Greece is a country in southeast Europe with thousands of islands scattered over the Aegean and Ionian oceans. Lacked the capacity to screen all travelers, including those who were not exhibiting symptoms, as did many other countries. One possibility was to test a sample of visitors, but Greece chose to try an artificial intelligence-based approach (AI).
Between August and November 2020, the authorities established a system that uses a machine-learning algorithm to decide which travelers entering the nation should be tested for COVID-19, using input from Drakopoulos and his colleagues. The scientists discovered that machine learning was more successful than random testing or testing based on a traveler’s country of origin in identifying asymptomatic patients. According to the researchers, the system found two to four times more infected travelers during peak tourist season than random testing.
Eva is the name of the machine-learning system, which is one of the first of its kind. It’s an example of how data analysis can help COVID-19 policies work better.
In many countries, passengers are picked at random or according to risk categories for COVID-19 testing.
A person arriving from a place with a high rate of illnesses, for example, may be given priority for testing over someone arriving from a region with a lower rate.
Furthermore, it collects demographic data such as sex and age from the information that travelers are obliged to provide. The data was then compared with data from previously tested passengers, and the results were used to calculate each individual’s risk of illness. COVID-19 tests were given to tourists who were deemed to be at the greatest risk. The system also issued checks to allow it to fill in data gaps, ensuring that it remained current as the crisis progressed.
Eve is one of the best examples of using data analysis to solve modern day problems. It suggests that we will not only rely on gut feeling but rather we should rely on what the data says and form a concrete plan on it.