Investigation of US Cyclospora cayetanensis outbreaks in 2019 and evaluation of an improved Cyclospora genotyping system against 2019 cyclosporiasis outbreak clusters.
Barratt, J
Houghton, K
Richins, T
Straily, A
Threlkel, R
Bera, B
Kenneally, J
Clemons, B
Madison-Antenucci, S
Cebelinski, E
Whitney, BM
Kreil, KR
Cama, V
Arrowood, MJ
Qvarnstrom, Y
- Publisher:
- Cambridge University Press
- Publication Type:
- Journal Article
- Citation:
- Epidemiology and Infection, 2021, 149, pp. 1-14
- Issue Date:
- 2021-09-13
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Full metadata record
Field | Value | Language |
---|---|---|
dc.contributor.author |
Barratt, J https://orcid.org/0000-0001-8711-2408 |
|
dc.contributor.author | Houghton, K | |
dc.contributor.author | Richins, T | |
dc.contributor.author | Straily, A | |
dc.contributor.author | Threlkel, R | |
dc.contributor.author | Bera, B | |
dc.contributor.author | Kenneally, J | |
dc.contributor.author | Clemons, B | |
dc.contributor.author | Madison-Antenucci, S | |
dc.contributor.author | Cebelinski, E | |
dc.contributor.author | Whitney, BM | |
dc.contributor.author | Kreil, KR | |
dc.contributor.author | Cama, V | |
dc.contributor.author | Arrowood, MJ | |
dc.contributor.author | Qvarnstrom, Y | |
dc.date.accessioned | 2022-01-14T01:10:32Z | |
dc.date.available | 2022-01-14T01:10:32Z | |
dc.date.issued | 2021-09-13 | |
dc.identifier.citation | Epidemiology and Infection, 2021, 149, pp. 1-14 | |
dc.identifier.issn | 0950-2688 | |
dc.identifier.issn | 1469-4409 | |
dc.identifier.uri | http://hdl.handle.net/10453/153093 | |
dc.description.abstract | Cyclosporiasis is an illness characterised by watery diarrhoea caused by the food-borne parasite Cyclospora cayetanensis. The increase in annual US cyclosporiasis cases led public health agencies to develop genotyping tools that aid outbreak investigations. A team at the Centers for Disease Control and Prevention (CDC) developed a system based on deep amplicon sequencing and machine learning, for detecting genetically-related clusters of cyclosporiasis to aid epidemiologic investigations. An evaluation of this system during 2018 supported its robustness, indicating that it possessed sufficient utility to warrant further evaluation. However, the earliest version of CDC's system had some limitations from a bioinformatics standpoint. Namely, reliance on proprietary software, the inability to detect novel haplotypes and absence of a strategy to select an appropriate number of discrete genetic clusters would limit the system's future deployment potential. We recently introduced several improvements that address these limitations and the aim of this study was to reassess the system's performance to ensure that the changes introduced had no observable negative impacts. Comparison of epidemiologically-defined cyclosporiasis clusters from 2019 to analogous genetic clusters detected using CDC's improved system reaffirmed its excellent sensitivity (90%) and specificity (99%), and confirmed its high discriminatory power. This C. cayetanensis genotyping system is robust and with ongoing improvement will form the basis of a US-wide C. cayetanensis genotyping network for clinical specimens. | |
dc.format | Electronic | |
dc.language | eng | |
dc.publisher | Cambridge University Press | |
dc.relation.ispartof | Epidemiology and Infection | |
dc.relation.isbasedon | 10.1017/S0950268821002090 | |
dc.rights | © The Author(s), 2021. Published by Cambridge University Press. This is an Open Access article, distributed under the terms of the Creative Commons Attribution- NonCommercial-ShareAlike licence (http:// creativecommons.org/licenses/by-nc-sa/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the same Creative Commons licence is used to distribute the re-used or adapted article and the original article is properly cited. The written permission of Cambridge University Press must be obtained prior to any commercial use | |
dc.rights | info:eu-repo/semantics/restrictedAccess | |
dc.subject | 1117 Public Health and Health Services | |
dc.subject.classification | Epidemiology | |
dc.subject.mesh | Clinical Laboratory Techniques | |
dc.subject.mesh | Cluster Analysis | |
dc.subject.mesh | Cyclospora | |
dc.subject.mesh | Cyclosporiasis | |
dc.subject.mesh | Disease Outbreaks | |
dc.subject.mesh | DNA, Protozoan | |
dc.subject.mesh | Feces | |
dc.subject.mesh | Genotype | |
dc.subject.mesh | Genotyping Techniques | |
dc.subject.mesh | Humans | |
dc.subject.mesh | Molecular Epidemiology | |
dc.subject.mesh | United States | |
dc.subject.mesh | Clinical Laboratory Techniques | |
dc.subject.mesh | Cluster Analysis | |
dc.subject.mesh | Cyclospora | |
dc.subject.mesh | Cyclosporiasis | |
dc.subject.mesh | DNA, Protozoan | |
dc.subject.mesh | Disease Outbreaks | |
dc.subject.mesh | Feces | |
dc.subject.mesh | Genotype | |
dc.subject.mesh | Genotyping Techniques | |
dc.subject.mesh | Humans | |
dc.subject.mesh | Molecular Epidemiology | |
dc.subject.mesh | United States | |
dc.title | Investigation of US Cyclospora cayetanensis outbreaks in 2019 and evaluation of an improved Cyclospora genotyping system against 2019 cyclosporiasis outbreak clusters. | |
dc.type | Journal Article | |
utslib.citation.volume | 149 | |
utslib.location.activity | England | |
utslib.for | 1117 Public Health and Health Services | |
pubs.organisational-group | /University of Technology Sydney | |
pubs.organisational-group | /University of Technology Sydney/Faculty of Science | |
pubs.organisational-group | /University of Technology Sydney/Faculty of Science/School of Life Sciences | |
utslib.copyright.status | recently_added | * |
pubs.consider-herdc | false | |
dc.date.updated | 2022-01-14T01:10:30Z | |
pubs.publication-status | Published | |
pubs.volume | 149 |
Abstract:
Cyclosporiasis is an illness characterised by watery diarrhoea caused by the food-borne parasite Cyclospora cayetanensis. The increase in annual US cyclosporiasis cases led public health agencies to develop genotyping tools that aid outbreak investigations. A team at the Centers for Disease Control and Prevention (CDC) developed a system based on deep amplicon sequencing and machine learning, for detecting genetically-related clusters of cyclosporiasis to aid epidemiologic investigations. An evaluation of this system during 2018 supported its robustness, indicating that it possessed sufficient utility to warrant further evaluation. However, the earliest version of CDC's system had some limitations from a bioinformatics standpoint. Namely, reliance on proprietary software, the inability to detect novel haplotypes and absence of a strategy to select an appropriate number of discrete genetic clusters would limit the system's future deployment potential. We recently introduced several improvements that address these limitations and the aim of this study was to reassess the system's performance to ensure that the changes introduced had no observable negative impacts. Comparison of epidemiologically-defined cyclosporiasis clusters from 2019 to analogous genetic clusters detected using CDC's improved system reaffirmed its excellent sensitivity (90%) and specificity (99%), and confirmed its high discriminatory power. This C. cayetanensis genotyping system is robust and with ongoing improvement will form the basis of a US-wide C. cayetanensis genotyping network for clinical specimens.
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