Coronavirus Searches on Google Can Facilitate Control of the Outbreak

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These days, when we experience the slightest sore throat or cough, we are directly at Google. As a matter of fact, the searches for coronavirus symptoms on the internet are currently on the ceiling. Researchers think these searches can be used as a very important ‘public health tool’.

According to a report published in the New York Times newspaper, a group of scientists from Harvard and the University of London Academy discovered a consistent correlation between searches on COVID-19 symptoms on Google and outbreak points. Experts can help this finding better monitor health authorities, coronavirus pandemics, predict or manage the outbreak.

Scientists who have examined the ‘odor loss’ searches, which are said to be one of the symptoms of COVID-19, say that searches in this direction are an important early warning signal for the detection of someone infected.

Researchers found a link between Google searches and the number of cases
Some reports show that 30 to 60 percent of people with COVID-19 disease experience this symptom. In the United States, searches for “I can’t smell” over the past week were very high in New York, New Jersey, Louisiana, and Michigan, where the outbreak was common.

The crucial part of the job was that the search increase in this period was almost the same as the increase in the number of cases. Computer scientist Vasileios Lampos and other researchers from the University of London Academy say that the most sought-after symptoms are odor loss, fever and shortness of breath.

It is stated that the most searched coronavirus symptoms on Google are odor loss, fever and shortness of breath.
Google data can be used to measure various situations related to COVID-19, but past experience shows that extreme caution should be exercised when creating models based on search data to measure the geographic spread of diseases.

In an article published in the journal Nature in 2009, researchers discovered that flu-related Google searches are proportional to weekly flu cases from the US Centers for Disease Control and Prevention (CDC). Researchers used these search terms to create a model to help identify outbreaks before official data was collected.

Although the model worked very efficiently at the beginning, the H1N1 began to give inconsistent results during the flu epidemic. The reason was that many people searched not for showing H1N1 symptoms, but because they were wondering or feared. In short, anxious searches caused by the disease were significantly higher than the number of cases. Now it is worried that a similar misconception can be imagined in COVID-19.

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