Stochastic simulations of a multi-group compartmental model for Johne's disease on US dairy herds with test-based culling intervention

Investigating effective testing strategies for the control of Johne's disease in western Canadian cow-calf herds using an agent-based simulation model

Johne's disease is an insidious infectious disease of ruminants caused by Mycobacterium avium subspecies paratuberculosis (MAP). Johne's disease can have important implications for animal welfare and risks causing economic losses in affected herds due to reduced productivity, premature culling and replacement, and veterinary costs. Despite the limited accuracy of diagnostic tools, testing and culling is the primary option for controlling Johne's disease in beef herds. However, evidence to inform specific test and cull strategies is lacking. In this study, a stochastic, continuous-time agent-based model was developed to investigate Johne's disease and potential control options in a typical western Canadian cow-calf herd. The objective of this study was to compare different testing and culling scenarios that included varying the testing method and frequency as well as the number and risk profile of animals targeted for testing using the model. The relative effectiveness of each testing scenario was determined by the simulated prevalence of cattle shedding MAP after a 10-year testing period. A second objective was to compare the direct testing costs of each scenario to identify least-cost options that are the most effective at reducing within-herd disease prevalence. Whole herd testing with individual PCR at frequencies of 6 or 12 months were the most effective options for reducing disease prevalence. Scenarios that were also effective at reducing prevalence but with the lowest total testing costs included testing the whole herd with individual PCR every 24 months and testing the whole herd with pooled PCR every 12 months. The most effective method with the lowest annual testing cost per unit of prevalence reduction was individual PCR on the whole herd every 24 months. Individual PCR testing only cows that had not already been tested 4 times also ranked well when considering both final estimated prevalence at 10 years and cost per unit of gain. A more in-depth economic analysis is needed to compare the cost of testing to the cost of disease, taking into account costs of culling, replacements and impacts on calf crops, and to determine if testing is an economically attractive option for commercial cow-calf operations.

Predicting Positive ELISA Results in Dairy Herds with a Preferred Status in a Paratuberculosis Control Program

Dairy herds participating in the Dutch milk quality assurance program for paratuberculosis are assigned a herd status on the basis of herd examinations by ELISA of individual serum or milk samples, followed by an optional confirmatory fecal PCR. Test-negative herds are assigned Status A; the surveillance of these herds consists of biennial herd examinations. Farmers falsely believing that their Status A herds are Map-free may inadvertently refrain from preventive measures. Therefore, we aimed to develop a predictive model to alert Status A farmers at increased risk of future positive ELISA results. Using data of 8566 dairy herds with Status A in January 2016, two logistic regression models were built, with the probabilities of ≥1 or ≥2 positive samples from January 2017–June 2019 as dependent variables, and province, soil type, herd size, proportion of cattle born elsewhere, time since previous positive ELISA results, and the 95th percentile of the S/P ratios in 2015–2016, as explanatory variables. As internal validation, both models were applied to predict positive ELISA results from January 2019–June 2021, in 8026 herds with Status A in January 2019. The model predicting ≥1 positive sample had an area under the receiver-operating-characteristics curve of 0.76 (95% CI: 0.75, 0.77). At a cut-off predicted probability πc = 0.40, 25% of Status A herds would be alerted with positive and negative predictive values of 0.52 and 0.83, respectively. The model predicting ≥2 positive samples had lower positive, but higher negative, predictive values. This study indicates that discrimination of Status A herds with high and low risks of future positive ELISA results is feasible. This might stimulate farmers with the highest risks to take additional measures to control any undetected Map infections.

Rewiring cattle trade movements helps to control bovine paratuberculosis at a regional scale

Paratuberculosis is a worldwide disease mainly introduced through trade. Due to the low sensitivity of diagnostic tests, it is difficult to protect herds from purchasing infected animals. Our objective was to assess if rewiring trade networks to promote risk-based movements could reduce the spread of Mycobacterium avium subsp. paratuberculosis (MAP) between dairy cattle herds at a regional scale. Two levels of control strategies were assessed. At the between-herd scale, trade rewiring aimed to prevent animals from high-risk herds moving into low-risk herds. At the within-herd scale, complementary additional measures were considered based on the herd infection status, aiming to limit the within-herd spread by reducing calf exposure to adult faeces and culling more rapidly after positive test results. We used a stochastic individual-based and between-herd mechanistic epidemiological model adapted to the 12,857 dairy cattle herds located in Brittany, western France. We compared the regional spread of MAP using observed trade movements against a rewiring algorithm rendering trade movements risk-based. All females over two years old were tested. Based on the results, and taking into account the low test sensitivity, herds were annually assigned one of three statuses: A if the estimated true prevalence was below 7%, B if it ranged from 7 to 21 %, C otherwise. We also identified herds with a high probability of being MAP-free (AAA herds that had obtained an A status over three consecutive years) to assess the effect of decreasing their risk of purchasing infected animals on MAP regional spread. We showed that movement rewiring to prevent the sale of animals from high to low-prevalence herds reduces MAP regional spread. Targeting AAA herds made it possible to minimize the control effort to decrease MAP regional spread. However, animals purchased by AAA herds should have a moderate to high probability of being MAP-free, especially if the risk of purchasing animals from herds of unknown status cannot be managed. Improved hygiene and early culling of positive animals were relevant complementary on-farm control options to further decrease MAP spread. Future studies should identify how to define herd statuses to target optimal control measure combinations that could reduce the spread of MAP on a regional scale most effectively.

