cost of the culture test. Net returns per year (NR) for various levels of information were estimated using a dynamic programming model. The Value of Information (VOI) was then calculated as the difference in NR using a specific level of information as compared to more detailed information on the CM causative agent. The highest VOI was observed where the farmer assumed the pathogen causing CM was the one with the highest incidence in the herd and no pathogen specific CM information was obtained. The VOI of pathogen specific information, compared with non-optimal treatment of Staphylococcus aureus where recurrence and spread occurred due to lack of treatment efficacy, was $20.43 when the same incorrect treatment was applied to recurrent cases, and $30.52 when recurrent cases were assumed to be the next highest incidence pathogen and treated accordingly. This indicates that negative consequences associated with choosing the wrong CM treatment can make additional information cost-effective if pathogen identification is assessed at the generic information level and if the pathogen can spread to other cows if not treated appropriately.

Longitudinal data collection of Mycobacterium avium subspecies Paratuberculosis infections in dairy herds: the value of precise field data.

Longitudinal infection data on Mycobacterium avium subspecies paratuberculosis (MAP) was collected on three dairy farms in Northeastern United States during approximately 10 years. Precise data on animal characteristics and animal location within farm were collected on these farms. Cows were followed over time with regard to MAP status during biannual fecal and serum sampling and quarterly serum sampling. Approximately 13 000 serum samples, 6500 fecal samples and 2000 tissue samples were collected during these years. Prevalence of positive samples was 1.4% for serological samples, 2.2% in fecal samples and 16.7% in tissue samples. Infection dynamics of MAP was studied and resulted in a number of potential changes in our understanding of MAP infection dynamics. First, a high prevalence of MAP infection was observed in these herds due to lifetime follow up of cows, including slaughter. Second, two distinctly different infection patterns were observed, so called non-progressors and progressors. Non-progressors were characterized by intermittent and low shedding of MAP bacteria and a virtual absence of a humoral immune response. Progressors were characterized by continuous and progressive shedding and a clearly detectable and progressive humoral immune response. Strain typing of MAP isolates on the three farms identified on two of three farms a dominant strain type, indicating that some strains are more successful in terms of transmission and infection progression. Continuous high quality longitudinal data collection turned out to be an essential tool in our understanding of pathobiology and epidemiology of MAP infections in dairy herds.