A multispecies model for the transmission and control of mastitis in dairy cows

Indirect Sensing of Subclinical Intramammary Infections in Dairy Herds with a Milking Robot

This study determined the impact of subclinical intramammary infections (IMIs), such as the major and minor udder pathogens (MaPs and MiPs), on the somatic cell count (SCC) in cow milk and investigated the possibilities of indirect sensing of the udder pathogens using the mastitis detection index (MDi) (DeLaval, Tumba, Sweden). The MDi incorporates quarter-level milk electrical conductivity, blood in milk, and milking interval. The case group (n = 21; MDi ≥ 1.4) was compared with the control group (n = 24; MDi < 1.4) for the presence of IMIs. The microbiological investigation of udder quarter foremilk samples was performed two times with an interval of 10 to 14 days. The case and control groups differed in terms of the occurrence of MaPs and MiPs in milk. During the continuous subclinical IMI and the episodic MaP infection, a higher SCC was detected compared with the episodic MiP infection or quarters without IMI. The novel finding of this study was that by using the milk quality sensor for the sensing of subclinical IMIs, there was an indication for the successful detection of episodic MaPs. However, the sensing of the continuous subclinical IMIs was not possible in the current study and still needs to be investigated.

Analysis of Prototheca and yeast species isolated from bulk tank milk collected in Tokachi District, Japan

Bovine mastitis, a major infectious disease affecting milking cows, leads to reduced milk yield and quality, reduced animal welfare, and an increased need for culling. Although its major causative agents are bacteria, yeast species and achlorophyllous algae of the Prototheca genus are well known as causative agents of bovine refractory mastitis. Nevertheless, few studies have analyzed specific yeasts and Prototheca in this context. Herein, we present survey data of yeast species and Prototheca species isolated from bulk tank milk in the Tokachi district of Japan from April 2020 through March 2021. The species of 276 isolates were determined. Yeast species accounted for 184 isolates, of which Pichia kudriavzevii was the most prevalent species. Regarding Prototheca species, only Prototheca bovis was isolated (92 isolates). Prototheca bovis and Pichia kudriavzevii were detected throughout the year and were detected repeatedly on the same farm. Kluyveromyces marxianus was the second most frequently isolated yeast species after Pichia kudriavzevii. Candida parapsilosis, the fourth most frequently isolated yeast species, was found discontinuously. Analysis of monthly data indicated that Kluyveromyces marxianus and Candida parapsilosis were mainly found during the winter and summer months, respectively. Candida akabanensis and Pichia cactophila were the third and fifth most frequently isolated yeast species, respectively. They were detected repeatedly in bulk tank milk samples from the same farms. Results obtained from bulk tank milk underscore the prevalence of these species. These study results are expected to contribute to the elucidation of problematic yeast and Prototheca species.

Assessment of genetic variation for pathogen-specific mastitis resistance in Valle del Belice dairy sheep

Background

Mastitis resistance is a complex and multifactorial trait, and its expression depends on both genetic and environmental factors, including infection pressure. The objective of this research was to determine the genetic basis of mastitis resistance to specific pathogens using a repeatability threshold probit animal model.

Results

The most prevalent isolated pathogens were coagulase-negative staphylococci (CNS); 39 % of records and 77 % of the animals infected at least one time in the whole period of study. There was significant genetic variation only for Streptococci (STR). In addition, there was a positive genetic correlation between STR and all pathogens together (ALL) (0.36 ± 0.22), and CNS and ALL (0.92 ± 0.04).

Conclusion

The results of our study support the presence of significant genetic variation for mastitis caused by Streptococci and suggest the importance of discriminating between different pathogens causing mastitis due to the fact that they most likely influence different genetic traits. Low heritabilities for pathogen specific-mastitis resistance may be considered when including bacteriological status as a measure of mastitis presence to implement breeding strategies for improving udder health in dairy ewes.

