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Whole genome sequencing data used for surveillance of Campylobacter infections: detection of a large continuous outbreak, Denmark, 2019.

ABSTRACT: BackgroundCampylobacter is one of the most frequent causes of bacterial gastroenteritis. Campylobacter outbreaks are rarely reported, which could be a reflection of a surveillance without routine molecular typing. We have previously shown that numerous small outbreak-like clusters can be detected when whole genome sequencing (WGS) data of clinical Campylobacter isolates was applied.AimTyping-based surveillance of Campylobacter infections was initiated in 2019 to enable detection of large clusters of clinical isolates and to match them to concurrent retail chicken isolates in order to react on ongoing outbreaks.MethodsWe performed WGS continuously on isolates from cases (n = 701) and chicken meat (n = 164) throughout 2019. Core genome multilocus sequence typing was used to detect clusters of clinical isolates and match them to isolates from chicken meat.ResultsSeventy-two clusters were detected, 58 small clusters (2-4 cases) and 14 large clusters (5-91 cases). One third of the clinical isolates matched isolates from chicken meat. One large cluster persisted throughout the whole year and represented 12% of all studied Campylobacter cases. This cluster type was detected in several chicken samples and was traced back to one slaughterhouse, where interventions were implemented to control the outbreak.ConclusionOur WGS-based surveillance has contributed to an improved understanding of the dynamics of the occurrence of Campylobacter strains in chicken meat and the correlation to clusters of human cases.

SUBMITTER: Joensen KG

PROVIDER: S-EPMC8176674 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundOver one-third of the population of Havelock North, New Zealand, approximately 5500 people, were estimated to have been affected by campylobacteriosis in a large waterborne outbreak. Cases reported through the notifiable disease surveillance system (notified case reports) are inevitably delayed by several days, resulting in slowed outbreak recognition and delayed control measures. Early outbreak detection and magnitude prediction are critical to outbreak control. It is therefore important to consider alternative surveillance data sources and evaluate their potential for recognizing outbreaks at the earliest possible time.ObjectiveThe first objective of this study is to compare and validate the selection of alternative data sources (general practice consultations, consumer helpline, Google Trends, Twitter microblogs, and school absenteeism) for their temporal predictive strength for Campylobacter cases during the Havelock North outbreak. The second objective is to examine spatiotemporal clustering of data from alternative sources to assess the size and geographic extent of the outbreak and to support efforts to attribute its source.MethodsWe combined measures derived from alternative data sources during the 2016 Havelock North campylobacteriosis outbreak with notified case report counts to predict suspected daily Campylobacter case counts up to 5 days before cases reported in the disease surveillance system. Spatiotemporal clustering of the data was analyzed using Local Moran's I statistics to investigate the extent of the outbreak in both space and time within the affected area.ResultsModels that combined consumer helpline data with autoregressive notified case counts had the best out-of-sample predictive accuracy for 1 and 2 days ahead of notified case reports. Models using Google Trends and Twitter typically performed the best 3 and 4 days before case notifications. Spatiotemporal clusters showed spikes in school absenteeism and consumer helpline inquiries that preceded the notified cases in the city primarily affected by the outbreak.ConclusionsAlternative data sources can provide earlier indications of a large gastroenteritis outbreak compared with conventional case notifications. Spatiotemporal analysis can assist in refining the geographical focus of an outbreak and can potentially support public health source attribution efforts. Further work is required to assess the location of such surveillance data sources and methods in routine public health practice.

Dataset Information

Whole genome sequencing data used for surveillance of Campylobacter infections: detection of a large continuous outbreak, Denmark, 2019.

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