Investigation of the validity of two Bayesian ancestral state reconstruction models for estimating Salmonella transmission during outbreaks

Disentangling the role of poultry farms and wild birds in the spread of highly pathogenic avian influenza virus in Europe

In winter 2016-7, Europe was severely hit by an unprecedented epidemic of highly pathogenic avian influenza viruses (HPAIVs), causing a significant impact on animal health, wildlife conservation, and livestock economic sustainability. By applying phylodynamic tools to virus sequences collected during the epidemic, we investigated when the first infections occurred, how many infections were unreported, which factors influenced virus spread, and how many spillover events occurred. HPAIV was likely introduced into poultry farms during the autumn, in line with the timing of wild birds' migration. In Germany, Hungary, and Poland, the epidemic was dominated by farm-to-farm transmission, showing that understanding of how farms are connected would greatly help control efforts. In the Czech Republic, the epidemic was dominated by wild bird-to-farm transmission, implying that more sustainable prevention strategies should be developed to reduce HPAIV exposure from wild birds. Inferred transmission parameters will be useful to parameterize predictive models of HPAIV spread. None of the predictors related to live poultry trade, poultry census, and geographic proximity were identified as supportive predictors of HPAIV spread between farms across borders. These results are crucial to better understand HPAIV transmission dynamics at the domestic-wildlife interface with the view to reduce the impact of future epidemics.

Quantifying the transmission of antimicrobial resistance at the human and livestock interface with genomics

BACKGROUND

Livestock have been implicated as a reservoir for antimicrobial resistance (AMR) that can spread to humans. Close proximity and ecological interfaces involving livestock have been posited as risk factors for the transmission of AMR. In spite of this, there are sparse data and limited agreement on the transmission dynamics that occur.

OBJECTIVES

To identify how genome sequencing approaches can be used to quantify the dynamics of AMR transmission at the human-livestock interface, and where current knowledge can be improved to better understand the impact of transmission on the spread of AMR.

SOURCES

Key articles investigating various aspects of AMR transmission at the human-livestock interface are discussed, with a focus on Escherichia coli.

CONTENT

We recapitulate the current understanding of the transmission of AMR between humans and livestock based on current genomic and epidemiological approaches. We discuss how the use of well-designed, high-resolution genome sequencing studies can improve our understanding of the human-livestock interface.

IMPLICATIONS

A better understanding of the human-livestock interface will aid in the development of evidence-based and effective One Health interventions that can ultimately reduce the burden of AMR in humans.

Assessing biases in phylodynamic inferences in the presence of super-spreaders

Phylodynamic analyses using pathogen genetic data have become popular for making epidemiological inferences. However, many methods assume that the underlying host population follows homogenous mixing patterns. Nevertheless, in real disease outbreaks, a small number of individuals infect a disproportionately large number of others (super-spreaders). Our objective was to quantify the degree of bias in estimating the epidemic starting date in the presence of super-spreaders using different sample selection strategies. We simulated 100 epidemics of a hypothetical pathogen (fast evolving foot and mouth disease virus-like) over a real livestock movement network allowing the genetic mutations in pathogen sequence. Genetic sequences were sampled serially over the epidemic, which were then used to estimate the epidemic starting date using Extended Bayesian Coalescent Skyline plot (EBSP) and Birth–death skyline plot (BDSKY) models. Our results showed that the degree of bias varies over different epidemic situations, with substantial overestimations on the epidemic duration occurring in some occasions. While the accuracy and precision of BDSKY were deteriorated when a super-spreader generated a larger proportion of secondary cases, those of EBSP were deteriorated when epidemics were shorter. The accuracies of the inference were similar irrespective of whether the analysis used all sampled sequences or only a subset of them, although the former required substantially longer computational times. When phylodynamic analyses need to be performed under a time constraint to inform policy makers, we suggest multiple phylodynamics models to be used simultaneously for a subset of data to ascertain the robustness of inferences.