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

Rohart, Florian, Milinovich, Gabriel J., Avril, Simon M. R., Le Cao, Kim-Anh, Tong, Shilu and Hu, Wenbiao (2016) Disease surveillance based on Internet-based linear models: an Australian case study of previously unmodeled infection diseases. Scientific Reports, 6 38522. doi:10.1038/srep38522


Author Rohart, Florian
Milinovich, Gabriel J.
Avril, Simon M. R.
Le Cao, Kim-Anh
Tong, Shilu
Hu, Wenbiao
Title Disease surveillance based on Internet-based linear models: an Australian case study of previously unmodeled infection diseases
Journal name Scientific Reports   Check publisher's open access policy
ISSN 2045-2322
Publication date 2016-12-20
Year available 2016
Sub-type Article (original research)
DOI 10.1038/srep38522
Open Access Status DOI
Volume 6
Start page 38522
Total pages 11
Place of publication London, United Kingdom
Publisher Nature Publishing Group
Language eng
Subject 1000 General
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.
Keyword Multidisciplinary Sciences
Science & Technology - Other Topics
Q-Index Code C1
Q-Index Status Provisional Code
Grant ID APP1011459
DP110100651
APP1087415
DP130100777
FT140101216
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: HERDC Pre-Audit
Admin Only - School of Medicine
School of Medicine Publications
UQ Diamantina Institute Publications
 
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