Timely and accurate identification of cows with intramammary infections is essential for optimal udder health management. Various sensor systems have been developed to provide udder health information that can be used as a decision support tool for the farmer. Among these sensors, the DeLaval Online Cell Counter (DeLaval, Tumba, Sweden) provides somatic cell counts from every milking at cow level. Our aim was to describe and evaluate diagnostic sensor properties of these online cell counts (OCC) for detecting an intramammary infection, defined as an episode of subclinical mastitis or a new case of clinical mastitis. The predictive abilities of a single OCC value, rolling averages of OCC values, and an elevated mastitis risk (EMR) variable were compared for their accuracy in identifying cows with episodes of subclinical mastitis or new cases of clinical mastitis. Detection of subclinical mastitis episodes by OCC was performed in 2 separate groups of different mastitis pathogens, Pat 1 and Pat 2, categorized by their known ability to increase somatic cell count. The data for this study were obtained in a field trial conducted in the dairy herd of the Norwegian University of Life Sciences. Altogether, 173 cows were sampled at least once during a 17-mo study period. The total number of quarter milk cultures was 5,330. The most common Pat 1 pathogens were Staphylococcus epidermidis, Staphylococcus aureus, and Streptococcus dysgalactiae. The most common Pat 2 pathogens were Corynebacterium bovis, Staphylococcus chromogenes, and Staphylococcus haemolyticus. The OCC were successfully recorded from 82,182 of 96,542 milkings during the study period. For episodes of subclinical mastitis the rolling 7-d average OCC and the EMR approach performed better than a single OCC value for detection of Pat 1 subclinical mastitis episodes. The EMR approach outperformed the OCC approaches for detection of Pat 2 subclinical mastitis episodes. For the 2 pathogen groups, the sensitivity of detection of subclinical mastitis episodes was 69% (Pat 1) and 31% (Pat 2), respectively, at a predefined specificity of 80% (EMR). All 3 approaches were equally good at detecting new cases of clinical mastitis, with an optimum sensitivity of 80% and specificity of 90% (single OCC value).

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

Management of udder health is particularly focused on preventing new infections. Data from the DeLaval Online Cell Counter (DeLaval, Tumba, Sweden) may be used in forecasting to improve decision support for improved udder health management. It provides online cell counts (OCC) as a proxy for somatic cell counts from every milking at the cow level. However, these values are typically too insensitive and nonspecific to indicate subclinical intramammary infection (IMI). Our aim was to describe and evaluate use of dynamic transmission models to forecast subclinical IMI episodes using milk cultures or changes in OCC patterns over time. The latter was expressed by an elevated mastitis risk variable. Data were obtained from the dairy herd of the Norwegian University of Life Sciences (Oslo, Norway). In total, 173 cows were sampled monthly for bacteriological milk culture during a 17-mo study period and 5,330 quarter milk samples were cultured. Mastitis pathogens identified were assigned to 1 of 2 groups, Pat 1 or Pat 2. Pathogens from which a high cell count would be expected during a subclinical IMI episode were assigned to the Pat 1 group. Pathogens not in the Pat 1 group were assigned to the Pat 2 group. Staphylococcus epidermidis, Staphylococcus aureus, and Streptococcus dysgalactiae were the most common Pat 1 pathogens. Corynebacterium bovis, Staphylococcus chromogenes, and Staphylococcus haemolyticus were the most common Pat 2 pathogens. The OCC were successfully recorded from 82,182 of 96,542 milkings. The current study included 324 subclinical IMI episodes. None of the mastitis pathogens demonstrated a basic reproduction number (R 0 ) >1. Patterns of OCC change related to an episode of Pat 1 subclinical IMI at specificity levels of 80, 90, and 95% at sensitivity levels of 69, 59, and 48% respectively, demonstrated an R 0 >1. An existing infection was significant for transmission for several Pat 2 pathogens, but only for Staphylococcus aureus and Staphylococcus epidermidis among Pat 1 pathogens. Dynamic transmission models showed that patterns of OCC change related to an episode of Pat 1 subclinical IMI were significantly related to the same pattern occurring in susceptible cows at specificity levels of 80, 90, and 99% at sensitivity levels of 69, 48, and 8%, respectively. We conclude that changes in herd prevalence of subclinical IMI can be predicted using dynamic transmission models based on patterns of OCC change. Choice of specificity level depends on management goals and tolerance for false-positive alerts.