An immuno-epidemiological model for Johne's disease in cattle

Alternatives to antibiotics in veterinary medicine: considerations for the management of Johne's disease

Antibiotic resistance has become a major health concern globally, with current predictions expecting deaths related to resistant infections to surpass those of cancer by 2050. Major efforts are being undertaken to develop derivative and novel alternatives to current antibiotic therapies in human medicine. What appears to be lacking however, are similar efforts into researching the application of those alternatives, such as (bacterio)phage therapy, in veterinary contexts. Agriculture is still undoubtedly the most prominent consumer of antibiotics, with up to 70% of annual antibiotic usage attributed to this sector, despite policies to reduce their use in food animals. This not only increases the risk of resistant infections spreading from farm to community but also the risk that animals may acquire species-specific infections that subvert treatment. While these diseases may not directly affect human welfare, they greatly affect the profit margin of industries reliant on livestock due to the cost of treatments and (more frequently) the losses associated with animal death. This means actively combatting animal infection not only benefits animal welfare but also global economies. In particular, targeting recurring or chronic conditions associated with certain livestock has the potential to greatly reduce financial losses. This can be achieved by developing novel diagnostics to quickly identify ill animals alongside the design of novel therapies. To explore this concept further, this review employs Johne's disease, a chronic gastroenteritis condition that affects ruminants, as a case study to exemplify the benefits of rapid diagnostics and effective treatment of chronic disease, with particular regard to the diagnostic and therapeutic potential of phage.

Modeling Paratuberculosis Transmission in a Small Dairy Herd Typical of Slovenia Suggests That Different Models Should Be Used to Study Disease Spread in Herds of Different Sizes

This study aimed to investigate the possible dynamics of paratuberculosis or Johne’s disease in a typical Slovenian dairy herd of about 17 cows. Paratuberculosis is a worldwide endemic disease of cattle caused by Mycobacterium avium subsp. paratuberculosis (MAP) and is associated with significant economic losses. We developed a stochastic compartmental model with two pathways of disease progression, infections of adult cows and infections of young animals through horizontal and vertical transmission, and transmission through animal movements. The average proportions of subclinically and clinically infected cows were 4% and 0.47%, respectively. The prevalence within the herd, which included latently infected animals, averaged 7.13% and ranged from 0% to 70.59%. Under the given circumstances, the results showed a relatively high rate of spontaneous elimination (0.22 per herd per year) of the disease and a high rate of reinfection (0.18 per herd per year) facilitated by active animal trade. To our knowledge, this stochastic compartmental model is the first to be developed specifically to represent a small dairy herd and could apply to other countries with a similar structure of dairy farms. The results suggest that different models should be used to study MAP spread in herds of various sizes.

An Embedded Multiscale Modelling to Guide Control and Elimination of Paratuberculosis in Ruminants

In recent years, multiscale modelling approach has begun to receive an overwhelming appreciation as an appropriate technique to characterize the complexity of infectious disease systems. In this study, we develop an embedded multiscale model of paratuberculosis in ruminants at host level that integrates the within-host scale and the between-host. A key feature of embedded multiscale models developed at host level of organization of an infectious disease system is that the within-host scale and the between-host scale influence each other in a reciprocal (i.e., both) way through superinfection, that is, through repeated infection before the host recovers from the initial infectious episode. This key feature is demonstrated in this study through a multiscale model of paratuberculosis in ruminants. The results of this study, through numerical analysis of the multiscale model, show that superinfection influences the dynamics of paratuberculosis only at the start of the infection, while the MAP bacteria replication continuously influences paratuberculosis dynamics throughout the infection until the host recovers from the initial infectious episode. This is largely because the replication of MAP bacteria at the within-host scale sustains the dynamics of paratuberculosis at this scale domain. We further use the embedded multiscale model developed in this study to evaluate the comparative effectiveness of paratuberculosis health interventions that influence the disease dynamics at different scales from efficacy data.

New and rapid strategies for the diagnosis of bovine paratuberculosis "in situ" using latex particles

The chemical coupling of a protoplasmatic antigen from Mycobacterium avium subsp. paratubeculosis onto core-shell carboxylated particles was investigated with the aim of producing latex-protein complexes to be used in immunoagglutination assays capable of detecting bovine paratuberculosis disease. For this purpose, sensitizations were carried out using both colored and not colored carboxylated latexes as well as the protoplasmatic antigen at pH close to its isoelectric point to favor the antigenic protein to approach the particle surface. In all cases, higher fractions of proteins were chemically-bound to carboxyl groups on the surface of the particles. The assessment of the performance of the visual immunoagglutination assays consisted of evaluating 111 sera from healthy and infected bovines with Mycobacterium avium subsp. paratuberculosis. Complexes obtained from the colored latex allowed an acceptable visual discrimination between the studied positive and negative sera. Most of the positive samples showed strong to very strong agglutination and only a few samples reacted weakly, i.e. a sensitivity of 70%. The specificity of the assay, on the other hand, was 86%. Therefore, this rapid detection technique allows an easy and inexpensive identification of animals possibly infected with paratuberculosis "in situ" in the herds.

