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February 10th, 2021 
Awake Goy An Israel-based epidemiologist has been helping English health officials to detect COVID-19 outbreaks some 17 days before they happen, by monitoring popular Google searches.
The influential journal Nature Digital Medicine has revealed England’s success in getting advanced warnings on outbreaks using an algorithm that has been taught what terms people search for when they start to feel COVID-19 symptoms.
The tech has also been tested, and proved effective, in Italy, Australia and South Africa, irrespective of cultural, socioeconomic and climate differences. It has not yet been tested or deployed in Israel. The data analysis doesn’t compromise privacy, as no personal search information is delivered to researchers, only mass anonymized data.
Writing in a peer-reviewed article published on Monday, the pioneers of the tech say they have shown the power of the internet search as a “complementary health surveillance method” in fighting the pandemic, as it can allow health officials to better detect and address outbreaks.
A large electronic billboard displays a message urging people to wear a face mask as a precuation against the transmission of the novel coronavirus, in Manchester, northwest England, on July 31, 2020 (Oli SCARFF / AFP)
British-Israeli epidemiologist Prof. Michael Edelstein helped to build the algorithm in London alongside a team from University College London, and has been playing a key role in maintaining it since he moved to Israel in the summer to take up an academic post at Bar Ilan University’s medicine faculty.
“Our best chance of tackling health emergencies such as the COVID-19 pandemic is to detect them early in order to act early,” Edelstein said, adding that the algorithm’s success shows that “using innovative approaches to disease detection such as analyzing internet search activity to complement established approaches is the best way to identify outbreaks early.”
Epidemiologist Michael Edelstein (courtesy of Michael Edelstein)
Asked how the algorithm works, Edelstein, who was a senior official at Public Health England when he started the project, told The Times of Israel: “You take clinical reports from cases of COVID and see what symptoms people report. You look at what symptoms they are concerned about, and see how exactly they describe the symptoms when searching. You can then use those search terms to see when COVID is likely to rise.
“We then found ways to remove what we call the ‘noise,’ like searches that are related to news coverage rather than people feeling unwell. We applied the algorithm in countries that are ahead of others in outbreak progression and compared them to those that are behind, which gave us more information. What we got in the end is a very good predictor of places where COVID cases are set to rise in about two weeks’ time.”
The United Kingdom has seen nearly four million confirmed coronavirus cases and over 113,000 deaths.
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