Google’s online search activity can help predict peaks in Covid-19 cases up to 17 days in advance, a new study shows.
Researchers at University College London have created computer models based on the frequency of online search queries to obtain information about the prevalence of the disease in several countries, including the United Kingdom.
Models based on online searches successfully anticipated reported cases, confirmed by Covid-19 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 searches for symptoms of odor loss and rash – two symptoms of the disease that are listed by Public Health England.
Online search data should be used with “more established approaches” to develop methods of public health surveillance for Covid and other new infectious diseases, experts say.
Google’s online search data may help inform the public health response to Covid-19, according to a report by academics at University College London. Previous research has shown that the various properties of infectious diseases can be deduced from online search 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 have shown that our approach works in different countries, regardless of cultural, socio-economic and climatic differences.”
UCL researchers used the Covid-19 symptom profile to develop patterns of its prevalence by examining symptom-related searches through Google.
They then recalibrated these models to reduce the bias of these “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 related to Covid-19 symptoms, identified by the NHS and Public Health England (PHE).
The three most common symptoms of Covid-19 are a high temperature, a new, continuous cough, and a loss or change in sense of smell or taste.
PHE also lists fewer uncommon symptoms, including pain, headache, and rash.
The terms were weighted according to their occurrence ratio in cases confirmed by Covid-19.
This model provided “useful information”, including early warnings, and presented 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 surges.
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 model emerged, in which case increases were predicted by their model.
The graph shows online search scores for Covid-19 for different countries at the end of 2019 and the beginning of 2020. Query frequencies are weighted by the probability of symptoms (blue line) and have minimized news media effects (black line). Data for physical spacing or blocking measures are indicated by dashed vertical lines
“Our best chance to address health emergencies, such as the Covid-19 pandemic, is to detect them early to act early,” said study co-author Michael Edelstein of the University. Bar-Ilan, Israel.
“Using innovative approaches to disease detection, such as internet search analysis to complement established approaches, is the best way to identify outbreaks early.”
Academics working on the models shared their results with PHE weekly to support the disease response, which are available for online viewing.
“We are pleased that public health organizations such as PHE have also recognized the usefulness of these new and non-traditional approaches to epidemiology,” said Dr. Lampos.
Internet search analysis is an established method of tracking and understanding infectious diseases and is currently used to monitor seasonal flu. The flu detector estimates flu rates in England based on web searches and is included in the Public Health England flu surveillance measures.
Internet search analysis is an established method of tracking and understanding infectious diseases.
The technique is already used monitor seasonal flu in the form of UCL Influenza detector.
The constantly updated online tool estimates flu-like rates in England based on web searches and is included in the English Public Health Influenza Surveillance Measures.
“Previous research has shown the usefulness of online search in shaping infectious diseases such as the flu,” said Dr. Lampos.
The study, “Tracking COVID-19 Using Online Search,” was published today in Nature Digital Medicine.