How mechanistic modelling supports decision making for the control of enzootic infectious diseases

Controlling enzootic diseases, which generate a large cumulative burden and are often unregulated, is needed for sustainable farming, competitive agri-food chains, and veterinary public health. We discuss the benefits and challenges of mechanistic epidemiological modelling for livestock enzootics, with particular emphasis on the need for interdisciplinary approaches. We focus on issues arising when modelling pathogen spread at various scales (from farm to the region) to better assess disease control and propose targeted options. We discuss in particular the inclusion of farmers' strategic decision-making, the integration of within-host scale to refine intervention targeting, and the need to ground models on data.

The research and development process for multiscale models of infectious disease systems

Multiscale modelling of infectious disease systems falls within the domain of complexity science—the study of complex systems. However, what should be made clear is that current progress in multiscale modelling of infectious disease dynamics is still as yet insufficient to present it as a mature sub-discipline of complexity science. In this article we present a methodology for development of multiscale models of infectious disease systems. This methodology is a set of partially ordered research and development activities that result in multiscale models of infectious disease systems built from different scientific approaches. Therefore, the conclusive result of this article is a methodology to design multiscale models of infectious diseases. Although this research and development process for multiscale models cannot be claimed to be unique and final, it constitutes a good starting point, which may be found useful as a basis for further refinement in the discourse for multiscale modelling of infectious disease dynamics.

Complex systems such as infectious disease systems are inherently multilevel and multiscale systems. The study of such complex systems is called complexity science. In this article we present a methodology to design multiscale models of infectious disease systems from a complex systems perspective. Within this perspective we define complexity science as the study of the interconnected relationships of the levels and scales of organization of a complex system. We therefore, define the degree of complexity of a complex system as the number of levels and scales of organization of the complex system needed to describe it. In this work we first present a common multiscale vision of the multilevel and multiscale structure of infectious disease systems as complex systems in which the levels and scales of organization of an infectious disease system interact through different self-sustained multiscale cycles/loops (primary multiscale loops, or secondary multiscale loops, or tertiary multiscale loops) formed at different levels of organization of an infectious disease system due to ongoing reciprocal influence between the microscale and the macroscale. Guided by this multiscale vision, we propose a four-stage research and development process that result in multiscale models of infectious disease systems built from different scientific approaches.

Mycobacterium avium paratuberculosis infection of calves - The impact of dam infection status

Johne's disease, caused by Mycobacterium avium subsp. paratuberculosis (MAP), is a chronic condition of dairy cattle, and is endemic in the UK. Lack of understanding of the relative importance of different transmission routes reduces the impact of control scheme recommendations. The long incubation period for Johne's disease makes evaluation of control schemes difficult, and so this long-term cohort study offers a rare and valuable insight into the disease epidemiology. A longitudinal study was carried out following a cohort of 440 UK dairy cows in 6 herds recruited in 2012-2013. Individuals entering the milking herd were routinely monitored for the presence of MAP using quarterly milk ELISA testing. Using a Cox proportional-hazards regression model the relationship between time until first detection of infection and dam MAP status was investigated. We then compared the magnitude of the effect of dam status with that of other risk factors in order to understand its relative importance. Dam status was found to be the only observed factor that was significantly associated with time to an individual testing MAP-positive (p = 0.012). When compared to negative dams, we found a marginally significant effect of having a positive dam at time of calving, that increased the hazard of an individual testing positive by a factor of 2.6 (95% confidence interval: 0.89-7.79, p = 0.081). Further positive associations were found with dams becoming positive after the birth of the subject; a dam seroconverting within 12 months post parturition being associated with a 3.6 fold increase in hazard (95% confidence interval: 1.32-9.77, p = 0.013), and dams seroconverting more than a year after calving increased the hazard by a factor of 2.8 (95% confidence interval: 1.39-5.76, p = 0.004). These results suggest that cows may be transmitting MAP to their offspring at an earlier stage than had previously been thought, and so raise important questions about how this transmission may be occurring. The results of the study may have important practical implications for the management on-farm of the offspring of MAP-positive animals, with the potential to vastly reduce the time required to eliminate this chronic disease.

