Can policy be risk-based? The cultural theory of risk and the case of livestock disease containment

This article explores the nature of calls for risk-based policy present in expert discourse from a cultural theory perspective. Semi-structured interviews with professionals engaged in the research and management of livestock disease control provide the data for a reading proposing that the real basis of policy relating to socio-technical hazards is deeply political and cannot be purified through ‘escape routes’ to objectivity. Scientists and risk managers are shown calling, on the one hand, for risk-based policy approaches while on the other acknowledging a range of policy drivers outside the scope of conventional quantitative risk analysis including group interests, eventualities such as outbreaks, historical antecedents, emergent scientific advances and other contingencies. Calls for risk-based policy are presented, following cultural theory, as ideals connected to a reductionist epistemology and serving particular professional interests over others rather than as realistic proposals for a paradigm shift

The need for One Health systems-thinking approaches to understand multiscale dissemination of antimicrobial resistance

Although the effects of antimicrobial resistance (AMR) are most obvious at clinical treatment failure, AMR evolution, transmission, and dispersal happen largely in environmental settings, for example within farms, waterways, livestock, and wildlife. We argue that systems-thinking, One Health approaches are crucial for tackling AMR, by understanding and predicting how anthropogenic activities interact within environmental subsystems, to drive AMR emergence and transmission. Innovative computational methods integrating big data streams (eg, from clinical, agricultural, and environmental monitoring) will accelerate our understanding of AMR, supporting decision making. There are challenges to accessing, integrating, synthesising, and interpreting such complex, multidimensional, heterogeneous datasets, including the lack of specific metrics to quantify anthropogenic AMR. Moreover, data confidentiality, geopolitical and cultural variation, surveillance gaps, and science funding cause biases, uncertainty, and gaps in AMR data and metadata. Combining systems-thinking with modelling will allow exploration, scaling-up, and extrapolation of existing data. This combination will provide vital understanding of the dynamic movement and transmission of AMR within and among environmental subsystems, and its effects across the greater system. Consequently, strategies for slowing down AMR dissemination can be modelled and compared for efficacy and cost-effectiveness

The need for One Health systems-thinking approaches to understand multiscale dissemination of antimicrobial resistance

Although the effects of antimicrobial resistance (AMR) are most obvious at clinical treatment failure, AMR evolution, transmission, and dispersal happen largely in environmental settings, for example within farms, waterways, livestock, and wildlife. We argue that systems-thinking, One Health approaches are crucial for tackling AMR, by understanding and predicting how anthropogenic activities interact within environmental subsystems, to drive AMR emergence and transmission. Innovative computational methods integrating big data streams (eg, from clinical, agricultural, and environmental monitoring) will accelerate our understanding of AMR, supporting decision making. There are challenges to accessing, integrating, synthesising, and interpreting such complex, multidimensional, heterogeneous datasets, including the lack of specific metrics to quantify anthropogenic AMR. Moreover, data confidentiality, geopolitical and cultural variation, surveillance gaps, and science funding cause biases, uncertainty, and gaps in AMR data and metadata. Combining systems-thinking with modelling will allow exploration, scaling-up, and extrapolation of existing data. This combination will provide vital understanding of the dynamic movement and transmission of AMR within and among environmental subsystems, and its effects across the greater system. Consequently, strategies for slowing down AMR dissemination can be modelled and compared for efficacy and cost-effectiveness

Global selective sweep of a highly inbred genome of the cattle parasite Neospora caninum

Neospora caninum, a cyst-forming apicomplexan parasite, is a leading cause of neuromuscular diseases in dogs as well as fetal abortion in cattle worldwide. The importance of the domestic and sylvatic life cycles of Neospora, and the role of vertical transmission in the expansion and transmission of infection in cattle, is not sufficiently understood. To elucidate the population genomics of Neospora, we genotyped 50 isolates collected worldwide from a wide range of hosts using 19 linked and unlinked genetic markers. Phylogenetic analysis and genetic distance indices resolved a single genotype of N. caninum. Whole-genome sequencing of 7 isolates from 2 different continents identified high linkage disequilibrium, significant structural variation, but only limited polymorphism genome-wide, with only 5,766 biallelic single nucleotide polymorphisms (SNPs) total. Greater than half of these SNPs (∼3,000) clustered into 6 distinct haploblocks and each block possessed limited allelic diversity (with only 4 to 6 haplotypes resolved at each cluster). Importantly, the alleles at each haploblock had independently segregated across the strains sequenced, supporting a unisexual expansion model that is mosaic at 6 genomic blocks. Integrating seroprevalence data from African cattle, our data support a global selective sweep of a highly inbred livestock pathogen that originated within European dairy stock and expanded transcontinentally via unisexual mating and vertical transmission very recently, likely the result of human activities, including recurrent migration, domestication, and breed development of bovid and canid hosts within similar proximities

Comparative Genomics of the Apicomplexan Parasites Toxoplasma gondii and Neospora caninum: Coccidia Differing in Host Range and Transmission Strategy

Toxoplasma gondii is a zoonotic protozoan parasite which infects nearly one third of the human population and is found in an extraordinary range of vertebrate hosts. Its epidemiology depends heavily on horizontal transmission, especially between rodents and its definitive host, the cat. Neospora caninum is a recently discovered close relative of Toxoplasma, whose definitive host is the dog. Both species are tissue-dwelling Coccidia and members of the phylum Apicomplexa; they share many common features, but Neospora neither infects humans nor shares the same wide host range as Toxoplasma, rather it shows a striking preference for highly efficient vertical transmission in cattle. These species therefore provide a remarkable opportunity to investigate mechanisms of host restriction, transmission strategies, virulence and zoonotic potential. We sequenced the genome of N. caninum and transcriptomes of the invasive stage of both species, undertaking an extensive comparative genomics and transcriptomics analysis. We estimate that these organisms diverged from their common ancestor around 28 million years ago and find that both genomes and gene expression are remarkably conserved. However, in N. caninum we identified an unexpected expansion of surface antigen gene families and the divergence of secreted virulence factors, including rhoptry kinases. Specifically we show that the rhoptry kinase ROP18 is pseudogenised in N. caninum and that, as a possible consequence, Neospora is unable to phosphorylate host immunity-related GTPases, as Toxoplasma does. This defense strategy is thought to be key to virulence in Toxoplasma. We conclude that the ecological niches occupied by these species are influenced by a relatively small number of gene products which operate at the host-parasite interface and that the dominance of vertical transmission in N. caninum may be associated with the evolution of reduced virulence in this species

Correction: "Wrong, but useful":Negotiating uncertainty in infectious disease modelling (PLoS ONE)

Links to Figure 1 were incorrectly inserted in the terms "Fig. 1A" and "Fig. 1B" in the extract labeled "E9 (scientific paper)" in the Production of Models section of the Results and Discussion. These terms refer to a figure mentioned in the extract and not Figure 1 of the article. In addition, Figure 1 is placed incorrectly within the article. Rather than immediately below the aforementioned extract, Figure 1 should appear immediately below the first paragraph of the sub-section "Comparison against independent data" in the "Testing Models" section of the Results and Discussion