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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.

Neisseria meningitidis, a gram-negative bacterium, is the causative agent for meningococcal meningitis and septicemia and has been associated with isolated cases, outbreaks, and epidemics worldwide (1). The rapid progression of [...]

To the Editor: Zika virus infection is an emerging arboviral disease linked to an increased risk for severe neurologic outcomes and devastating adverse fetal complications, including pregnancy loss and congenital [...]

Genomic surveillance is an essential part of effective disease control, enabling identification of emerging and expanding strains and monitoring of subsequent interventions. Whole-genome sequencing was used to analyze the genomic diversity of all Neisseria meningitidis isolates submitted to the New Zealand Meningococcal Reference Laboratory during 2013-2018. Of the 347 isolates submitted for whole-genome sequencing, we identified 68 sequence types belonging to 18 clonal complexes (CC). The predominant CC was CC41/44; next in predominance was CC11. Comparison of the 45 New Zealand group W CC11 isolates with worldwide representatives of group W CC11 isolates revealed that the original UK strain, the 2013 UK strain, and a distinctive variant (the 2015 strain) were causing invasive group W meningococcal disease in New Zealand. The 2015 strain also demonstrated increased resistance to penicillin and has been circulating in Canada and several countries in Europe, highlighting that close monitoring is needed to prevent future outbreaks around the world. [ABSTRACT FROM AUTHOR]

In 2014, a sustained outbreak of yersiniosis due to Yersinia pseudotuberculosis occurred across all major cities in New Zealand (NZ), with a total of 220 laboratory-confirmed cases, representing one of the largest ever reported outbreaks of Y. pseudotuberculosis. Here, we performed whole genome sequencing of outbreak-associated isolates to produce the largest population analysis to date of Y. pseudotuberculosis, giving us unprecedented capacity to understand the emergence and evolution of the outbreak clone. Multivariate analysis incorporating our genomic and clinical epidemiological data strongly suggested a single point-source contamination of the food chain, with subsequent nationwide distribution of contaminated produce. We additionally uncovered significant diversity in key determinants of virulence, which we speculate may help explain the high morbidity linked to this outbreak. [ABSTRACT FROM AUTHOR]