Using geographically weighted regression to explore the spatially heterogeneous spread of bovine tuberculosis in England and Wales

An understanding of the factors that affect the spread of endemic bovine tuberculosis (bTB) is critical for the development of measures to stop and reverse this spread. Analyses of spatial data need to account for the inherent spatial heterogeneity within the data, or else spatial autocorrelation can lead to an overestimate of the significance of variables. This study used three methods of analysis—least-squares linear regression with a spatial autocorrelation term, geographically weighted regression (GWR) and boosted regression tree (BRT) analysis—to identify the factors that influence the spread of endemic bTB at a local level in England and Wales. The linear regression and GWR methods demonstrated the importance of accounting for spatial differences in risk factors for bTB, and showed some consistency in the identification of certain factors related to flooding, disease history and the presence of multiple genotypes of bTB. This is the first attempt to explore the factors associated with the spread of endemic bTB in England and Wales using GWR. This technique improves on least-squares linear regression approaches by identifying regional differences in the factors associated with bTB spread. However, interpretation of these complex regional differences is difficult and the approach does not lend itself to predictive models which are likely to be of more value to policy makers. Methods such as BRT may be more suited to such a task. Here we have demonstrated that GWR and BRT can produce comparable outputs

Molecular characterization of extended spectrum cephalosporin resistant Escherichia coli isolated from livestock and in-contact humans in Southeast Nigeria

The rise in antimicrobial resistance (AMR) in bacteria is reducing therapeutic options for livestock and human health, with a paucity of information globally. To fill this gap, a One-Health approach was taken by sampling livestock on farms (n = 52), abattoir (n = 8), and animal markets (n = 10), and in-contact humans in Southeast Nigeria. Extended spectrum cephalosporin (ESC)-resistant (ESC-R) Escherichia coli was selectively cultured from 975 healthy livestock faecal swabs, and hand swabs from in-contact humans. Antimicrobial susceptibility testing (AST) was performed on all ESC-R E. coli. For isolates showing a multi-drug resistance (MDR) phenotype (n = 196), quantitative real-time PCR (qPCR) was performed for confirmation of extended-spectrum β-lactamase (ESBL) and carbapenemase genes. Whole-genome sequencing (WGS) was performed on a subset (n = 157) for detailed molecular characterisation. The results showed ESC-R E. coli was present in 41.2% of samples, with AST results indicating 48.8% of isolates were phenotypically MDR. qPCR confirmed presence of ESBL genes, with bla(CTX-M) present in all but others in a subset [bla(TEM) (62.8%) and bla(SHV) (0.5%)] of isolates; none harboured transferable carbapenemase genes. Multi-locus sequence typing identified 34 Sequence Types (ST) distributed among different sampling levels; ST196 carrying bla(CTX-M-55) was predominant in chickens. Large numbers of single nucleotide polymorphisms (SNPs) in the core genome of isolates, even within the same clade by phylogenetic analysis, indicated high genetic diversity. AMR genotyping indicated the predominant bla(CTX-M) variant was bla(CTX-M-15) (87.9%), although bla(CTX-M-55), bla(CTX-M-64,) and bla(CTX-M-65) were present; it was notable that bla(CTX-M-1), common in livestock, was absent. Other predominant AMR genes included: sul2, qnrS1, strB, bla(TEM-1b), tetA-v2, and dfrA14, with prevalence varying according to host livestock species. A bla(CTX-M-15) harbouring plasmid from livestock isolates in Ebonyi showed high sequence identity to one from river/sewage water in India, indicating this ESBL plasmid to be globally disseminated, being present beyond the river environment. In conclusion, ESC-R E. coli was widespread in livestock and in-contact humans from Southeast Nigeria. WGS data indicated the isolates were genetically highly diverse, probably representing true diversity of wild type E. coli; they were likely to be MDR with several harbouring bla(CTX-M-15.) Surprisingly, human isolates had highest numbers of AMR genes and pigs the least

An assessment of risk compensation and spillover behavioural adaptions associated with the use of vaccines in animal disease management

This paper analyses farmers’ behavioural responses to Government attempts to reduce the risk of disease transmission from badgers to cattle through badger vaccination. Evidence for two opposing behavioural adaptions is examined in response to the vaccination of badgers to reduce the risk of transmission to farmed cattle. Risk compensation theory suggests that interventions that reduce risk, such as vaccination, are counterbalanced by negative behavioural adaptions. By contrast, the spillover effect suggests that interventions can prompt further positive behaviours. The paper uses data from a longitudinal mixed methods study of farmers’ attitudes to badger vaccination to prevent the spread of bovine tuberculosis, their reports of biosecurity practices, and cattle movement data in 5 areas of England, one of which experienced badger vaccination. Analysis finds limited evidence of spillover behaviours following vaccination. Lack of spillover is attributed to farmers’ beliefs in the effectiveness of biosecurity and the lack of similarity between badger vaccination and vaccination for other animal diseases. Risk compensation behaviours are associated with farmers’ beliefs as to who should manage animal disease. Rather than farmers’ belief in vaccine effectiveness, it is more likely that farmers’ low sense of being able to do anything to prevent disease influences their apparent risk compensation behaviours. These findings address the gap in the literature relating to farmers' behavioural adaptions to vaccine use in the management of animal disease

