Bacteriology and histopathology are the most commonly used tests used for official confirmatory diagnosis of bovine tuberculosis (bTB) in cattle in most countries. PCR is also being used increasingly because it allows a fast diagnosis. This test could be applied as a supplement to or replacement for current bTB confirmatory diagnostic tests but its characteristics have first to be evaluated. The aim of this study was to estimate and compare sensitivities and specificities of bacteriology, histopathology and PCR under French field conditions, in the absence of a gold standard using latent class analysis. The studied population consisted of 5,211 animals from which samples were subjected to bacteriology and PCR (LSI VetMAX™ Mycobacterium tuberculosis Complex PCR Kit, Life Technologies) as their herd of origin was either suspected or confirmed infected with bTB or because bTB-like lesions were detected during slaughterhouse inspection. Samples from 697 of these animals (all with bTB-like lesions) were subjected to histopathology. Bayesian models were developed, allowing for dependence between bacteriology and PCR, while assuming independence from histopathology. The sensitivity of PCR was higher than that of bacteriology (on average 87.7% [82.5–92.3%] versus 78.1% [72.9–82.8%]) while specificity of both tests was very good (on average 97.0% for PCR [94.3–99.0%] and 99.1% for bacteriology [97.1–100.0%]). Histopathology was at least as sensitive as PCR (on average 93.6% [89.9–96.9%]) but less specific than the two other tests (on average 83.3% [78.7–87.6%]). These results suggest that PCR has the potential to replace bacteriology to confirm bTB in samples submitted from suspect cattle.

IntroductionFrance is one of Europe’s foremost poultry producers and the world’s fifth largest producer of poultry meat. In November 2016, highly pathogenic avian influenza (HPAI) virus subtype H5N8 emerged in poultry in the country. As of 23 March 2017, a total of 484 confirmed outbreaks were reported, with consequences on animal health and socio-economic impacts for producers. Methods: We examined the spatio-temporal distribution of outbreaks that occurred in France between November 2016 and March 2017, using the space–time K-function and space–time permutation model of the scan statistic test. Results: Most outbreaks affected duck flocks in south-west France. A significant space–time interaction of outbreaks was present at the beginning of the epidemic within a window of 8 km and 13 days. This interaction disappeared towards the epidemic end. Five spatio-temporal outbreak clusters were identified in the main poultry producing areas, moving sequentially from east to west. The average spread rate of the epidemic front wave was estimated to be 5.5 km/week. It increased from February 2017 and was negatively associated with the duck holding density. Conclusion: HPAI-H5N8 infections varied over time and space in France. Intense transmission events occurred at the early stages of the epidemic, followed by long-range jumps in the disease spread towards its end. Findings support strict control strategies in poultry production as well as the maintenance of high biosecurity standards for poultry holdings. Factors and mechanisms driving HPAI spread need to be further investigated.

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.

Q fever is a worldwide zoonosis caused by Coxiella burnetii. Although ruminants are recognized as the most important source of human infection, no previous studies have focused on assessing the characteristics of the bacterial spread within a cattle herd and no epidemic model has been proposed in this context. We assess the key epidemiological parameters from field data in a Bayesian framework that takes into account the available knowledge, missing data and the uncertainty of the observation process owing to the imperfection of diagnostic tests. We propose an original individual-based Markovian model in discrete time describing the evolution of the infection for each animal. Markov chain Monte Carlo methodology is used to estimate parameters of interest from data consisting of individual health states of 217 cows of five chronically infected dairy herds sampled every week for a four-week period. Outputs are the posterior distributions of the probabilities of transition between health states and of the environmental bacterial load. Our findings show that some herds are characterized by a very low infection risk while others have a mild infection risk and a non-negligible intermittent shedding probability. Moreover, the antibody status seems to be a key point in the bacterial spread (shedders with antibodies shed for a longer period of time than shedders without antibodies). In addition to the biological insights, these estimates also provide information for calibrating simulation models to assess control strategies for C. burnetii infection.