Electrochemical Detection of Serum Antibodies Against Mycobacterium avium Subspecies paratuberculosis

Mycobacterium avium subsp. paratuberculosis (MAP) causes a chronic inflammatory intestinal disease, called Johne's disease (JD) in many ruminants. In the dairy industry, JD is responsible for significant economic losses due to decreased milk production and premature culling of infected animals. Test-and-cull strategy in conjunction with risk management is currently recommended for JD control in dairy herds. However, current diagnostic tests are labor-intensive, time-consuming, and/or too difficult to operate on site. In this study, we developed a new method for the detection of anti-M. paratuberculosis antibodies from sera of M. paratuberculosis-infected animals. M. paratuberculosis antigen-coated magnetic beads were sequentially reacted with bovine serum followed by a horseradish peroxidase (HRP)-labeled secondary antibody. The reaction of HRP with its substrate was then quantitatively measured electrochemically using a redox-active probe, ferrocyanide. After optimization of electrochemical conditions and concentration of the redox-active probe, we showed that the new electrochemical detection method could distinguish samples of M. paratuberculosis-infected cattle from those of uninfected cattle with greater separation between the two groups of samples when compared with a conventional colorimetric testing method. Since electrochemical detection can be conducted with an inexpensive, battery-operated portable device, this new method may form a basis for the development of an on-site diagnostic system for JD.

Short communication: Seroprevalence of paratuberculosis in Italian water buffaloes (Bubalus bubalis) in the region of Campania

Paratuberculosis is a chronic enteric disease affecting virtually all ruminants, but only anecdotal information is currently available about the occurrence of this disease in water buffaloes (Bubalus bubalis). We carried out a survey study aimed at determining the prevalence of paratuberculosis in 2 provinces in the region of Campania, Italy, where about half of all Italian buffaloes are reared. From May 2017 to December 2018, we collected 201,175 individual serum samples from 995 buffalo herds. The sera were collected from animals over 24 mo old and were tested using a commercial ELISA test. The herd-level apparent prevalence result was 54.7%, and the animal-level apparent prevalence was 1.8%. The herd-level true prevalence was estimated using a Bayesian approach, demonstrating a high herd-level prevalence of paratuberculosis in water buffaloes from the Campania area. These findings suggest that the urgent adoption of paratuberculosis herd-control programs for water buffaloes in this area would be beneficial.

Implications of Cattle Trade for the Spread and Control of Infectious Diseases in Slovenia

The objectives of this study were to gain insight into the structure of the cattle trade network in Slovenia and to evaluate the potential for infectious disease spread through movements. The study considered cattle movements between different types of premises that occurred between August 1, 2011 and July 31, 2016 with the exclusion of the movements to the end nodes (e.g., slaughterhouses). In the first part, we performed a static network analysis on monthly and yearly snapshots of the network. These time scales reflect our interest in slowly spreading pathogens; namely Mycobacterium avium subsp. paratuberculosis (MAP), which causes paratuberculosis, a worldwide economically important disease. The results showed consistency in the network measures over time; nevertheless, it was evident that year to year contacts between premises were changing. The importance of individual premises for the network connectedness was highly heterogeneous and the most influential premises in the network were collection centers, mountain pastures, and pastures. Compared to random node removal, targeted removal informed by ranking based on local network measures from previous years was substantially more effective in network disassociation. Inclusion of the latest movement data improved the results. In the second part, we simulated disease spread using a Susceptible-Infectious (SI) model on the temporal network. The SI model was based on the empirically estimated true prevalence of paratuberculosis in Slovenia and four scenarios for probabilities of transmission. Different probabilities were realized by the generation of new networks with the corresponding proportion of contacts which were randomly selected from the original network. These diluted networks served as substrates for simulation of MAP spread. The probability of transmission had a significant influence on the velocity of disease spread through the network. The peaks in daily incidence rates of infected herds were observed at the end of the grazing period. Our results suggest that network analysis may provide support in the optimization of paratuberculosis surveillance and intervention in Slovenia. The approach of simulating disease spread on a diluted network may also be used to model other transmission pathways between herds.

A review of paratuberculosis in dairy herds - Part 2: On-farm control

Bovine paratuberculosis is a chronic infectious disease of cattle, caused by Mycobacterium avium subspecies paratuberculosis (MAP). This is the second in a two-part review of the epidemiology and control of paratuberculosis in dairy herds. Several negative production effects associated with MAP infection have been described, but perhaps the most significant concern in relation to the importance of paratuberculosis as a disease of dairy cattle is the potential link with Crohn's disease in humans. Milk is considered a potential transmission route to humans and it is recognised that pasteurisation does not necessarily eliminate the bacterium. Therefore, control must also include reduction of the levels of MAP in bulk milk supplied from dairy farms. There is little field evidence in support of specific control measures, although several studies seem to show a decreased prevalence associated with the implementation of a combined management and test-and-cull programme. Improvements in vaccination efficacy and reduced tuberculosis (TB) test interference may increase uptake of vaccination as a control option. Farmer adoption of best practice recommendations at farm level for the control of endemic diseases can be challenging. Improved understanding of farmer behaviour and decision making will help in developing improved communication strategies which may be more efficacious in affecting behavioural change on farm.

A data-driven individual-based model of infectious disease in livestock operation: A validation study for paratuberculosis

Chronic livestock diseases cause large financial loss and affect animal health and welfare. Controlling these diseases mostly requires precise information on both individual animal and population dynamics to inform the farmer’s decisions, but even successful control programmes do by no means assure elimination. Mathematical models provide opportunities to test different control and elimination options rather than implementing them in real herds, but these models require robust parameter estimation and validation. Fitting these models to data is a difficult task due to heterogeneities in livestock processes. In this paper, we develop an infectious disease modeling framework for a livestock disease (paratuberculosis) that is caused by Mycobacterium avium subsp. paratuberculosis (MAP). Infection with MAP leads to reduced milk production, pregnancy rates, and slaughter value and increased culling rates in cattle and causes significant economic losses to the dairy industry. These economic effects are particularly important motivations in the control and elimination of MAP. In this framework, an individual-based model (IBM) of a dairy herd was built and MAP infection dynamics was integrated. Once the model produced realistic dynamics of MAP infection, we implemented an evaluation method by fitting it to data from three dairy herds from the Northeast region of the US. The model fitting exercises used least-squares and parameter space searching methods to obtain the best-fitted values of selected parameters. The best set of parameters were used to model the effect of interventions. The results show that the presented model can complement real herd statistics where the intervention strategies suggest a reduction in MAP prevalence without elimination. Overall, this research not only provides a complete model for MAP infection dynamics in a dairy herd but also offers a method for estimating parameters by fitting IBM models.