Backward bifurcation and oscillations in a nested immuno-eco-epidemiological model

This paper introduces a novel partial differential equation immuno-eco-epidemiological model of competition in which one species is affected by a disease while another can compete with it directly and by lowering the first species' immune response to the infection, a mode of competition termed stress-induced competition. When the disease is chronic, and the within-host dynamics are rapid, we reduce the partial differential equation model (PDE) to a three-dimensional ordinary differential equation (ODE) model. The ODE model exhibits backward bifurcation and sustained oscillations caused by the stress-induced competition. Furthermore, the ODE model, although not a special case of the PDE model, is useful for detecting backward bifurcation and oscillations in the PDE model. Backward bifurcation related to stress-induced competition allows the second species to persist for values of its invasion number below one. Furthermore, stress-induced competition leads to destabilization of the coexistence equilibrium and sustained oscillations in the PDE model. We suggest that complex systems such as this one may be studied by appropriately designed simple ODE models.

A nested compartmental model to assess the efficacy of paratuberculosis control measures on U.S. dairy farms

Paratuberculosis, also known as Johne's disease (JD), is a chronic contagious disease, caused by Mycobacterium avium subsp. paratuberculosis (MAP). The disease is incurable, fatal and causes economic losses estimated to exceed 200 million dollars to the U.S. dairy industry annually. Several preventive and control measures have been recommended; however, only a few of these measures have been validated empirically. Using a nested compartmental (NC) modeling approach, the main objective of this research was to identify the best combination of control and preventive measures that minimizes the prevalence and incidence of JD and the risk of MAP occurrence in a dairy herd. The NC model employs both MAP transmission estimates and data on pen movement of cattle on a dairy to quantify the effectiveness of control and preventive measures. To obtain reasonable ranges of parameter values for between-pen movements, the NC model was fitted to the movement data of four typical California dairy farms. Using the estimated ranges of the movement parameters and those of JD from previous research, the basic reproduction number was calculated to measure the risk of MAP occurrence in each pen environment as well as the entire dairy. Although the interventions evaluated by the NC model were shown to reduce the infection, no single measure alone was capable of eradicating the infection. The numerical simulations suggest that a combination of test and cull with more frequent manure removal is the most effective method in reducing incidence, prevalence and the risk of MAP occurrence. Other control measures such as limiting calf-adult cow contacts, raising calves in a disease-free herd or colostrum management were less effective.

A complete categorization of multiscale models of infectious disease systems

Modelling of infectious disease systems has entered a new era in which disease modellers are increasingly turning to multiscale modelling to extend traditional modelling frameworks into new application areas and to achieve higher levels of detail and accuracy in characterizing infectious disease systems. In this paper we present a categorization framework for categorizing multiscale models of infectious disease systems. The categorization framework consists of five integration frameworks and five criteria. We use the categorization framework to give a complete categorization of host-level immuno-epidemiological models (HL-IEMs). This categorization framework is also shown to be applicable in categorizing other types of multiscale models of infectious diseases beyond HL-IEMs through modifying the initial categorization framework presented in this study. Categorization of multiscale models of infectious disease systems in this way is useful in bringing some order to the discussion on the structure of these multiscale models.

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.

Mycobacterium avium Subsp. paratuberculosis Induces Specific IgE Production in Japanese People with Allergies

Background. The prevalence of allergies is steadily increasing worldwide; however, the pathogenesis is still unclear. We hypothesized that Mycobacterium avium subsp. paratuberculosis (MAP) may contribute to allergy development. This organism can be present in dairy foods, it can elicit an immunomodulatory switch from a Th1 to a Th2 response, and it has been speculated that it is linked to several human autoimmune diseases. To determine the contribution, sera from 99 individuals with various atopic disorders and 45 healthy nonallergic controls were assessed for total IgE levels and successively for MAP-specific IgE by ELISA. Results. The mean total serum IgE level in allergic patients was 256 ± 235 IU/mL, and in the healthy controls it was 62 ± 44 IU/mL (AUC = 0.88; p < 0.0001). Among the patient groups, 50 of the 99 subjects had increased IgE total level ≥ 150 IU/mL, while 49 subjects had IgE ≤ 150 IU/mL (mean level: 407 ± 256 IU/mL versus 106 ± 16 IU/mL; p < 0.0001). Additionally, 6 out of 50 subjects (12%) with IgE ≥ 150 IU/mL and none (0%) with IgE ≤ 150 IU/mL were positive for specific MAP IgE (AUC = 0.63; p = 0.03). Conclusion. The present study revealed that MAP has the ability to induce specific IgE and might contribute to the induction of allergic inflammation in genetically predisposed individuals.

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.