Clustering of equine grass sickness cases in the United Kingdom: a study considering the effect of position-dependent reporting on the space-time K-function

Equine grass sickness (EGS) is a largely fatal, pasture-associated dysautonomia. Although the aetiology of this disease is unknown, there is increasing evidence that Clostridium botulinum type C plays an important role in this condition. The disease is widespread in the United Kingdom, with the highest incidence believed to occur in Scotland. EGS also shows strong seasonal variation (most cases are reported between April and July). Data from histologically confirmed cases of EGS from England and Wales in 1999 and 2000 were collected from UK veterinary diagnostic centres. The data did not represent a complete census of cases, and the proportion of all cases reported to the centres would have varied in space and, independently, in time. We consider the variable reporting of this condition and the appropriateness of the space–time K-function when exploring the spatial-temporal properties of a ‘thinned’ point process. We conclude that such position-dependent under-reporting of EGS does not invalidate the Monte Carlo test for space–time interaction, and find strong evidence for space–time clustering of EGS cases (P<0.001). This may be attributed to contagious or other spatially and temporally localized processes such as local climate and/or pasture management practices

Semi-automatic selection of summary statistics for ABC model choice

A central statistical goal is to choose between alternative explanatory models of data. In many modern applications, such as population genetics, it is not possible to apply standard methods based on evaluating the likelihood functions of the models, as these are numerically intractable. Approximate Bayesian computation (ABC) is a commonly used alternative for such situations. ABC simulates data x for many parameter values under each model, which is compared to the observed data xobs. More weight is placed on models under which S(x) is close to S(xobs), where S maps data to a vector of summary statistics. Previous work has shown the choice of S is crucial to the efficiency and accuracy of ABC. This paper provides a method to select good summary statistics for model choice. It uses a preliminary step, simulating many x values from all models and fitting regressions to this with the model as response. The resulting model weight estimators are used as S in an ABC analysis. Theoretical results are given to justify this as approximating low dimensional sufficient statistics. A substantive application is presented: choosing between competing coalescent models of demographic growth for Campylobacter jejuni in New Zealand using multi-locus sequence typing data

A Bayesian spatio-temporal framework to identify outbreaks and examine environmental and social risk factors for infectious diseases monitored by routine surveillance

Spatio-temporal disease patterns can provide clues to etiological pathways, but can be complex to model. Using a flexible Bayesian hierarchical framework, we identify previously undetected space-time clusters and environmental and socio-demographic risk factors for reported giardiasis and cryptosporidiosis at the New Zealand small area level. For giardiasis, there was no seasonal pattern in outbreak probability and an inverse association with density of dairy cattle (β^₁= -0.09, Incidence Risk Ratio (IRR) 0.90 (95% CI 0.84, 0.97) per 1 log increase in cattle/km²). In dairy farming areas, cryptosporidiosis outbreaks were observed in spring. Reported cryptosporidiosis was positively associated with dairy cattle density: β^₁= 0.12, IRR 1.13 (95% CI 1.05, 1.21) per 1 log increase in cattle/km2 and inversely associated with weekly average temperature: β^₁=-0.07, IRR 0.92 (95% CI 0.87, 0.98) per 4°C increase. This framework can be generalized to determine the potential drivers of sporadic cases and latent outbreaks of infectious diseases of public health importance

Marked Campylobacteriosis Decline after Interventions Aimed at Poultry, New Zealand

A population-level food safety response successfully reduced disease incidence

Bayesian inference for multi-strain epidemics with application to Escherichia coli O157 : H7 in feedlot cattle

For most pathogens, testing procedures can be used to distinguish between different strains with which individuals are infected. Due to the growing availability of such data, multistrain models have increased in popularity over the past few years. Quantifying the interactions between different strains of a pathogen is crucial in order to obtain a more complete understanding of the transmission process, but statistical methods for this type of problem are still in the early stages of development. Motivated by this demand, we construct a stochastic epidemic model that incorporates additional strain information and propose a statistical algorithm for efficient inference. The model improves upon existing methods in the sense that it allows for both imperfect diagnostic test sensitivities and strain misclassification. Extensive simulation studies were conducted in order to assess the performance of our method, while the utility of the developed methodology is demonstrated on data obtained from a longitudinal study of Escherichia coli O157:H7 strains in feedlot cattle

Excretion of Vancomycin-Resistant Enterococci by Wild Mammals

A survey of fecal samples found enterococcal excretion in 82% of 388 bank voles (Clethrionomys glareolus), 92% of 131 woodmice (Apodemus sylvaticus), and 75% of 165 badgers (Meles meles). Vancomycin-resistant enterococci, all Enterococcus faecium of vanA genotype, were excreted by 4.6% of the woodmice and 1.2% of the badgers, but by none of the bank voles

Whole-Genome Comparison of Two Campylobacter jejuni Isolates of the Same Sequence Type Reveals Multiple Loci of Different Ancestral Lineage