Control measures to prevent the increase of paratuberculosis prevalence in dairy cattle herds: an individual-based modelling approach

Paratuberculosis, a gastrointestinal disease caused by Mycobacterium avium subsp. paratuberculosis (Map), can lead to severe economic losses in dairy cattle farms. Current measures are aimed at controlling prevalence in infected herds, but are not fully effective. Our objective was to determine the most effective control measures to prevent an increase in adult prevalence in infected herds. We developed a new individual-based model coupling population and infection dynamics. Animals are characterized by their age (6 groups) and health state (6 states). The model accounted for all transmission routes and two control measures used in the field, namely reduced calf exposure to adult faeces and test-and-cull. We defined three herd statuses (low, moderate, and high) based on realistic prevalence ranges observed in French dairy cattle herds. We showed that the most relevant control measures depend on prevalence. Calf management and test-and-cull both were required to maximize the probability of stabilizing herd status. A reduced calf exposure was confirmed to be the most influential measure, followed by test frequency and the proportion of infected animals that were detected and culled. Culling of detected high shedders could be delayed for up to 3 months without impacting prevalence. Management of low prevalence herds is a priority since the probability of status stabilization is high after implementing prioritized measures. On the contrary, an increase in prevalence was particularly difficult to prevent in moderate prevalence herds, and was only feasible in high prevalence herds if the level of control was high.

Evaluating the impact of transmission mode, calibration level and farmer compliance in simulation models of paratuberculosis in dairy herds

Simulation models can predict the outcome of different strategies for the control and eradication of paratuberculosis (PTB) in dairy herds. Two main transmission modes have previously been used to simulate the spread of PTB: direct (contact between animals) and indirect (through the environment). In addition, previous models were calibrated to either low or high within-herd prevalence levels, which we refer to as normal and low hygiene levels, respectively. We simulated both direct and indirect transmission with the same model in both normal and low hygiene level scenarios. The effectiveness of a test-and-cull strategy was dependent on the calibration level of the simulation model, and eradication occurred less frequently with the more biologically plausible indirect transmission mode. The results were compared to within-herd prevalence records from 314 dairy herds. The prevalence in 50% of the herds varied less than 0.9% per year on average, and less than 4% in 90% of the herds. We therefore conclude that the normal-hygiene scenario best describes most dairy herds in Denmark. Finally, we simulated different levels of farmer compliance with a test-and-cull strategy and found that a 60% compliance level was not sufficient to reach eradication within 10 years.

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.

Evaluation of fecal shedding and antibody response in dairy cattle infected with paratuberculosis using national surveillance data in Japan

Paratuberculosis or Johne's disease (JD), is a chronic infectious disease causing intractable diarrhea in cattle, which leads to less productivity, such as decreased milk yield, and lower daily weight gain. As a control measure against JD in cattle, national serological surveillance has been conducted in Japan since 1998. To conduct modeling studies that are useful to evaluate the effectiveness of control measures against JD, reliable parameter values, such as length of time from infection to the start of fecal shedding or antibody expression, are especially important. These parameters in the Japanese cattle population are assumed to be different from those in other countries with a higher prevalence of JD or in experimental infection settings; therefore, they must be estimated for the cattle population in Japan. Data from national surveillance conducted in Tokachi District, Hokkaido Prefecture, were used for this study. Using data from JD diagnostic tests for all cattle in Tokachi District between 1998 and 2014, all testing histories for infected animals were estimated as the number of tested cattle and positive cattle at each age of month for both fecal and antibody tests. A deterministic mathematical model for JD development, from infection to fecal shedding and antibody expression in infected cattle, was constructed to obtain the probability of testing positive when applied to both fecal and antibody tests at a given age. Likelihood was obtained from these estimated test results and best values for parameters were obtained using the Markov Chain Monte-Carlo method. Fifty-five percent of infected cattle were projected to have a transient shedding period, which was estimated to start 12 months after infection and last for 4 months. Persistent shedding was projected to occur in all infected cattle, and estimated to begin 7-84 months from infection. Following persistent shedding, antibody expression was estimated to start 7 months later. These values are useful for developing models to evaluate the status of JD infection and the effectiveness of control measures in the Japanese cattle population.

Which phenotypic traits of resistance should be improved in cattle to control paratuberculosis dynamics in a dairy herd: a modelling approach

Paratuberculosis is a worldwide disease causing production losses in dairy cattle herds. Variability of cattle response to exposure to Mycobacterium avium subsp. paratuberculosis (Map) has been highlighted. Such individual variability could influence Map spread at larger scale. Cattle resistance to paratuberculosis has been shown to be heritable, suggesting genetic selection could enhance disease control. Our objective was to identify which phenotypic traits characterising the individual course of infection influence Map spread in a dairy cattle herd. We used a stochastic mechanistic model. Resistance consisted in the ability to prevent infection and the ability to cope with infection. We assessed the effect of varying (alone and combined) fourteen phenotypic traits characterising the infection course. We calculated four model outputs 25 years after Map introduction in a naïve herd: cumulative incidence, infection persistence, and prevalence of infected and affected animals. A cluster analysis identified influential phenotypes of cattle resistance. An ANOVA quantified the contribution of traits to model output variance. Four phenotypic traits strongly influenced Map spread: the decay in susceptibility with age (the most effective), the quantity of Map shed in faeces by high shedders, the incubation period duration, and the required infectious dose. Interactions contributed up to 12% of output variance, highlighting the expected added-value of improving several traits simultaneously. Combinations of the four most influential traits decreased incidence to less than one newly infected animal per year in most scenarios. Future genetic selection should aim at improving simultaneously the most influential traits to reduce Map spread in cattle populations.