Molecular characterization of extended spectrum cephalosporin resistant Escherichia coli isolated from livestock and in-contact humans in Southeast Nigeria

The rise in antimicrobial resistance (AMR) in bacteria is reducing therapeutic options for livestock and human health, with a paucity of information globally. To fill this gap, a One-Health approach was taken by sampling livestock on farms (n = 52), abattoir (n = 8), and animal markets (n = 10), and in-contact humans in Southeast Nigeria. Extended spectrum cephalosporin (ESC)-resistant (ESC-R) Escherichia coli was selectively cultured from 975 healthy livestock faecal swabs, and hand swabs from in-contact humans. Antimicrobial susceptibility testing (AST) was performed on all ESC-R E. coli. For isolates showing a multi-drug resistance (MDR) phenotype (n = 196), quantitative real-time PCR (qPCR) was performed for confirmation of extended-spectrum β-lactamase (ESBL) and carbapenemase genes. Whole-genome sequencing (WGS) was performed on a subset (n = 157) for detailed molecular characterisation. The results showed ESC-R E. coli was present in 41.2% of samples, with AST results indicating 48.8% of isolates were phenotypically MDR. qPCR confirmed presence of ESBL genes, with blaCTX-M present in all but others in a subset [blaTEM (62.8%) and blaSHV (0.5%)] of isolates; none harboured transferable carbapenemase genes. Multi-locus sequence typing identified 34 Sequence Types (ST) distributed among different sampling levels; ST196 carrying blaCTX-M-55 was predominant in chickens. Large numbers of single nucleotide polymorphisms (SNPs) in the core genome of isolates, even within the same clade by phylogenetic analysis, indicated high genetic diversity. AMR genotyping indicated the predominant blaCTX-M variant was blaCTX-M-15 (87.9%), although blaCTX-M-55, blaCTX-M-64, and blaCTX-M-65 were present; it was notable that blaCTX-M-1, common in livestock, was absent. Other predominant AMR genes included: sul2, qnrS1, strB, blaTEM-1b, tetA-v2, and dfrA14, with prevalence varying according to host livestock species. A blaCTX-M-15 harbouring plasmid from livestock isolates in Ebonyi showed high sequence identity to one from river/sewage water in India, indicating this ESBL plasmid to be globally disseminated, being present beyond the river environment. In conclusion, ESC-R E. coli was widespread in livestock and in-contact humans from Southeast Nigeria. WGS data indicated the isolates were genetically highly diverse, probably representing true diversity of wild type E. coli; they were likely to be MDR with several harbouring blaCTX-M-15. Surprisingly, human isolates had highest numbers of AMR genes and pigs the least

Convalescent plasma in patients admitted to hospital with COVID-19 (RECOVERY): a randomised controlled, open-label, platform trial

SummaryBackground Azithromycin has been proposed as a treatment for COVID-19 on the basis of its immunomodulatoryactions. We aimed to evaluate the safety and efficacy of azithromycin in patients admitted to hospital with COVID-19.Methods In this randomised, controlled, open-label, adaptive platform trial (Randomised Evaluation of COVID-19Therapy [RECOVERY]), several possible treatments were compared with usual care in patients admitted to hospitalwith COVID-19 in the UK. The trial is underway at 176 hospitals in the UK. Eligible and consenting patients wererandomly allocated to either usual standard of care alone or usual standard of care plus azithromycin 500 mg once perday by mouth or intravenously for 10 days or until discharge (or allocation to one of the other RECOVERY treatmentgroups). Patients were assigned via web-based simple (unstratified) randomisation with allocation concealment andwere twice as likely to be randomly assigned to usual care than to any of the active treatment groups. Participants andlocal study staff were not masked to the allocated treatment, but all others involved in the trial were masked to theoutcome data during the trial. The primary outcome was 28-day all-cause mortality, assessed in the intention-to-treatpopulation. The trial is registered with ISRCTN, 50189673, and ClinicalTrials.gov, NCT04381936.Findings Between April 7 and Nov 27, 2020, of 16 442 patients enrolled in the RECOVERY trial, 9433 (57%) wereeligible and 7763 were included in the assessment of azithromycin. The mean age of these study participants was65·3 years (SD 15·7) and approximately a third were women (2944 [38%] of 7763). 2582 patients were randomlyallocated to receive azithromycin and 5181 patients were randomly allocated to usual care alone. Overall,561 (22%) patients allocated to azithromycin and 1162 (22%) patients allocated to usual care died within 28 days(rate ratio 0·97, 95% CI 0·87–1·07; p=0·50). No significant difference was seen in duration of hospital stay (median10 days [IQR 5 to >28] vs 11 days [5 to >28]) or the proportion of patients discharged from hospital alive within 28 days(rate ratio 1·04, 95% CI 0·98–1·10; p=0·19). Among those not on invasive mechanical ventilation at baseline, nosignificant difference was seen in the proportion meeting the composite endpoint of invasive mechanical ventilationor death (risk ratio 0·95, 95% CI 0·87–1·03; p=0·24).Interpretation In patients admitted to hospital with COVID-19, azithromycin did not improve survival or otherprespecified clinical outcomes. Azithromycin use in patients admitted to hospital with COVID-19 should be restrictedto patients in whom there is a clear antimicrobial indication

