Researchers from CSIRO’s Data61, the data science arm of Australia’s national science agency, and UNSW Sydney’s Kirby Institute, have developed a new tool that harnesses artificial intelligence and Twitter for the earlier detection of acute disease events, such as thunderstorm asthma.
Thunderstorm asthma is the triggering of an asthma attack by environmental conditions directly caused by a local thunderstorm. The sudden outbreak in Melbourne on 21 November 2016 inundated emergency services and hospitals resulting in over 8000 hospital admissions by 6pm that day.
Dr Aditya Joshi, Postdoctoral Fellow at CSIRO’s Data61, said a key challenge in the case of acute disease events is to detect them as soon as possible to assist health agencies to respond swiftly in emergency situations.
“The popularity of social media makes it a valuable source of information for epidemic intelligence,” Dr Joshi said.
“We developed a technique that was able to detect the disease outbreak up to nine hours before it was officially reported and before the first news story broke.
“We can draw upon informal sources such as social media data to understand how acute disease events occur, and we can detect when and where an outbreak is likely to occur. This means hospitals and public health agencies can be as prepared as possible.”
Using anonymised and publicly available Twitter data, the tool analysed more than 3 million tweets containing keywords related to asthma such as “breath” and “coughing”.
The technique combines two fields of artificial intelligence — natural language processing and statistical time series modelling — and a four-step process to ensure the tweets containing the keywords were indeed reports of health conditions and to remove duplicates where an individual might tweet more than once about their condition.
Natural language processing, or NLP, is the ability of a computer program to process human language. The tool uses NLP based on word embeddings, to distinguish between symptoms and unrelated mentions of the keywords.
Professor Raina MacIntyre, Head of Biosecurity Research Program, Kirby Institute, UNSW Sydney said that this work is a remarkable contribution to public health research.
“In future, this system can be used to provide health authorities and the community early warning of a serious and sudden health event,” Professor MacIntyre said.
“Early detection could significantly improve our capability to mitigate the impact of epidemics.”
The tool can be used to detect other outbreaks such as Influenza, Ebola and the Zika virus. It draws on Data61’s Emergency Situation Awareness system, which analyses Twitter messages posted during disasters and crises to support disaster response efforts.