Use of an Individual-based Model to Control Transmission Pathways of Mycobacterium avium Subsp. paratuberculosis Infection in Cattle Herds

Johne’s disease (JD) is a chronic enteric disease in cattle caused by Mycobacterium avian subsp. paratuberculosis (MAP). Eradicating JD is a difficult task due to the long incubation period of MAP, inefficient diagnostic tests, and delayed clinical signs. Effective control strategies can help farmers to reduce prevalence, but those most acceptable to farmers combine specific information about lactation performance and testing results, which existing models do not provide. This paper presents an individual-based model of MAP infection dynamics and assesses the relative performance of the applied alternative control strategies. The base dairy herd model included the daily life events of a dairy cow and reflects several current dairy management processes. We then integrated MAP infection dynamics into the model. The model adopted four different test-based control strategies based on risk-based culling decisions and three hygiene scenarios. The model tracked the source of each infection and quantified the efficacy of each control strategy in reducing the risks of different transmission routes. The results suggest that risk-based culling can reduce prevalence compared with no control, but cannot eliminate the infection. Overall, this work provides not only a valuable tool to investigate MAP transmission dynamics but also offers adaptability to model similar infectious diseases.

Relative importance of herd-level risk factors for probability of infection with paratuberculosis in Irish dairy herds

Control of paratuberculosis is challenging due to the relatively poor performance of diagnostic tests, a prolonged incubation period, and protracted environmental survival. Prioritization of herd-level interventions is not possible because putative risk factors are often not supported by risk factor studies. The objective for this study was to investigate the relative importance of risk factors for an increased probability of herd paratuberculosis infection. Risk assessment data, comprehensive animal purchase history, and diagnostic test data were available for 936 Irish dairy herds. Both logistic regression and a Bayesian β regression on the outcome of a latent class analysis were conducted. Population attributable fractions and proportional reduction in variance explained were calculated for each variable in the logistic and Bayesian models, respectively. Routine use of the calving area for sick or lame cows was found to be a significant explanatory covariate in both models. Purchasing behavior for the previous 10 yr was not found to be significant. For the logistic model, length of time calves spend in the calving pen (25%) and routine use of the calving pen for sick or lame animals (14%) had the highest attributable fractions. For the Bayesian model, the overall R² was 16%. Dry cow cleanliness (7%) and routine use of the calving area for sick or lame cows (6%) and had the highest proportional reduction in variance explained. These findings provide support for several management practices commonly recommended as part of paratuberculosis control programs; however, a large proportion of the observed variation in probability of infection remained unexplained, suggesting other important risks factors may exist.

Epidemiological and economic consequences of purchasing livestock infected with Mycobacterium avium subsp. paratuberculosis

Background

Paratuberculosis (PTB) is a chronic disease which may lead to reduced milk yield, lower animal welfare and death in cattle. The causative agent is Mycobacterium avium subsp. paratuberculosis (MAP). The economic consequences are particularly important incentives in the control and eradication of the infection.

One strategy to control PTB in a herd is to purchase animals from farms with a low risk of MAP infection. We wanted to investigate the epidemiological and economic consequences of buying livestock from different supplier farms of low, medium or high risk, as well as farms with unknown status. We also wanted to estimate the probability of spontaneous fadeout if the farmer of an initially MAP-free herd bought a specified number of infected animals in a single year, or continually bought infected animals. This was achieved through simulation modeling, and the effects of consistently introducing one, five or ten infected animals annually into an initially infection-free herd was also modeled.

Results

Our findings show that once infected, a farm can relatively safely purchase animals from other low and medium-risk farms without experiencing an increase in the prevalence, highlighting the importance of certification programmes. Furthermore, farms free of MAP are highly susceptible and cannot purchase more than a small number of animals per year without having a high risk of being infected. The probability of spontaneous fadeout after 10 years was 82% when introducing a single infected animal into an initially MAP-free herd. When purchasing ten infected animals, this probability was 46%. The continual purchase of infected animals resulted in very low probabilities of spontaneous fadeout.

Conclusions

We demonstrated that MAP-free farms can purchase a small number of animals, preferably from certified farms, each year and still remain free of MAP. Already infected farms have little risk of increasing the prevalence on a farm when purchasing animals from other farms.

Comparative risk assessment for new cow-level Mycobacterium avium ssp. paratuberculosis infections between 3 dairy production types: Organic, conventional, and conventional-grazing systems

