The online search activity obtained from Google may help predict peaks in Covid-19 cases up to 17 days in advance, a new study reveals.
University College London researchers created computer models based on online research consultation frequencies to obtain information on the prevalence of the disease in several countries, including the United Kingdom.
Models based on online surveys successfully anticipated reported cases and confirmed Covid-19 cases and deaths in 16.7 and 22.1 days, respectively.
The team’s analysis was one of the first to find an association between the incidence of Covid-19 and research on the symptoms of loss of smell and rash – two symptoms of the disease listed by Public Health England.
Online research data should be used with “more established approaches” to develop methods of public health surveillance for Covid and other new infectious diseases, experts say.
Online research data taken from Google can help inform the public health response to Covid-19, according to an academic report from University College London. Previous research has shown that various properties of infectious diseases can be inferred from online research behavior
“This study provides a new set of tools that can be used to track Covid-19,” said the study’s lead author, Dr. Vasileios Lampos of University College London.
“We show that our approach works in different countries, regardless of cultural, socioeconomic and climatic differences.”
UCL researchers used the Covid-19 symptom profile to develop models of its prevalence, looking at symptom-related searches on Google.
They then recalibrated these models to reduce the bias in these ‘signs’ caused by the public interest – in other words, the effect that media coverage has on online surveys.
They developed the uncalibrated model by choosing search terms related to Covid-19 symptoms, identified by the NHS and Public Health England (PHE).
The three most common symptoms of Covid-19 are high temperature, new and continuous cough, and loss or change in smell or taste.
PHE also lists several less common symptoms, including aches and pains, headaches and a rash.
The terms were weighted according to their reason for occurrence in confirmed Covid-19 cases.
This model provided ‘useful insights’, including early warnings, and showed the effects of physical distance measures, according to UCL.
The calibrated version, which took into account news coverage, allowed academics to provide PHE with a model to more accurately predict UK spikes.
The model has been applied in several countries, including the United Kingdom, USA, Italy, Australia and South Africa, among others.
They found that the same pattern appeared, in which case peaks were predicted by their model.
The graph shows online search scores for Covid-19 for different countries in late 2019 and early 2020. Consultation frequencies are weighted by the likelihood of symptoms occurring (blue line) and have the effects of the news media minimized (line black). Dates for physical distance or blocking measures are indicated with dashed and dotted vertical lines
“Our best chance of dealing with health emergencies, such as the Covid-19 pandemic, is to detect them early to act soon,” said study co-author Professor Michael Edelstein of Bar-Ilan University in Israel.
‘Using innovative approaches to disease detection, such as analyzing Internet research activity to complement established approaches, is the best way to identify outbreaks early.
Academics working on the models have shared their findings with PHE weekly to support the response to the disease, which are available for viewing online.
“We are delighted that public health organizations like PHE have also recognized the usefulness of these new, non-traditional approaches to epidemiology,” said Dr. Lampos.
Analyzing Internet research activity is an established method of tracking and understanding infectious diseases and is currently used to monitor seasonal flu. The flu detector estimates rates of influenza-like illnesses in England based on web research and is included in Public Health England’s flu surveillance metrics
Analysis of Internet research activity is an established method of tracking and understanding infectious diseases.
The technique is already being used to monitor seasonal flu in the form of UCL’s Flu Detector.
The constantly updated online tool estimates the rates of influenza-like illnesses in England based on web research and is included in Public Health England’s flu surveillance metrics.
“Previous research has demonstrated the usefulness of online research activity in modeling infectious diseases like influenza,” said Dr. Lampos.
The study, ‘Tracking COVID-19 using online search’, was published today in Nature Digital Medicine.