Thursday April 18, 2019

Googling About Symptoms Can Predict Disease

The study was found by using digital surveillance through search engine algorithms such as Google Trends and Google Insights

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Google users
Google unveils new shopping search features for Indian users. Pixabay

Irritated at recurring symptoms? Searching for an online diagnosis on google is not a bad idea before visiting the doctor.

The habit of searching on internet for an online diagnosis before visiting a doctor can provide early warning of an infectious disease epidemic, says a study.

In the study published in the journal 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.

“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,” said Wenbiao Hu, senior research fellow at Queensland University of Technology (QUT) in Australia.

Representational image.
Representational image. IANS

There was often a lag time of two weeks before traditional surveillance methods could detect an emerging infectious disease. “In contrast, digital surveillance can provide real-time detection of epidemics,” Hu added.

The study found by using digital surveillance through search engine algorithms such as Google Trends and Google Insights, detecting the 2005-06 avian influenza outbreak ‘Bird Flu’ would have been possible between one and two weeks earlier than official surveillance reports.

“In another example, a digital data collection network was found to be able to detect the SARS outbreak more than two months before the first publications by the World Health Organisation (WHO),” he said.

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Early detection means early warning and that can help reduce or contain an epidemic, as well alert public health authorities to ensure risk management strategies such as the provision of adequate medication are implemented, the study noted.

Hu said social media tools 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 added.  (IANS)

Next Story

Researchers Develop Novel Device to Identify Early Symptoms of Sepsis

cNEWS will now be introduced carefully into hospitals with appropriate information technology infrastructure

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Vaccine
Influenza leads to serious and potentially life-threatening complications such as pneumonia, sepsis and heart disease, the study noted. VOA

Researchers have developed a new computer-aided model to identify early symptoms of sepsis — a potentially life-threatening condition caused by the body’s response to an infection.

Sepsis is a major cause of death in hospitals, and early detection is key to preventing deaths. Every hour of delay is linked to a 7 per cent reduction of survival.

The study showed that the computer-aided National Early Warning Score (cNEWS) determined if it could enhance the accuracy of predicting sepsis. The score can trigger screening for sepsis within 30 minutes.

scientists, genome
FILE – Researcher Ben Matthews speaks in a room housing mosquitoes in the Vosshall Laboratory at Rockefeller University in New York, Feb. 12, 2019. VOA

“The main advantage of these computer models is that they are designed to incorporate data that exist in the patient record, can be easily automated and place no extra burden on the hospital staff to collect additional information,” said Mohammed A. Mohammed, Professor at the University of Bradford in Britain.

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“These risk scores should support, rather than replace, clinical judgment. We hope they will heighten awareness of sepsis with additional information on this serious condition,” he added, in the paper published in Canadian Medical Association Journal.

cNEWS will now be introduced carefully into hospitals with appropriate information technology infrastructure, the study noted. (IANS)