Johne's disease, a granulomatous enteritis of ruminant animals, is a hidden threat on dairy farms, adversely affecting animal welfare as well as herd productivity. Control programs in the United States advocate for specific management practices to temper the spread of the causal organism (Mycobacterium avium ssp. paratuberculosis, MAP), such as improving calving area hygiene and limiting introduction of replacement stock with unknown infection status. A need remains for direct exploration of Johne's disease prevention strategies in the United States with respect to production type. Alongside the growing demand for organic products, the safety of organic dairy practices with respect to MAP control is warranted. Further, conventional herds for which organic practices such as pasture grazing are used should be situated within the risk spectrum. We developed a risk assessment model using the US Voluntary Bovine Johne's Disease Control Program as a framework, with the goal of evaluating the risk of new cow-level MAP infections. A total of 292 organic and conventional farms in 3 states were surveyed on management practices, and an overall analysis was conducted in which each farm was first scored on individual practices using a range of "no risk" to "high risk," according to the literature. The sum of all risk factors was then analyzed to quantify and compare the risk burden for each production type. Organic herds received higher overall risk scores compared with both conventional grazing and nongrazing subtypes. To identify which factors contributed to the overall increased risk for organic herds, the management practices were categorized and evaluated by logistic regression. We determined that the increased risk incurred by organic herds was predominantly due to decisions made in the calving area and preweaned calf group. However, although certain individual risk factors related to calf management are commonly involved in prevention strategies (e.g., cow/calf separation) and were thus included in the overall risk assessment, empirical evidence linking them to the spread of MAP is lacking. Instead, these factors are problematic when executed with other management decisions, leading to a hypothesized synergism of transmission risk. To this end, we developed a set of compound risk factors, which were also evaluated as outcomes in logistic regression models, with production type serving as the predictor of interest. Organic farms in our study were more susceptible to risks associated with the synergism of study variables. Notably, organic producers were most likely to allow calves to spend extended time with the dam, while also lacking a dedicated calving area. Additionally, calves in organic herds were more often permitted to nurse even with poor udder hygiene on farm. A heightened vigilance toward calving area hygiene is therefore indicated for these herds.

A new compartmental model of Mycobacterium avium subsp. paratuberculosis infection dynamics in cattle

Models of Mycobacterium avium subsp. paratuberculosis (MAP), a chronic infectious agent of cattle, are used to identify effective control programs. However, new biological findings show that adult infections occur and that infected animals can be separated into 2 paths: animals that will become high-shedding and, eventually, experience clinical disease (high-path); and animals that will shed only small quantities of MAP and will remain subclinical (low-path). Longitudinal data analysis found that high-path animals progress more quickly than previously believed. A standard model of MAP transmission in dairy herds was modified to include adult low-path infections and 2 infection pathways for infected calves. Analysis of this model showed that adult infection may play an important role in MAP dynamics on a dairy farm, and that the increased rate of progression for high-path animals influences both the prevalence and the persistence of MAP on a dairy farm. This new model will be able to determine the effectiveness of MAP control programs more accurately than previous models.

Modelling of paratuberculosis spread between dairy cattle farms at a regional scale

Mycobacterium avium subsp. paratuberculosis (Map) causes Johne's disease, with large economic consequences for dairy cattle producers worldwide. Map spread between farms is mainly due to animal movements. Locally, herd size and management are expected to influence infection dynamics. To provide a better understanding of Map spread between dairy cattle farms at a regional scale, we describe the first spatio-temporal model accounting simultaneously for population and infection dynamics and indirect local transmission within dairy farms, and between-farm transmission through animal trade. This model is applied to Brittany, a French region characterized by a high density of dairy cattle, based on data on animal trade, herd size and farm management (birth, death, renewal, and culling) from 2005 to 2013 for 12,857 dairy farms. In all simulated scenarios, Map infection highly persisted at the metapopulation scale. The characteristics of initially infected farms strongly impacted the regional Map spread. Network-related features of incident farms influenced their ability to contaminate disease-free farms. At the herd level, we highlighted a balanced effect of the number of animals purchased: when large, it led to a high probability of farm infection but to a low persistence. This effect was reduced when prevalence in initially infected farms increased. Implications of our findings in the current enzootic situation are that the risk of infection quickly becomes high for farms buying more than three animals per year. Even in regions with a low proportion of infected farms, Map spread will not fade out spontaneously without the use of effective control strategies.

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.

Agent-based model for Johne's disease dynamics in a dairy herd

Johne’s disease is an infectious gastrointestinal disease in ruminants caused by Mycobacterium avium subsp. paratuberculosis that causes diarrhea, emaciation, decreased milk production and eventually death. The disease is transmitted in utero and via milk and colostrums to calves, and fecal-orally to all age classes. Financial losses due to the disease are estimated to be over $200 million in the US dairy industry. The goal of this study was to evaluate the cost effectiveness of control measures based on diagnosis with a sensitive ELISA, EVELISA. An agent-based, discrete time model was developed to simulate Johne’s disease dynamics in a US dairy herd. Spatial aspects of disease transmission were taken into account by using six spatial compartments. The effects on disease prevalence were studied with and without transmission routes included in the model. Further, using the model, cost effectiveness of ELISA-based Johne’s disease control was evaluated. Using the parameters we collected and assumed, our model showed the initial prevalence of Johne’s disease (33.1 ± 0.2%) in the farm increased to 87.7 ± 1.7% in a 10 year-simulation. When ELISA-based control measures were included in the simulation, the increase in prevalence was significantly slowed down, especially when EVELISA was used. However, the level of the prevalence was still higher than the initial level after 10 year simulation even with the ELISA-based diagnostic intervention. The prevalence was further reduced when quarterly ELISA testing was included. The cost analysis showed that the quarterly ELISA and EVELISA testing could bring $44.8 and $51.5/animal/year more revenues, respectively, to a dairy farm.

Back to the real world: connecting models with data

Mathematical models for infectious disease are often used to improve our understanding of infection biology or to evaluate the potential efficacy of intervention programs. Here, we develop a mathematical model that aims to describe infection dynamics of Mycobacterium avium subspecies paratuberculosis (MAP). The model was developed using current knowledge of infection biology and also includes some components of MAP infection dynamics that are currently still hypothetical. The objective was to show methods for parameter estimation of state transition models and to connect simulation models with detailed real life data. Thereby making model predictions and results of simulations more reflective and predictive of real world situations. Longitudinal field data from a large observational study are used to estimate parameter values. It is shown that precise data, including molecular diagnostics on the obtained MAP strains, results in more precise and realistic parameter estimates. It is argued that modeling of infection disease dynamics is of great value to understand the patho-biology, epidemiology and control of infectious diseases. The quality of conclusions drawn from model studies depend on two key issues; first, the quality of biology that has gone in the process of developing the model structure; second the quality of the data that go into the estimation of the parameters and the quality and quantity of the data that go into model validation. The more real world data that are used in the model building process, the more likely that modeling studies will provide novel, innovative and valid results.

