People’s online searches can be used as a tool to help epidemiologists spot coronavirus outbreaks early, researchers have said.
Using symptom-related searches through Google could allow experts to predict a peak in cases on average 17 days in advance, a group from University College London (UCL) said.
Analysing internet search activity is already used to track and understand the seasonal flu.
Using data on Covid-19 web searches in a similar way alongside more established approaches could improve public health surveillance methods.
“Adding to previous research that has showcased the utility of online search activity in modelling infectious diseases such as influenza, this study provides a new set of tools that can be used to track Covid-19,” said lead author Dr Vasileios Lampos.
“We have shown that our approach works on different countries irrespective of cultural, socioeconomic and climate differences.
“Our analysis was also among the first to find an association between Covid-19 incidence and searches about the symptoms of loss of sense of smell and skin rash.
“We are delighted that public health organisations such as PHE (Public Health England) have also recognised the utility of these novel and non-traditional approaches to epidemiology.”
Scientists found their model provides useful insights, such as early warnings, and showcased the effects of physical distancing measures.
Professor Michael Edelstein, from Bar-Ilan University, Israel, who co-authored the research, said: “Our best chance of tackling health emergencies such as the Covid-19 pandemic is to detect them early in order to act early.
“Using innovative approaches to disease detection such as analysing internet search activity to complement established approaches is the best way to identify outbreaks early.”
Details of the model have been published in the Nature Digital Medicine journal.
