Detecting Covid-19 outbreaks weeks earlier might be as easy as checking web searches, new UK study suggests

Checking how people use web searches for Covid-19 symptoms could predict the peak of outbreaks more than two weeks before they occur, a new study published in the UK suggests.

Researchers from University College London (UCL) say online search data for terms like “loss of sense of smell” or “skin rash” provide valuable data for epidemiologists who are looking for early warning signs of an outbreak. They say the data can give a 17-day jump start to medical experts.

The team looked at search data in four English-speaking countries – the US, UK, Canada and Australia – and examined how the word ‘covid’ was used with other symptoms. They also analyzed infection data and found a 17-day lag between search terms and peaks in cases.

The same system is already being used to track the seasonal flu.

“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 in the study published in the Nature Digital Medicine journal.

The UCL team say public health officials are willing to look at the usefulness of novel “non-traditional” ways of detecting outbreaks. And the study says looking at search terms also provides an early warning on the effectiveness of “physical distancing” measures.

“Our best chance of tackling health emergencies such as the Covid-19 pandemic is to detect them early in order to act early,” said Professor Michael Edelstein of Bar-Ilan University, Israel, who co-authored the study.

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