QUALITATIVE STUDY FOR A MULTI-DRUG RESISTANT TB MODEL WITH EXOGENOUS REINFECTION AND RELAPSE

One tuberculosis transmission model is formulated by incorporating exogenous reinfection, relapse, and two treatment stages of infectious TB cases. The global stability of the unique disease-free equilibrium is obtained by applying the comparison principle if the effective reproduction number for the full model is less than unity. The existence and stability of the boundary equilibria are given by introducing the invasion reproduction numbers. Furthermore, the existence and local stability of the endemic equilibrium are addressed under some conditions.

Understanding the transmission dynamics of respiratory syncytial virus using multiple time series and nested models

The nature and role of re-infection and partial immunity are likely to be important determinants of the transmission dynamics of human respiratory syncytial virus (hRSV). We propose a single model structure that captures four possible host responses to infection and subsequent reinfection: partial susceptibility, altered infection duration, reduced infectiousness and temporary immunity (which might be partial). The magnitude of these responses is determined by four homotopy parameters, and by setting some of these parameters to extreme values we generate a set of eight nested, deterministic transmission models. In order to investigate hRSV transmission dynamics, we applied these models to incidence data from eight international locations. Seasonality is included as cyclic variation in transmission. Parameters associated with the natural history of the infection were assumed to be independent of geographic location, while others, such as those associated with seasonality, were assumed location specific. Models incorporating either of the two extreme assumptions for immunity (none or solid and lifelong) were unable to reproduce the observed dynamics. Model fits with either waning or partial immunity to disease or both were visually comparable. The best fitting structure was a lifelong partial immunity to both disease and infection. Observed patterns were reproduced by stochastic simulations using the parameter values estimated from the deterministic models.

The transmission and control of mastitis in dairy cows: A theoretical approach

A multi-species model that incorporates the transmission of both major and minor mastitis pathogens as well as the interaction between them via coinfection of a quarter is fitted to data from seven dairy herds. The results suggest that major and minor pathogens can interact, on occasion, in a counter-intuitive way with implications for the control of clinical mastitis. The key finding is that delaying culling of cows with major pathogen infections for more than 100 days post infection could result in a higher prevalence of major pathogen infections, whereas early culling would reduce the levels. A theoretical exploration of current and proposed control strategies is carried out, informed by parameters estimated from the model and data. The results at each stage suggest of areas of further research such as: field-testing of the hypotheses presented; the exploration of a stochastic formulation of the model; analysis of the raw repeated measures data; application of control theory to determine the most effective combination of control strategies; inclusion of economic factors into the modelling framework.

Prevalence and associations between bacterial isolates from dry mammary glands of dairy cows

To assess the prevalence and patterns of bacterial isolates, cultures were made from the dry mammary glands of dairy cows in six commercial dairy herds in the UK. Milk samples were taken from all four quarters of 480 cows at drying off and at weekly intervals from 14 days before to seven days after calving. A major mastitis pathogen was isolated from at least one quarter of 220 (45.8 per cent) of the cows and from more than one quarter of 90 (18.8 per cent) of them. During the late dry to calving period, of the 957 quarters with three culture results, a major mastitis pathogen was cultured from 236 (24.7 per cent) quarters of 186 (38.8 [corrected] per cent) cows. The most commonly isolated major pathogen was Escherichia coli, followed by Streptococcus uberis and coagulase-positive staphylococci. There were significant differences between the patterns of isolates from different farms and in different calving months, suggesting that the rate of infection was partially dependent on external conditions. The isolation of E. coli, S. uberis or coagulase-positive staphylococci from a cow during the late dry/periparturient period was associated with an increased risk of that cow being culled in the next lactation. Bayesian general linear mixed models were used to assess the associations between the different bacterial species. The probability of isolating either E. coli or S. uberis was significantly greater when the other organism was cultured in a milk sample; this was also true of coagulase-positive staphylococci and S. uberis. When Corynebacterium species were isolated from a milk sample, the probability of isolating coagulase-positive staphylococci or S. uberis decreased significantly, and when coagulase-negative staphylococci were isolated the probability of isolating coagulase-positive staphylococci was reduced.