Progression to multi-scale models and the application to food system intervention strategies

The aim of this article is to discuss how the systems science approach can be used to optimize intervention strategies in food animal systems. It advocates the idea that the challenges of maintaining a safe food supply are best addressed by integrating modeling and mathematics with biological studies critical to formulation of public policy to address these challenges. Much information on the biology and epidemiology of food animal systems has been characterized through single-discipline methods, but until now this information has not been thoroughly utilized in a fully integrated manner. The examples are drawn from our current research. The first, explained in depth, uses clinical mastitis to introduce the concept of dynamic programming to optimize management decisions in dairy cows (also introducing the curse of dimensionality problem). In the second example, a compartmental epidemic model for Johne's disease with different intervention strategies is optimized. The goal of the optimization strategy depends on whether there is a relationship between Johne's and Crohn's disease. If so, optimization is based on eradication of infection; if not, it is based on the cow's performance only (i.e., economic optimization, similar to the mastitis example). The third example focuses on food safety to introduce risk assessment using Listeria monocytogenes and Salmonella Typhimurium. The last example, practical interventions to effectively manage antibiotic resistance in beef and dairy cattle systems, introduces meta-population modeling that accounts for bacterial growth not only in the host (cow), but also in the cow's feed, drinking water and the housing environment. Each example stresses the need to progress toward multi-scale modeling. The article ends with examples of multi-scale systems, from food supply systems to Johne's disease. Reducing the consequences of foodborne illnesses (i.e., minimizing disease occurrence and associated costs) can only occur through an understanding of the system as a whole, including all its complexities. Thus the goal of future research should be to merge disciplines such as molecular biology, applied mathematics and social sciences to gain a better understanding of complex systems such as the food supply chain.

MODELING FOR COST ANALYSIS OF JOHNE'S DISEASE CONTROL BASED ON EVELISA TESTING

Use of enzyme-linked immunosorbent assay (ELISA) is recommended for control of Johne's disease (JD) in the cattle industry. A recent report showed that prevalence of JD in dairy farms could be reduced by applying an ELISA-based control strategy, even though the sensitivity of the current ELISA has been reported to be lower than 30%. We previously developed a more sensitive ELISA test (EVELISA; Ethanol Vortex ELISA) for diagnosis of JD and, in this report, aimed to evaluate cost-effectiveness of the EVELISA in JD control compared to that of a current ELISA test. For simulation of population dynamics, we developed a deterministic, discrete-time mathematical model incorporating contact structure, possibility of adult infection and the concept of order of events. In our model, the number of animals infected with the causative agent of JD, Mycobacterium avium subsp. paratuberculosis (MAP), increases in a 10-year simulation if no JD control measure is applied. When test results of ELISA or EVELISA are used for JD control, the increase in MAP-infected animals is less significant. According to our model, EVELISA-based control measures increase the annual per capita revenue of US dairy farms when compared to no JD control and ELISA-based JD control, respectively.

Competition for antigen between Th1 and Th2 responses determines the timing of the immune response switch during Mycobaterium avium subspecies paratuberulosis infection in ruminants

Johne's disease (JD), a persistent and slow progressing infection of ruminants such as cows and sheep, is caused by slow replicating bacilli Mycobacterium avium subspecies paratuberculosis (MAP) infecting macrophages in the gut. Infected animals initially mount a cell-mediated CD4 T cell response against MAP which is characterized by the production of interferon (Th1 response). Over time, Th1 response diminishes in most animals and antibody response to MAP antigens becomes dominant (Th2 response). The switch from Th1 to Th2 response occurs concomitantly with disease progression and shedding of the bacteria in feces. Mechanisms controlling this Th1/Th2 switch remain poorly understood. Because Th1 and Th2 responses are known to cross-inhibit each other, it is unclear why initially strong Th1 response is lost over time. Using a novel mathematical model of the immune response to MAP infection we show that the ability of extracellular bacteria to persist outside of macrophages naturally leads to switch of the cellular response to antibody production. Several additional mechanisms may also contribute to the timing of the Th1/Th2 switch including the rate of proliferation of Th1/Th2 responses at the site of infection, efficiency at which immune responses cross-inhibit each other, and the rate at which Th1 response becomes exhausted over time. Our basic model reasonably well explains four different kinetic patterns of the Th1/Th2 responses in MAP-infected sheep by variability in the initial bacterial dose and the efficiency of the MAP-specific T cell responses. Taken together, our novel mathematical model identifies factors of bacterial and host origin that drive kinetics of the immune response to MAP and provides the basis for testing the impact of vaccination or early treatment on the duration of infection.

Mycobacterium avium subsp. paratuberculosis (MAP) is the causative agent of Johne's disease, a chronic enteric disease of ruminants such as sheep and cows. Due to early culling and reduction in milk production of affected animals, MAP inflicts high economic cost to diary farms. MAP infection has a long incubation period of several years, and during the asymptomatic stage a strong cellular (T helper 1) immune response is thought to control MAP replication. Over time, Th1 response is lost and ineffective antibody response driven by Th2 cells becomes predominant. We develop the first mathematical model of helper T cell response to MAP infection to understand impact of various mechanisms on the dynamics of the switch from Th1 to Th2 response. Our results suggest that in contrast to the generally held belief, Th1/Th2 switch may be driven by the accumulation of long-lived extracellular bacteria, and therefore, may be the consequence of the disease progression of MAP-infected animals and not its cause. Our model highlights limitations of our current understanding of regulation of helper T cell responses during MAP infection and identifies areas for future experimental research.

