A Novel Approach for Time Series Forecasting of Influenza-like Illness Using a Regression Chain Method.

Influenza is a communicable respiratory illness that can cause serious public health hazards. Due to its huge threat to the community, accurate forecasting of Influenza-like-illness (ILI) can diminish the impact of an influenza season by enabling early public health interventions. Machine learning models are increasingly being applied in infectious disease modelling, but are limited in their performance, particularly when using a longer forecasting window. This paper proposes a novel time series forecasting method, Randomized Ensembles of Auto-regression chains (Reach). Reach implements an ensemble of random chains for multistep time series forecasting. This new approach is evaluated on ILI case counts in Auckland, New Zealand from the years 2015-2018 and compared to other standard methods. The results demonstrate that the proposed method performed better than baseline methods when applied to this ILI time series forecasting problem.

COVID-19 vaccine strategies for Aotearoa New Zealand: a mathematical modelling study.

Background: COVID-19 elimination measures, including border closures have been applied in New Zealand. We have modelled the potential effect of vaccination programmes for opening borders. Methods: We used a deterministic age-stratified Susceptible, Exposed, Infectious, Recovered (SEIR) model. We minimised spread by varying the age-stratified vaccine allocation to find the minimum herd immunity requirements (the effective reproduction number Reff<1 with closed borders) under various vaccine effectiveness (VE) scenarios and R0 values. We ran two-year open-border simulations for two vaccine strategies: minimising Reff and targeting high-risk groups. Findings: Targeting of high-risk groups will result in lower hospitalisations and deaths in most scenarios. Reaching the herd immunity threshold (HIT) with a vaccine of 90% VE against disease and 80% VE against infection requires at least 86•5% total population uptake for R0=4•5 (with high vaccination coverage for 30-49-year-olds) and 98•1% uptake for R0=6. In a two-year open-border scenario with 10 overseas cases daily and 90% total population vaccine uptake (including 0-15 year olds) with the same vaccine, the strategy of targeting high-risk groups is close to achieving HIT, with an estimated 11,400 total hospitalisations (peak 324 active and 36 new daily cases in hospitals), and 1,030 total deaths. Interpretation: Targeting high-risk groups for vaccination will result in fewer hospitalisations and deaths with open borders compared to targeting reduced transmission. With a highly effective vaccine and a high total uptake, opening borders will result in increasing cases, hospitalisations, and deaths. Other public health and social measures will still be required as part of an effective pandemic response. Funding: This project was funded by the Health Research Council [20/1018]. Research in context.

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

BACKGROUND: Over 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. OBJECTIVE: The 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. 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 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. 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 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. 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 location of such surveillance data sources and methods in routine public health practice.

Electronic Sentinel Surveillance of Influenza-like Illness. Experience from a pilot study in New Zealand.

BACKGROUND: Electronic reporting of Influenza-like illness (eILI) from primary care was implemented and evaluated in three general medical practices in New Zealand during May to September 2015. OBJECTIVE: To measure the uptake of eILI and to identify the system's strength and limitations. METHODS: Analysis of transactional data from the eILI system; comparative study of influenza-like illness cases reported using manual methods and eILI; questionnaire administered to clinical and operational stakeholders. RESULTS: Over the study period 66% of total ILI cases were reported using eILI. Reporting timeliness improved significantly compared to manual reporting with an average of 24 minutes from submission by the clinician to processing in the national database. Users found the system to be user-friendly. CONCLUSION: eILI assists clinicians to report ILI cases to public health authorities within a stipulated time period and is associated with faster, more reliable and improved information transfer.

Development of an Electronic Notification System for Influenza-Like Illness Sentinel Surveillance.

An electronic Influenza like Illness surveillance system developed to support general practices to electronically notify the cases of influenza like illness (ILI) for national sentinel surveillance in New Zealand. Content analysis was performed to capture the information necessary for ILI surveillance. An online form was implemented within the patient management system to record the details of ILI cases. A middleware framework was developed to manage the information flow between GPs and national influenza surveillance coordinators. The framework used an HL7 version 2.4 messaging standard to receive the notification data and Rhapsody integration engines to parse the message and store the information in national ILI data base. This paper presents the system design and implementation details of electronic ILI notification system. It presents data model designed to capture information for ILI case along with the HL7 messages structure implemented in the system.

HL7 Middleware Framework for Laboratory Notifications for Notifiable Diseases.

LabSurv is an electronic notification system developed to support laboratories to directly notify the results of notifiable disease testing to public health services in New Zealand. A direct laboratory notification middleware framework was developed to manage the information flow between laboratories and public health services. The framework uses an HL7 messaging standard to receive the laboratory results and windows services to integrate the results with the cases of notifiable diseases within a national electronic surveillance system. This paper presents the system design and implementation details of direct laboratory notification system in LabSurv. It presents the HL7 messages structure implemented in the system. Finally, the performance of the system based on implemented framework is analysed and presented to evaluate the efficiency of our design.

Iterative refinement of SemLink to enhance patient readability of discharge summaries.

A Discharge Summary contains vocabulary that is difficult to understand for health consumers. We used iterative refinements in developing a system, SemLink, which dynamically generate synonyms and hyperlinks to appropriate Internet resources for difficult terms in discharge summary text to make the text more comprehensible to consumers. This paper describes our iterative refinement protocol to enhance the semantic annotation and dynamic hyperlinking algorithms to link topic-specific web pages for difficult terms found occurring in Discharge Summary text.