Potential Early Identification of a Large Campylobacter Outbreak Using Alternative Surveillance Data Sources: Autoregressive Modelling and Spatiotemporal Clustering.

Mehnaz Adnan,Tim Wood,Xiaoying Gao,Jill Sherwood,Wei Gao,Nicholas Jones,Michael Baker,Xiaohan Bai,Elizabeth Newbern

doi:10.2196/18281

Mehnaz Adnan, Tim Wood + Show 7 more

Open Access

https://doi.org/10.2196/18281

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Abstract

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.

Highlights

BackgroundIn August 2016, Havelock North, one of the 5 cities in the Hawke’s Bay region, New Zealand, was the site of a large waterborne outbreak of Campylobacter infection
Spatiotemporal clusters showed spikes in school absenteeism and consumer helpline inquiries that preceded the notified cases in the city primarily affected by the outbreak
We found multiple models suitable for prediction: school absenteeism performed best with autoregressive integrated moving average (ARIMA) (5,1,3) for forecasting 1 to 2 days ahead and ARIMA (5,0,2) for forecasting 3 to 5 days ahead, followed by Google Trends with ARIMA (2,0,0) for forecasting up to 5 days ahead

Summary

Introduction

In August 2016, Havelock North, one of the 5 cities in the Hawke’s Bay region, New Zealand, was the site of a large waterborne outbreak of Campylobacter infection. This outbreak began on August 8, but a large number of cases were not known to the national notifiable disease surveillance system until August 14. More than a third of Havelock North residents had been infected with Campylobacter This event led to serious interruption of daily life in the area and large economic costs [1,2]. It is important to consider alternative surveillance data sources and evaluate their potential for recognizing outbreaks at the earliest possible time

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