Disease surveillance in England and Wales, December 2016

doi:10.1136/vr.j120

Cited by 5 publications

(4 citation statements)

References 3 publications

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“…In the analysed animal isolate dataset, 163 of the 399 isolates were S. Typhimurium (and in two cases monophasic S. Typhimurium) of DT104, of which 60 were clonal isolates related to the known 2015–2018 DT104 outbreak in England and Wales [ Animal and Plant Health Agency (APHA), 2017 ]. Even though S. Typhimurium of DT104 can reside in numerous host species, it is considered primarily a cattle pathogen ( Poppe et al, 1998 ).…”

Section: Discussionmentioning

confidence: 99%

“…Especially in the case of foodborne outbreaks, which are inherently clonal, a RF model could be applied to rapidly detect or narrow down the potential outbreak sources provided that these were represented in the model training set ( Vilne et al, 2019 ). It is understood that the England and Wales 2015–2018 S. Typhimurium DT104 outbreak most likely originated from cattle, but the spread was probably due to the movement of sheep, and most human cases that were not linked to farms were likely due to mutton consumption [ Animal and Plant Health Agency (APHA), 2017 ]. Thus, the RF1 model, for which 20 of the 48 RF1 training set DT104 outbreak related isolates were from Cattle, behaved as expected when assigning most of the DT104 outbreak related human isolates with a high confidence probability of assignment of over 0.500 to Cattle.…”

Section: Discussionmentioning

confidence: 99%

“…The fastq files of the 852 human isolates were downloaded from the NCBI GenBank Sequence Read Archive (BioProject PRJNA248792) using fasterq-dump of SRA Toolkit v2.9.6 1 . Both the animal and human isolate datasets included samples that were linked with the 2015–2018 S. Typhimurium DT104 outbreak in England and Wales [ Animal and Plant Health Agency (APHA), 2017 ].…”

Section: Methodsmentioning

confidence: 99%

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Development and validation of a random forest algorithm for source attribution of animal and human Salmonella Typhimurium and monophasic variants of S. Typhimurium isolates in England and Wales utilising whole genome sequencing data

Guzinski,

Tang,

Chattaway

et al. 2024

Front. Microbiol.

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Source attribution has traditionally involved combining epidemiological data with different pathogen characterisation methods, including 7-gene multi locus sequence typing (MLST) or serotyping, however, these approaches have limited resolution. In contrast, whole genome sequencing data provide an overview of the whole genome that can be used by attribution algorithms. Here, we applied a random forest (RF) algorithm to predict the primary sources of human clinical Salmonella Typhimurium (S. Typhimurium) and monophasic variants (monophasic S. Typhimurium) isolates. To this end, we utilised single nucleotide polymorphism diversity in the core genome MLST alleles obtained from 1,061 laboratory-confirmed human and animal S. Typhimurium and monophasic S. Typhimurium isolates as inputs into a RF model. The algorithm was used for supervised learning to classify 399 animal S. Typhimurium and monophasic S. Typhimurium isolates into one of eight distinct primary source classes comprising common livestock and pet animal species: cattle, pigs, sheep, other mammals (pets: mostly dogs and horses), broilers, layers, turkeys, and game birds (pheasants, quail, and pigeons). When applied to the training set animal isolates, model accuracy was 0.929 and kappa 0.905, whereas for the test set animal isolates, for which the primary source class information was withheld from the model, the accuracy was 0.779 and kappa 0.700. Subsequently, the model was applied to assign 662 human clinical cases to the eight primary source classes. In the dataset, 60/399 (15.0%) of the animal and 141/662 (21.3%) of the human isolates were associated with a known outbreak of S. Typhimurium definitive type (DT) 104. All but two of the 141 DT104 outbreak linked human isolates were correctly attributed by the model to the primary source classes identified as the origin of the DT104 outbreak. A model that was run without the clonal DT104 animal isolates produced largely congruent outputs (training set accuracy 0.989 and kappa 0.985; test set accuracy 0.781 and kappa 0.663). Overall, our results show that RF offers considerable promise as a suitable methodology for epidemiological tracking and source attribution for foodborne pathogens.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Development and validation of a random forest algorithm for source attribution of animal and human Salmonella Typhimurium and monophasic variants of S. Typhimurium isolates in England and Wales utilising whole genome sequencing data

Guzinski,

Tang,

Chattaway

et al. 2024

Front. Microbiol.