A comparison of the value of two machine learning predictive models to support bovine tuberculosis disease control in England

Nearly a decade into Defra’s current eradication strategy, bovine tuberculosis (bTB) remains a serious animal health problem in England, with c.30,000 cattle slaughtered annually in the fight against this insidious disease. There is an urgent need to improve our understanding of bTB risk in order to enhance the current disease control policy. Machine learning approaches applied to big datasets offer a potential way to do this. Regularized regression and random forest machine learning methodologies were implemented using 2016 herd-level data to generate the best possible predictive models for a bTB incident in England and its three surveillance risk areas (High-risk area [HRA], Edge area [EA] and Low-risk area [LRA]). Their predictive performance was compared and the best models in each area were used to characterize herds according to risk. While all models provided excellent discrimination, random forest models achieved the highest balanced accuracy (i.e. average of sensitivity and specificity) in England, HRA and LRA, whereas the regularized regression LASSO model did so in the EA. The time since the last confirmed incident was resolved was the only variable in the top-ten ranking in all areas according to both types of models, which highlights the importance of bTB history as a predictor of a new incident. Risk categorisation based on Receiver Operating Characteristic (ROC) analysis was carried out using the best predictive models in each area setting a 99 % threshold value for sensitivity and specificity (97 % in the LRA). Thirteen percent of herds in the whole of England as well as in its HRA, 14 % in its EA and 31 % in its LRA were classified as high-risk. These could be selected for the deployment of additional disease control measures at national or area level. In this way, low-risk herds within the area considered would not be penalised unnecessarily by blanket control measures and limited resources be used more efficiently. The methodology presented in this paper demonstrates a way to accurately identify high-risk farms to inform a targeted disease control and prevention strategy in England that supplements existing population strategies

Decision tree machine learning applied to bovine tuberculosis risk factors to aid disease control decision making

Identifying and understanding the risk factors for endemic bovine tuberculosis (TB) in cattle herds is critical for the control of this disease. Exploratory machine learning techniques can uncover complex non-linear relationships and interactions within disease causation webs, and enhance our knowledge of TB risk factors and how they are interrelated. Classification tree analysis was used to reveal associations between predictors of TB in England and each of the three surveillance risk areas (High Risk, Edge, and Low Risk) in 2016, identifying the highest risk herds. The main classifying predictor for farms in England overall related to the TB prevalence in the 100 nearest cattle herds. In the High Risk and Edge areas it was the number of slaughterhouse destinations and in the Low Risk area it was the number of cattle tested in surveillance tests. How long ago the last confirmed incident was resolved was the most frequent classifier in trees; if within two years, leading to the highest risk group of herds in the High Risk and Low Risk areas. At least two different slaughterhouse destinations led to the highest risk group of herds in England, whereas in the Edge area it was a combination of no contiguous low-risk neighbours (i.e. in a 1 km radius) and a minimum proportion of 6–23 month-old cattle in November. A threshold value of prevalence in 100 nearest neighbours increased the risk in all areas, although the value was specific to each area. Having low-risk contiguous neighbours reduced the risk in the Edge and High Risk areas, whereas high-risk ones increased the risk in England overall and in the Edge area specifically. The best classification tree models informed multivariable binomial logistic regression models in each area, adding statistical inference outputs. These two approaches showed similar predictive performance although there were some disparities regarding what constituted high-risk predictors. Decision tree machine learning approaches can identify risk factors from webs of causation: information which may then be used to inform decision making for disease control purposes