Using vaccination to prevent the invasion of Mycobacterium avium subsp. paratuberculosis in dairy herds: a stochastic simulation study

Paratuberculosis, or Johne's disease (JD), is a chronic enteric disease of ruminants infected by Mycobacterium avium subsp. paratuberculosis (MAP) that causes a significant financial loss in dairy industry. To reduce prevalence and transmission in dairy herds infected with MAP, control programs have been implemented, including test-based culling, improved calf rearing management, and vaccination. The important issue of preventing MAP invasion into a MAP-free herd has been less investigated, however. The objective of this study was to examine whether vaccination was able to prevent MAP invasion in dairy cattle using a stochastic simulation approach. We developed a MAP vaccination model in which calves were vaccinated with a vaccine that is both imperfect in reducing the susceptibility of the host ('leaky') and that does not successfully immunize all calves ('failure in take'). Probability of MAP persistence and the number of infected animals in herds were computed for both control and vaccinated herds over a ten-year period after introduction of an initial infected heifer. Global parameter sensitivity analyses were performed to find the most influential parameters for MAP invasion. Our results show that vaccination of calves is effective in preventing MAP invasion, provided that the vaccine is of high efficacy in both reduction of susceptibility and 'take' effects; however, there is still a small chance (<0.15) that MAP can be sustained in herds over a long time (>10 years) due to vertical transmission. This study indicates that reduction in the transmission rate of high shedders (>50 CFU), the number of infected heifers initially introduced to herds, and vertical transmission are important to further decrease the probability of MAP becoming endemic and the overall number of infected animals in endemic herds. The simulation work is useful for designing vaccination programs aimed at preventing MAP invasion in MAP-free herds.

Accounting for uncertainty in model-based prevalence estimation: paratuberculosis control in dairy herds

Background

A common approach to the application of epidemiological models is to determine a single (point estimate) parameterisation using the information available in the literature. However, in many cases there is considerable uncertainty about parameter values, reflecting both the incomplete nature of current knowledge and natural variation, for example between farms. Furthermore model outcomes may be highly sensitive to different parameter values. Paratuberculosis is an infection for which many of the key parameter values are poorly understood and highly variable, and for such infections there is a need to develop and apply statistical techniques which make maximal use of available data.

Results

A technique based on Latin hypercube sampling combined with a novel reweighting method was developed which enables parameter uncertainty and variability to be incorporated into a model-based framework for estimation of prevalence. The method was evaluated by applying it to a simulation of paratuberculosis in dairy herds which combines a continuous time stochastic algorithm with model features such as within herd variability in disease development and shedding, which have not been previously explored in paratuberculosis models. Generated sample parameter combinations were assigned a weight, determined by quantifying the model’s resultant ability to reproduce prevalence data. Once these weights are generated the model can be used to evaluate other scenarios such as control options. To illustrate the utility of this approach these reweighted model outputs were used to compare standard test and cull control strategies both individually and in combination with simple husbandry practices that aim to reduce infection rates.

Conclusions

The technique developed has been shown to be applicable to a complex model incorporating realistic control options. For models where parameters are not well known or subject to significant variability, the reweighting scheme allowed estimated distributions of parameter values to be combined with additional sources of information, such as that available from prevalence distributions, resulting in outputs which implicitly handle variation and uncertainty. This methodology allows for more robust predictions from modelling approaches by allowing for parameter uncertainty and combining different sources of information, and is thus expected to be useful in application to a large number of disease systems.

Impact of imperfect Mycobacterium avium subsp. paratuberculosis vaccines in dairy herds: a mathematical modeling approach

The objective of this study was to investigate the potential impacts of imperfect Mycobacterium avium subsp. paratuberculosis (MAP) vaccines on the dynamics of MAP infection in US dairy herds using a mathematical modeling approach. Vaccine-based control programs have been implemented to reduce the prevalence of MAP infection in some dairy herds; however, MAP vaccines are imperfect. Vaccines can provide partial protection for susceptible calves, reduce the infectiousness of animals shedding MAP, lengthen the latent period of infected animals, slow the progression from low shedding to high shedding in infectious animals, and reduce clinical disease. To quantitatively study the impacts of imperfect MAP vaccines, we developed a deterministic multi-group vaccination model and performed global sensitivity analyses. Our results explain why MAP vaccination might have a beneficial, negligible, or detrimental effect in the reduction of prevalence and show that vaccines that are beneficial to individual animals may not be useful for a herd-level control plan. The study suggests that high efficacy vaccines that are aimed at reducing the susceptibility of the host are the most effective in controlling MAP transmission. This work indicates that MAP vaccination should be integrated into a comprehensive control program that includes test-and-cull intervention and improved calf rearing management.

Economic analysis of Mycobacterium avium subspecies paratuberculosis vaccines in dairy herds

Johne's disease, or paratuberculosis, is a chronic infectious enteric disease of ruminants, caused by infection with Mycobacterium avium ssp. paratuberculosis (MAP). Given the absence of a fail-safe method of prevention or a cure, Johne's disease can inflict significant economic loss on the US dairy industry, with an estimated annual cost of over $200 million. Currently available MAP control strategies include management measures to improve hygiene, culling MAP serologic- or fecal-positive adult cows, and vaccination. Although the 2 first control strategies have been reported to be effective in reducing the incidence of MAP infection, the changes in herd management needed to conduct these control strategies require significant effort on the part of the dairy producer. On the other hand, vaccination is relatively simple to apply and requires minor changes in herd management. Despite these advantages, only 5% of US dairy operations use vaccination to control MAP. This low level of adoption of this technology is due to limited information on its cost-effectiveness and efficacy and some important inherent drawbacks associated with current MAP vaccines. This study investigates the epidemiological effect and economic values of MAP vaccines in various stages of development. We create scenarios for the potential epidemiological effects of MAP vaccines, and then estimate economically justifiable monetary values at which vaccines become economically beneficial to dairy producers such that a net present value (NPV) of a farm's net cash flow can be higher than the NPV of a farm using no control or alternative nonvaccine controls. Any vaccination with either low or high efficacy considered in this study yielded a higher NPV compared with a no MAP control. Moreover, high-efficacy vaccines generated an even higher NPV compared with alternative controls, making vaccination economically attractive. Two high-efficacy vaccines were particularly effective in MAP control and NPV maximization. One was a high-efficacy vaccine that reduced susceptibility to MAP infection. The other was a high-efficacy vaccine that had multiple efficacies on the dynamics of MAP infection and disease progress. Only one high-efficacy vaccine, in which the vaccine is targeted at reducing MAP shedding and the number of clinical cases, was not economically beneficial to dairy producers compared with an alternative nonvaccine control, when herds were highly infected with MAP.