Campylobacter jejuni ST-474 is the most important human enteric pathogen in New Zealand, and yet this genotype is rarely found elsewhere in the world. Insight into the evolution of this organism was gained by a whole genome comparison of two ST-474, flaA SVR-14 isolates and other available C. jejuni isolates and genomes. The two isolates were collected from different sources, human (H22082) and retail poultry (P110b), at the same time and from the same geographical location. Solexa sequencing of each isolate resulted in 1.659 Mb (H22082) and 1.656 Mb (P110b) of assembled sequences within 28 (H22082) and 29 (P110b) contigs. We analysed 1502 genes for which we had sequences within both ST-474 isolates and within at least one of 11 C. jejuni reference genomes. Although 94.5% of genes were identical between the two ST-474 isolates, we identified 83 genes that differed by at least one nucleotide, including 55 genes with non-synonymous substitutions. These covered 101 kb and contained 672 point differences. We inferred that 22 (3.3%) of these differences were due to mutation and 650 (96.7%) were imported via recombination. Our analysis estimated 38 recombinant breakpoints within these 83 genes, which correspond to recombination events affecting at least 19 loci regions and gives a tract length estimate of 2 kb. This includes a 12 kb region displaying non-homologous recombination in one of the ST-474 genomes, with the insertion of two genes, including ykgC, a putative oxidoreductase, and a conserved hypothetical protein of unknown function. Furthermore, our analysis indicates that the source of this recombined DNA is more likely to have come from C. jejuni strains that are more closely related to ST-474. This suggests that the rates of recombination and mutation are similar in order of magnitude, but that recombination has been much more important for generating divergence between the two ST-474 isolates

Meat safety in Tanzania’s value chain : experiences, explanations and expectations in butcheries and eateries

This research was funded by the Biotechnology and Biological Sciences Research Council, the Department for International Development, the Economic and Social Research Council, the Medical Research Council, the Natural Environment Research Council, and the Defence Science and Technology Laboratory, under the UK Zoonoses and Emerging Livestock Systems Initiative (Grant Numbers BB/L017679/1 and BB/L018926/1).Urbanisation is associated with changes in consumption patterns and food production processes. These patterns and processes can increase or decrease the risks of outbreaks of foodborne diseases and are generally accompanied by changes in food safety policies and regulations about food handling. This affects consumers, as well as people economically engaged in the food value chain. This study looks at Tanzania’s red meat value chain—which in its totality involves about one third of the population—and focuses on the knowledge, attitudes and reported practices of operators of butcheries and eateries with regards to meat safety in an urban and in a rural environment. We interviewed 64 operators about their experiences with foodborne diseases and their explanations and expectations around meat safety, with a particular emphasis on how they understood their own actions regarding food safety risks vis-à-vis regulations. We found operators of eateries emphasising their own agency in keeping meat safe, whereas operators of butcheries—whose products are more closely inspected—relied more on official inspections. Looking towards meat safety in the future, interviewees in rural areas were, relative to their urban counterparts, more optimistic, which we attribute to rural operators’ shorter and relatively unmediated value chains.Publisher PDFPeer reviewe

COVID-19 vaccine strategies for Aotearoa New Zealand:a mathematical modelling study

Summary: Background: COVID-19 elimination measures, including border closures have been applied in New Zealand. We have modelled the potential effect of vaccination programmes for opening borders.Methods: We used a deterministic age-stratified Susceptible, Exposed, Infectious, Recovered (SEIR) model. We minimised spread by varying the age-stratified vaccine allocation to find the minimum herd immunity requirements (the effective reproduction number Reff<1 with closed borders) under various vaccine effectiveness (VE) scenarios and R0 values. We ran two-year open-border simulations for two vaccine strategies: minimising Reff and targeting high-risk groups.Findings: Targeting of high-risk groups will result in lower hospitalisations and deaths in most scenarios. Reaching the herd immunity threshold (HIT) with a vaccine of 90% VE against disease and 80% VE against infection requires at least 86•5% total population uptake for R0=4•5 (with high vaccination coverage for 30–49-year-olds) and 98•1% uptake for R0=6. In a two-year open-border scenario with 10 overseas cases daily and 90% total population vaccine uptake (including 0–15 year olds) with the same vaccine, the strategy of targeting high-risk groups is close to achieving HIT, with an estimated 11,400 total hospitalisations (peak 324 active and 36 new daily cases in hospitals), and 1,030 total deaths.Interpretation: Targeting high-risk groups for vaccination will result in fewer hospitalisations and deaths with open borders compared to targeting reduced transmission. With a highly effective vaccine and a high total uptake, opening borders will result in increasing cases, hospitalisations, and deaths. Other public health and social measures will still be required as part of an effective pandemic response.Funding: This project was funded by the Health Research Council [20/1018].Research in contex