Nowadays, when we feel the slightest sore throat or cough, we are directly at Google. In fact, searches for symptoms of coronavirus on the Internet are currently on the ceiling. Researchers believe that this research can be used as a very important “public health tool”.
According to a report in the New York Times newspaper, a group of scientists from Harvard and the University of London Academy have found a consistent correlation between research on COVID-19 symptoms on Google and outbreaks. Experts can help the discovery better monitor health authorities, coronavirus pandemics, predict or manage the epidemic.
Scientists who have reviewed research on “odor loss”, which is believed to be a symptom of COVID-19, say research in this direction is an important early warning signal for detecting a person infected.
Researchers found link between Google searches and number of cases
Some reports show that 30 to 60% of people with COVID-19 have this symptom. In the United States, the search for “I can’t smell” in the past week has been very high in New York, New Jersey, Louisiana and Michigan, where the epidemic was common.
The crucial part of the work was that the increase in searches during this period was almost the same as the increase in the number of cases. Computer scientist Vasileios Lampos and other researchers at the University of London Academy say the most wanted symptoms are loss of smell, fever and shortness of breath.
The most common symptoms of coronavirus on Google are reported to be 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 research-based models to measure the geographic spread of diseases .
In an article published in the journal Nature in 2009, the researchers found that Google searches related to influenza are proportional to weekly cases of influenza from the United States Centers for Disease Control and Prevention (CDC). The researchers used these search terms to create a model for identifying outbreaks before official data was collected.
Although the model worked very effectively at first, H1N1 began to show inconsistent results during the flu epidemic. The reason is that many people did not try to show the symptoms of H1N1, but because they wondered or feared. In short, the anxiety searches caused by the disease were significantly higher than the number of cases. Now he is concerned that a similar misconception could be imagined in COVID-19.