JMIR Publications

ABSTRACT

Background:

Over a third of the population of Havelock North, New Zealand, approximately 5,500 people, were estimated to have been affected with 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 is 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.

Objective:

The first objective of this study is to compare and validate a 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 support efforts to attribute its source.

Methods:

We 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 five days prior to cases reported in the disease surveillance system. Spatiotemporal clustering of the data was analysed using Local Moran’s I statistics to investigate the extent of the outbreak in both space and time within the wider area affected.

Results:

Models 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 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.

Conclusions:

Alternative 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 place of such surveillance data sources and methods in routine public health practice.