Strategies for time of culling in control of paratuberculosis in dairy herds

Effect of time for culling cows infected with Mycobacterium avium ssp. paratuberculosis on prevalence and profitability was identified through simulations. Seven test-and-cull strategies with different culling criteria and no attempts to close infection routes were compared with strategies with (1) no control and (2) closure of infection routes and no culling. The effects on true prevalence and gross margin were evaluated in a herd with typical reproduction management (heat detection rate of 38%). This was repeated in a herd with poor reproduction management (heat detection rate of 28%), because poor reproduction leads to lack of replacement animals, which was hypothesized to affect the economic effects of culling. Effects of varying prices of milk, replacement heifers, and hourly wages were also evaluated. The simulated results predicted that immediate culling after the first positive antibody ELISA test would be the most effective culling strategy to reduce prevalence. However, closing transmission routes was even more effective in reducing the prevalence. In the first 3 to 6 yr, all test-and-cull strategies reduced gross margin by US$5 to 55/stall per year. These losses were fully compensated by increased gross margin in yr 6 to 19. In the short run (7 yr with typical reproduction and 10 yr with poor reproduction), it was most profitable to cull test-positive cows when their milk yield decreased below 85% of that expected according to their parity and lactation stage, especially in herds with poor reproduction management. However, this strategy only stabilized the prevalence and did not reduce it. In the long term (>7 yr from implementation of a strategy), it was most profitable to cull cows immediately or as soon as possible after testing positive the first time. Varying milk prices did not affect the ranking between the different culling strategies. Increased market price (20%) of replacement heifers made all culling strategies less profitable and made culling based on a milk yield criterion the most profitable culling strategy for a longer period (11 to 13 yr). A 20% reduction in heifer price made immediate culling after a positive test the most profitable strategy overall in herds with typical reproduction, and after 9 yr in herd with poor reproduction. To conclude, the ideal culling strategy depends on the aim of intervention, the time horizon, and the reproductive capabilities combined with prices of replacement animals.

Modelling the effect of heterogeneity of shedding on the within herd Coxiella burnetii spread and identification of key parameters by sensitivity analysis

Coxiella burnetii is the bacterium responsible for Q fever, a worldwide zoonosis. Ruminants, especially cattle, are recognized as the most important source of human infections. Although a great heterogeneity between shedder cows has been described, no previous studies have determined which features such as shedding route and duration or the quantity of bacteria shed have the strongest impact on the environmental contamination and thus on the zoonotic risk. Our objective was to identify key parameters whose variation highly influences C. burnetii spread within a dairy cattle herd, especially those related to the heterogeneity of shedding. To compare the impact of epidemiological parameters on different dynamical aspects of C. burnetii infection, we performed a sensitivity analysis on an original stochastic model describing the bacterium spread and representing the individual variability of the shedding duration, routes and intensity as well as herd demography. This sensitivity analysis consisted of a principal component analysis followed by an ANOVA. Our findings show that the most influential parameters are the probability distribution governing the levels of shedding, especially in vaginal mucus and faeces, the characteristics of the bacterium in the environment (i.e. its survival and the fraction of bacteria shed reaching the environment), and some physiological parameters related to the intermittency of shedding (transition probability from a non-shedding infected state to a shedding state) or to the transition from one type of shedder to another one (transition probability from a seronegative shedding state to a seropositive shedding state). Our study is crucial for the understanding of the dynamics of C. burnetii infection and optimization of control measures. Indeed, as control measures should impact the parameters influencing the bacterium spread most, our model can now be used to assess the effectiveness of different control strategies of Q fever within dairy cattle herds.

Current perspectives on Mycobacterium avium subsp. paratuberculosis, Johne's disease, and Crohn's disease: a review

Mycobacterium avium subsp. paratuberculosis (MAP) causes the disease of cattle, Johne's. The economic impact of this disease includes early culling of infected cattle, reduced milk yield, and weight loss of cattle sold for slaughter. There is a possible link between MAP and Crohn's disease, a human inflammatory bowel disease. MAP is also a potential human food borne pathogen because it survives current pasteurization treatments. We review the current knowledge of MAP, Johne's disease and Crohn's disease and note directions for future work with this organism including rapid and economical detection, effective management plans and preventative measures.

Mathematical modeling of the transmission and control of foodborne pathogens and antimicrobial resistance at preharvest

Foodborne diseases are a significant health-care and economic burden. Most foodborne pathogens are enteric pathogens harbored in the gastrointestinal tract of farm animals. Understanding the transmission of foodborne pathogens and the dissemination of antimicrobial resistance at the farm level is necessary to design effective control strategies at preharvest. Mathematical models improve our understanding of pathogen dynamics by providing a theoretical framework in which factors affecting transmission and control of the pathogens can be explicitly considered. In this review, we aim to present the principles underlying the mathematical modeling of foodborne pathogens and antimicrobial resistance at the farm level to a broader audience.