The use of Markov chain Monte Carlo for analysis of correlated binary data: patterns of somatic cells in milk and the risk of clinical mastitis in dairy cows

Two analytical approaches were used to investigate the relationship between somatic cell concentrations in monthly quarter milk samples and subsequent, naturally occurring clinical mastitis in three dairy herds. Firstly, cows with clinical mastitis were selected and a conventional matched analysis was used to compare affected and unaffected quarters of the same cow. The second analysis included all cows, and in order to overcome potential bias associated with the correlation structure, a hierarchical Bayesian generalised linear mixed model was specified. A Markov chain Monte Carlo (MCMC) approach, that is Gibbs sampling, was used to estimate parameters. The results of both the matched analysis and the hierarchical modelling suggested that quarters with a somatic cell count (SCC) in the range 41,000-100,000 cells/ml had a lower risk of clinical mastitis during the next month than quarters <41,000 cell/ml. Quarters with an SCC >200,000 cells/ml were at the greatest risk of clinical mastitis in the next month. There was a reduced risk of clinical mastitis between 1 and 2 months later in quarters with an SCC of 81,000-150,000 cells/ml compared with quarters below this level. The hierarchical modelling analysis identified a further reduced risk of clinical mastitis between 2 and 3 months later in quarters with an SCC 61,000-150,000 cells/ml, compared to other quarters. We conclude that low concentrations of somatic cells in milk are associated with increased risk of clinical mastitis, and that high concentrations are indicative of pre-existing immunological mobilisation against infection. The variation in risk between quarters of affected cows suggests that local quarter immunological events, rather than solely whole cow factors, have an important influence on the risk of clinical mastitis. MCMC proved a useful tool for estimating parameters in a hierarchical Bernoulli model. Model construction and an approach to assessing goodness of model fit are described.

Structural identifiability analysis of some highly structured families of statespace models using differential algebra

In this paper we identify biologically relevant families of models whose structural identifiability analysis could not be performed with available techniques directly. The models considered come from both the immunological and epidemiological literature.

The structural identifiability and parameter estimation of a multispecies model for the transmission of mastitis in dairy cows with postmilking teat disinfection

A mathematical model for the transmission of two interacting classes of mastitis causing bacterial pathogens in a herd of dairy cows is presented and applied to a specific data set. The data were derived from a field trial of a specific measure used in the control of these pathogens, where half the individuals were subjected to the control and in the others the treatment was discontinued. The resultant mathematical model (eight non-linear simultaneous ordinary differential equations) therefore incorporates heterogeneity in the host as well as the infectious agent and consequently the effects of control are intrinsic in the model structure. A structural identifiability analysis of the model is presented demonstrating that the scope of the novel method used allows application to high order non-linear systems. The results of a simultaneous estimation of six unknown system parameters are presented. Previous work has only estimated a subset of these either simultaneously or individually. Therefore not only are new estimates provided for the parameters relating to the transmission and control of the classes of pathogens under study, but also information about the relationships between them. We exploit the close link between mathematical modelling, structural identifiability analysis, and parameter estimation to obtain biological insights into the system modelled.

The structural identifiability and parameter estimation of a multispecies model for the transmission of mastitis in dairy cows

A structural identifiability analysis is performed on a mathematical model for the coupled transmission of two classes of pathogen. The pathogens, classified as major and minor, are aetiological agents of mastitis in dairy cows that interact directly and via the immunological reaction in their hosts. Parameter estimates are available from experimental data for all but four of the parameters in the model. Data from a longitudinal study of infection are used to estimate these unknown parameters. A novel approach and application of structural identifiability analysis is combined in this paper with the estimation of cross-protection parameters using epidemiological data.