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“…However low levels of virus circulation in 2014–16 (presumably due to the high numbers of susceptible hosts which seroconverted in the initial outbreak) meant that animals born in that time frame (a substantial portion of the 2016 UK sheep flock) were naive to the virus and vulnerable to infection [17]. Recently, SBV was identified in a large number of animals in the UK, Ireland and Belgium in late summer/autumn of 2016, confirmed by both seroconversion and the identification of SBV RNA positive Culicoides sp with the subsequent appearance in the 2016–17 lambing season of large numbers of deformed fetuses [18]. It is of particular importance to note that this time period (August to September) coincides with the breeding season of sheep (August to December) in the majority of European production systems.…”

Section: Introductionmentioning

confidence: 99%

Schmallenberg virus neutralising antibody responses in sheep

et al. 2019

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BackgroundSchmallenberg virus (SBV) is a midge borne virus of cattle and sheep. Infection is typically asymptomatic in adult sheep but fetal infection during pregnancy can result in abortion, stillbirth, neurological disorders and malformations of variable severity in newborn animals. It was first identified in Germany and the Netherlands in 2011 and then circulated throughout Europe in 2012 and 2013. Circulation in subsequent years was low or non-existent until summer and autumn 2016, leading to an increased incidence of deformed newborn lambs and calves in 2016–17. This study reports SBV circulation in October 2016 within a group of 24 ewes and 13 rams. The ewes were monitored at 3 times points over an 11 week period (September to December 2016).ResultsMost ewes displayed an increase in SBV VNT with antibody titre increases greater in older, previously exposed ewes. Two ewes had SBV RNA detectable by RT-qPCR, one on 30/09/16 and one on 04/11/16. Of these ewes, one had detectable serum SBV RNA (indicating viraemia) despite pre-existing antibody. The rams had been previously vaccinated with a commercial inactivated SBV vaccine, they showed minimal neutralising antibody titres against SBV 8 months post-vaccination and all displayed increased titre in October 2016.ConclusionThis data suggests that SBV circulated for a minimum period of 5 weeks in September to October 2016 in central England. Ewes previously exposed to virus showed an enhanced antibody response compared to naïve animals. Pre-existing antibody titre did not prevent re-infection in at least one animal, implying immunity to SBV upon natural exposure may not be life-long. In addition, data suggests that immunity provided by killed adjuvanted SBV vaccines only provides short term protection (< 8 months) from virus.

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Using SNP addresses for Salmonella Typhimurium DT104 in routine veterinary outbreak detection

Bettridge,

Snow,

Tang

et al. 2023

Epidemiol. Infect.

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SNP addresses are a pathogen typing method based on whole-genome sequences (WGSs), assigning groups at seven different levels of genetic similarity. Public health surveillance uses it for several gastro-intestinal infections; this work trialled its use in veterinary surveillance for salmonella outbreak detection. Comparisons were made between temporal and spatio-temporal cluster detection models that either defined cases by their SNP address or by phage type, using historical data sets. Clusters of SNP incidents were effectively detected by both methods, but spatio-temporal models consistently detected these clusters earlier than the corresponding temporal models. Unlike phage type, SNP addresses appeared spatially and temporally limited, which facilitated the differentiation of novel, stable, or expanding clusters in spatio-temporal models. Furthermore, these models flagged spatio-temporal clusters containing only two to three cases at first detection, compared with a median of seven cases in phage-type models. The large number of SNP addresses will require automated methods to implement these detection models routinely. Further work is required to explore how temporal changes and different host species may impact the sensitivity and specificity of cluster detection. In conclusion, given validation with more sequencing data, SNP addresses are likely to be a valuable addition to early warning systems in veterinary surveillance.

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Disease surveillance in England and Wales, December 2016

Cited by 5 publications

References 3 publications

Development and validation of a random forest algorithm for source attribution of animal and human Salmonella Typhimurium and monophasic variants of S. Typhimurium isolates in England and Wales utilising whole genome sequencing data

Development and validation of a random forest algorithm for source attribution of animal and human Salmonella Typhimurium and monophasic variants of S. Typhimurium isolates in England and Wales utilising whole genome sequencing data

Schmallenberg virus neutralising antibody responses in sheep

Using SNP addresses for Salmonella Typhimurium DT104 in routine veterinary outbreak detection

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