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Online research activity pulled from Google can help predict peaks in Covid-19 cases up to 17 days in advance, a new study reveals.
Researchers at University College London have created computer models based on the frequency of online search queries to obtain information on the prevalence of the disease in several countries, including the United Kingdom.
Models based on online research successfully anticipated reported and confirmed Covid-19 cases and deaths by 16.7 and 22.1 days, respectively.
The team’s analysis was among the first to find an association between the incidence of Covid-19 and research into symptoms of loss of smell and rash – two symptoms of the disease listed by Public Health England.
Online research data should be used along with “more established approaches” to develop public health surveillance methods for Covid and other new infectious diseases, experts say.
Online research data pulled from Google may help shed light on the public health response to Covid-19, according to a report by academics at University College London, previous research has shown that various properties of infectious diseases can be inferred online search behavior
“This study provides a new set of tools that can be used to track Covid-19,” said lead author of the study, Dr Vasileios Lampos of University College London.
“We have shown that our approach works in different countries regardless of cultural, socio-economic and climatic differences.”
UCL researchers used the symptom profile of Covid-19 to develop models of its prevalence by looking at symptom-related research via Google.
They then recalibrated these models to reduce bias in those “signals” that were caused by the public interest – in other words, the effect of media coverage on online searches.
They developed the uncalibrated model by choosing search terms relating to symptoms of Covid-19, identified by the NHS and Public Health England (PHE).
The three most common symptoms of Covid-19 are a high temperature, a 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 rash.
The terms have been weighted according to their rate of occurrence in confirmed cases of Covid-19.
This model provided “useful information,” including early warnings, and showcased the effects of physical distancing measures, according to UCL.
The calibrated version, which took into account media coverage, allowed academics to provide PHE with a model to more accurately predict surges in the UK.
The model has been applied in several countries, including the United Kingdom, the United States, Italy, Australia and South Africa, among others.
They found that the same pattern appeared, in that case relapses were predicted by their model.
The graph shows online search scores for Covid-19 for different countries at the end of 2019 and early 2020. Query frequencies are weighted by the likelihood of symptoms occurring (blue line) and media effects information are minimized (black line). The dates of physical distancing or lockdown measures are indicated by vertical dotted lines
“Our best chance of dealing with health emergencies such as the Covid-19 pandemic is to detect them early in order to act early,” said study co-author Professor Michael Edelstein of Bar University. -Ilan, in Israel.
“Using innovative disease detection approaches such as analysis of internet research activities to complement established approaches is the best way to identify outbreaks early.
Academics working on the models shared their findings with SPE on a weekly basis to support the disease response, which can be viewed online.
“We are delighted that public health organizations such as PHE have also recognized the usefulness of these new and non-traditional approaches to epidemiology,” said Dr Lampos.
Analysis of Internet search activity is an established method of tracking and understanding infectious diseases, and is currently used to monitor seasonal influenza. Flu detector estimates rates of influenza-like illness in England based on web research and is included in Public Health England’s influenza surveillance measures
Analysis of Internet search activity is an established method of tracking and understanding infectious diseases.
The technique is already used for watch for seasonal flu in the form of UCL Flu detector.
The constantly updated online tool estimates rates of influenza-like illness in England based on web research and is included in Public Health England’s influenza surveillance measures.
“Previous research has shown the usefulness of online research activity to model infectious diseases such as influenza,” said Dr Lampos.
The study, “Tracking COVID-19 using online search,” was published today in Nature Digital Medicine.
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