QUT : Early warning - Internet surveillance predicts disease outbreak

Friday, 14 March, 2014

blaze- featuredIn a new study published in Lancet Infectious Diseases, internet-based surveillance has been found to detect infectious diseases such Dengue Fever and Influenza up to two weeks earlier than traditional surveillance methods.
Senior author of the review article entitled Internet-based surveillance systems for monitoring emerging infectious diseases, Queensland University of Technology (QUT) Senior Research Fellow Dr Wenbiao Hu said when investigating the occurrence of epidemics, spikes in searches for information about infectious diseases could accurately predict outbreaks of that disease.
Dr Hu said there was often a lag time of two weeks before traditional surveillance methods could detect an emerging infectious disease.
“This is because traditional surveillance relies on the patient recognising the symptoms and seeking treatment before diagnosis, along with the time taken for health professionals to alert authorities through their health networks,” Dr Hu said.
Dr Hu said the review found that surveillance systems that use internet-search metrics provide faster notification of outbreaks. Internet-based surveillance systems are able to provide estimates of Influenza in the community up to two weeks ahead of traditional surveillance.
“Early detection means early warning and that can help reduce or contain an epidemic, as well as alert public health authorities to ensure risk management strategies such as the provision of adequate medication are implemented.”
Dr Hu said the study suggested social media and micoblogs including Twitter and Facebook could also be effective in detecting disease outbreaks.
“There is the potential for digital technology to revolutionise emerging infectious disease surveillance,” he said.
“The next step would be to combine the approaches currently available such as social media, aggregator websites and search engines, along with other factors such as climate and temperature, and develop a real-time infectious disease predictor.”
Dr Hu is part of QUT’s School of Public Health and Social Work and a member of the Institute of Health and Biomedical Innovation (IHBI).
The other authors of the paper were Gabriel Milinovich (first author), Gail Williams and Archie Clements from the University of Queensland School of Population, Health and State.
qut-logoFor more information www.qut.edu.au/public-health

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