Disease surveillance based on Internet-based linear models: an Australian case study of previously unmodeled infection diseases.

Florian Rohart,Wenbiao Hu,Kim-Anh Lê Cao,Simon M R Avril,Shilu Tong,Gabriel J Milinovich

doi:10.1038/srep38522

Abstract

Effective disease surveillance is critical to the functioning of health systems. Traditional approaches are, however, limited in their ability to deliver timely information. Internet-based surveillance systems are a promising approach that may circumvent many of the limitations of traditional health surveillance systems and provide more intelligence on cases of infection, including cases from those that do not use the healthcare system. Infectious disease surveillance systems built on Internet search metrics have been shown to produce accurate estimates of disease weeks before traditional systems and are an economically attractive approach to surveillance; they are, however, also prone to error under certain circumstances. This study sought to explore previously unmodeled diseases by investigating the link between Google Trends search metrics and Australian weekly notification data. We propose using four alternative disease modelling strategies based on linear models that studied the length of the training period used for model construction, determined the most appropriate lag for search metrics, used wavelet transformation for denoising data and enabled the identification of key search queries for each disease. Out of the twenty-four diseases assessed with Australian data, our nowcasting results highlighted promise for two diseases of international concern, Ross River virus and pneumococcal disease.

Highlights

Traditional, infectious disease surveillance systems typically rely upon data submitted to public health authorities by medical practitioners, laboratories and other health care providers[1]
Approaches based on internet search metrics hypothesize that, when people contract a disease, they will search for information on their condition/symptoms on the internet and that accurate estimates of disease occurrence in the community may be produced by monitoring changes in the frequency of specific searches
This study aimed to investigate the capacity of internet-based approaches to help monitoring a wide range of seasonal and non-seasonal infectious diseases in Australia

Summary

Introduction

Traditional, infectious disease surveillance systems typically rely upon data submitted to public health authorities by medical practitioners, laboratories and other health care providers[1]. These systems are critical to the effective functioning of health systems and form a central component in infectious disease prevention and control. Internet-based surveillance systems have been proposed as a complementary method to collecting information regarding disease in the community that may improve timeliness. We apply our models to worldwide unmodeled diseases to evaluate the utility of internet-based infectious disease surveillance to forecast one and two week incidences of Australian notification data

Objectives

Methods

Results

Conclusion