Analyzing search activities on the Internet has been used for quite a few years by epidemiologists to monitor annual cases of influenza. Now, researchers in England and Israel have adapted the technique to determine when COVID-19 peaks in various places around the world. Online search data can help inform the public health response to the Coronavirus pandemic, according to a report from University College London (UCL) and Bar-Ilan University (BIU) in Ramat Gan near Tel Aviv, allowing experts to predict a peak in cases some 17 days in advance.

For the paper, just published in the prestigious Nature Digital Medicine, lead author Dr.Vasileios Lampos (UCL Computer Science) and co-author Prof. Michael Edelstein of BIU’s Azrieli Faculty of Medicine used COVID-19’s symptom profile from existing epidemiological reports to develop models of its prevalence by looking at symptom-related searches by users of the omnipresent Google search engine. They then recalibrated these models to reduce public interest bias – that is, the effect media coverage has on online searches – to predict a peak in cases when applied to COVID-19.

The model was applied in several countries including the UK, USA, Italy, Australia, and South Africa, among others. The team found that the same pattern appeared, in that surges in cases were predicted by their model. provided useful insights including early warnings and showcased the effects of physical distancing measures. Academics working on the model have been sharing their findings with the UK’s National Health Service (NHS) and Public Health England (PHE) on a weekly basis to support the response to the disease.

Surveillance indicators suggest that at a national level COVID-19 case rates continued to decline in week #3 of 2021, while there was an indication that hospital and ICU admissions began to stabilize or decline slightly. There is currently limited testing for other respiratory viruses, however, laboratory indicators suggest that influenza activity has been low, they wrote in the study. Weekly data on flu vaccination was above 80% in people aged 65 years and over which was the highest uptake ever achieved. Vaccination of two- and three-year-old children was also the highest ever recorded. 

“We have shown that our approach works on different countries irrespective of cultural, socioeconomic, and climate differences,” said Lampos. “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 organizations such as PHE have also recognized the utility of these novel and non-traditional approaches to epidemiology.” 

Academics developed an uncalibrated model by choosing search terms relating to COVID-19 symptoms, identified by the NHS and PHE. The terms were weighted according to their ratio of occurrence in confirmed COVID-19 cases. This model provided useful insights including early warnings and showcased the effects of physical distancing measures. The calibrated version, which took news coverage into account, made it possible for academics to provide PHE with a model to more accurately predict surges in the UK.

Edelstein added that the best chance of tackling health emergencies such as the COVID-19 pandemic is to detect them early in order to act promptly. “Using innovative approaches to disease detection such as analyzing internet search activity to complement established approaches is the best way to identify outbreaks early.”

The team members are confident that these non-traditional data sets and methodologies will continue to be integrated in conventional epidemiological systems and always in a way that protects privacy. “We can at least use the plethora of data sets around COVID-19 for further experimentation and validation of such techniques in an attempt to complement current epidemiological approaches and be better prepared for the next pandemic,” Lampos concluded.