Journal article
Disease surveillance based on Internet-based linear models: an Australian case study of previously unmodeled infection diseases
Florian Rohart, Gabriel J Milinovich, Simon MR Avril, Kim-Anh Le Cao, Shilu Tong, Wenbiao Hu
SCIENTIFIC REPORTS | NATURE RESEARCH | Published : 2016
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 er..
View full abstractGrants
Awarded by National Health and Medical Research Council (NHMRC) Project
Awarded by Australian Research Council (ARC) Discovery Grant
Awarded by NHMRC Career Development fellowship
Awarded by ARC Discovery Grant
Awarded by ARC future fellowship
Funding Acknowledgements
We would like to express our gratitude to the Office of Health Protection, Department of Health for providing the disease notification data on behalf of the Communicable Disease Network Australia. This study was partly funded by the National Health and Medical Research Council (NHMRC) Project Grants (APP1011459, APP1002608) and Australian Research Council (ARC) Discovery Grant (DP110100651). KALC is supported in part by the Australian Cancer Research Foundation (ACRF) for the Diamantina Individualised oncology Care Centre at The University of Queensland Diamantina Institute and the NHMRC Career Development fellowship (APP1087415). FR is supported by the ARC Discovery Grant (DP130100777). WH is supported by ARC future fellowship (FT140101216).